upd readme

README.md

@@ -73,8 +73,10 @@ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/f
 
 相关文件和介绍在 [multiagent](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/tree/main/multiagent) 文件夹下。
 
-## 前瞻（开发中项目）
-
 ### Logic
 
-相关文件和介绍在 [logic](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/tree/main/logic) 文件夹下。
+相关文件和介绍在 [logic](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/tree/main/logic) 文件夹下。
+
+## 前瞻（开发中项目）
+
+### Tracking

logic/README.md

@@ -1,5 +1,3 @@
-~~coming soon~~
-
 #### 介绍
 
 在这个项目中，你将编写简单的 Python 函数，生成描述 Pacman 物理状态（记为 **pacphysics**）的逻辑句子。然后，你将使用 SAT 求解器 pycosat，解决与 **规划**（生成动作序列以到达目标位置并吃掉所有点）、**定位**（根据本地传感器模型在地图中找到自己）、**建图**（从零开始构建地图）以及 **SLAM**（同时定位与建图）相关的逻辑推理任务。
@@ -308,8 +306,8 @@ Pacman 从已知地图开始，但起始位置未知。它有一个 4-bit 的传
 
 - **添加到知识库**：墙所在的位置（`walls_list`）和不在的位置（not in `walls_list`）。
 - **对于在 `range(agent.num_timesteps)` 中的 $t$**：
-  - 添加 pacphysics、动作和感知信息到知识库。
-  - 使用更新的知识库查找可能的 Pacman 位置。
+  - [添加 pacphysics、动作和感知信息到知识库](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/blob/main/logic/README.md#%E5%B0%86-pacphysics%E5%8A%A8%E4%BD%9C%E5%92%8C%E6%84%9F%E7%9F%A5%E4%BF%A1%E6%81%AF%E6%B7%BB%E5%8A%A0%E5%88%B0-kb)。
+  - [使用更新的知识库查找可能的 Pacman 位置](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/blob/main/logic/README.md#%E4%BD%BF%E7%94%A8%E6%9B%B4%E6%96%B0%E7%9A%84-kb-%E6%9F%A5%E6%89%BE%E5%8F%AF%E8%83%BD%E7%9A%84-pacman-%E4%BD%8D%E7%BD%AE)。
   - 在时间 $t$ 上调用 `agent.moveToNextState(action_t)`。
   - 生成（`yield`）可能的位置。
 
@@ -336,8 +334,8 @@ Pacman 现在知道了他的起始位置，但不知道墙的位置（除了外
 
 1. 获取 Pacman 的初始位置 `(pac_x_0, pac_y_0)`，并将其添加到知识库中。同时添加该位置是否有墙。
 2. 对于 `t` 在 `range(agent.num_timesteps)` 中：
-   - 添加 pacphysics、动作和感知信息到知识库。
-   - 使用更新的知识库查找可证明的墙位置。
+   - [添加 pacphysics、动作和感知信息到知识库](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/blob/main/logic/README.md#%E5%B0%86-pacphysics%E5%8A%A8%E4%BD%9C%E5%92%8C%E6%84%9F%E7%9F%A5%E4%BF%A1%E6%81%AF%E6%B7%BB%E5%8A%A0%E5%88%B0-kb)。
+   - [使用更新的知识库查找可证明的墙位置](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/blob/main/logic/README.md#%E4%BD%BF%E7%94%A8%E6%9B%B4%E6%96%B0%E7%9A%84-kb-%E6%9F%A5%E6%89%BE%E5%8F%AF%E8%AF%81%E6%98%8E%E7%9A%84%E5%A2%99%E4%BD%8D%E7%BD%AE)。
    - 在时间 $t$ 上调用 `agent.moveToNextState(action_t)`。
    - 生成 `known_map`。
 
@@ -361,9 +359,9 @@ python autograder.py -q q7
 
 1. 获取 Pacman 的初始位置 `(pac_x_0, pac_y_0)`，并将其添加到知识库（KB）中。相应更新 `known_map` 并将适当的表达式添加到知识库。
 2. 对于 $t$ 在 `range(agent.num_timesteps)` 中：
-   - 添加 pacphysics、动作和感知信息到知识库。使用 `SLAMSensorAxioms`、`SLAMSuccessorAxioms` 和 `numAdjWallsPerceptRules`。
-   - 使用更新的知识库查找可证明的墙位置。
-   - 使用更新的知识库查找可能的 Pacman 位置。
+   - [添加 pacphysics、动作和感知信息到知识库](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/blob/main/logic/README.md#%E5%B0%86-pacphysics%E5%8A%A8%E4%BD%9C%E5%92%8C%E6%84%9F%E7%9F%A5%E4%BF%A1%E6%81%AF%E6%B7%BB%E5%8A%A0%E5%88%B0-kb)。使用 `SLAMSensorAxioms`、`SLAMSuccessorAxioms` 和 `numAdjWallsPerceptRules`。
+   - [使用更新的知识库查找可证明的墙位置](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/blob/main/logic/README.md#%E4%BD%BF%E7%94%A8%E6%9B%B4%E6%96%B0%E7%9A%84-kb-%E6%9F%A5%E6%89%BE%E5%8F%AF%E8%AF%81%E6%98%8E%E7%9A%84%E5%A2%99%E4%BD%8D%E7%BD%AE)。
+   - [使用更新的知识库查找可能的 Pacman 位置](https://github.com/ACMClassCourse-2023/PPCA-AIPacMan-2024/blob/main/logic/README.md#%E4%BD%BF%E7%94%A8%E6%9B%B4%E6%96%B0%E7%9A%84-kb-%E6%9F%A5%E6%89%BE%E5%8F%AF%E8%83%BD%E7%9A%84-pacman-%E4%BD%8D%E7%BD%AE)。
    - 在时间 $t$ 上调用 `agent.moveToNextState(action_t)`。
    - 生成 `known_map` 和 `possible_locations`。
 

tracking/VERSION

@@ -0,0 +1 @@
+v3.000

tracking/autograder.py

@@ -0,0 +1,428 @@
+# autograder.py
+# -------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+# imports from python standard library
+import grading
+import importlib.util
+import optparse
+import os
+import re
+import sys
+import projectParams
+import random
+random.seed(0)
+try: 
+    from pacman import GameState
+except:
+    pass
+
+# register arguments and set default values
+def readCommand(argv):
+    parser = optparse.OptionParser(description = 'Run public tests on student code')
+    parser.set_defaults(generateSolutions=False, edxOutput=False, gsOutput=False, muteOutput=False, printTestCase=False, noGraphics=False)
+    # BEGIN SOLUTION NO PROMPT
+    parser.set_defaults(generatePublicTests=False)
+    # END SOLUTION NO PROMPT
+    parser.add_option('--test-directory',
+                      dest = 'testRoot',
+                      default = 'test_cases',
+                      help = 'Root test directory which contains subdirectories corresponding to each question')
+    parser.add_option('--student-code',
+                      dest = 'studentCode',
+                      default = projectParams.STUDENT_CODE_DEFAULT,
+                      help = 'comma separated list of student code files')
+    parser.add_option('--code-directory',
+                    dest = 'codeRoot',
+                    default = "",
+                    help = 'Root directory containing the student and testClass code')
+    parser.add_option('--test-case-code',
+                      dest = 'testCaseCode',
+                      default = projectParams.PROJECT_TEST_CLASSES,
+                      help = 'class containing testClass classes for this project')
+    parser.add_option('--generate-solutions',
+                      dest = 'generateSolutions',
+                      action = 'store_true',
+                      help = 'Write solutions generated to .solution file')
+    parser.add_option('--edx-output',
+                    dest = 'edxOutput',
+                    action = 'store_true',
+                    help = 'Generate edX output files')
+    parser.add_option('--gradescope-output',
+                    dest = 'gsOutput',
+                    action = 'store_true',
+                    help = 'Generate GradeScope output files')
+    parser.add_option('--mute',
+                    dest = 'muteOutput',
+                    action = 'store_true',
+                    help = 'Mute output from executing tests')
+    parser.add_option('--print-tests', '-p',
+                    dest = 'printTestCase',
+                    action = 'store_true',
+                    help = 'Print each test case before running them.')
+    parser.add_option('--test', '-t',
+                      dest = 'runTest',
+                      default = None,
+                      help = 'Run one particular test.  Relative to test root.')
+    parser.add_option('--question', '-q',
+                    dest = 'gradeQuestion',
+                    default = None,
+                    help = 'Grade one particular question.')
+    parser.add_option('--no-graphics',
+                    dest = 'noGraphics',
+                    action = 'store_true',
+                    help = 'No graphics display for pacman games.')
+    # BEGIN SOLUTION NO PROMPT
+    parser.add_option('--generate-public-tests',
+                    dest = 'generatePublicTests',
+                    action = 'store_true',
+                    help = 'Generate ./test_cases/* from ./private_test_cases/*')
+    # END SOLUTION NO PROMPT
+    (options, args) = parser.parse_args(argv)
+    return options
+
+
+# confirm we should author solution files
+def confirmGenerate():
+    print('WARNING: this action will overwrite any solution files.')
+    print('Are you sure you want to proceed? (yes/no)')
+    while True:
+        ans = sys.stdin.readline().strip()
+        if ans == 'yes':
+            break
+        elif ans == 'no':
+            sys.exit(0)
+        else:
+            print('please answer either "yes" or "no"')
+
+
+# TODO: Fix this so that it tracebacks work correctly
+# Looking at source of the traceback module, presuming it works
+# the same as the intepreters, it uses co_filename.  This is,
+# however, a readonly attribute.
+def setModuleName(module, filename):
+    functionType = type(confirmGenerate)
+    classType = type(optparse.Option)
+
+    for i in dir(module):
+        o = getattr(module, i)
+        if hasattr(o, '__file__'): continue
+
+        if type(o) == functionType:
+            setattr(o, '__file__', filename)
+        elif type(o) == classType:
+            setattr(o, '__file__', filename)
+            # TODO: assign member __file__'s?
+        #print i, type(o)
+
+
+#from cStringIO import StringIO
+
+def loadModuleString(moduleSource):
+    # Below broken, imp doesn't believe its being passed a file:
+    #    ValueError: load_module arg#2 should be a file or None
+    #
+    #f = StringIO(moduleCodeDict[k])
+    #tmp = imp.load_module(k, f, k, (".py", "r", imp.PY_SOURCE))
+    tmp = imp.new_module(k)
+    exec(moduleCodeDict[k],tmp.__dict__)
+    setModuleName(tmp, k)
+    return tmp
+
+import py_compile
+
+def loadModuleFile(moduleName, filePath):
+    # https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly
+    spec = importlib.util.spec_from_file_location(moduleName, filePath)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def readFile(path, root=""):
+    "Read file from disk at specified path and return as string"
+    with open(os.path.join(root, path), 'r') as handle:
+        return handle.read()
+
+
+#######################################################################
+# Error Hint Map
+#######################################################################
+
+# TODO: use these
+ERROR_HINT_MAP = {
+  'q1': {
+    "<type 'exceptions.IndexError'>": """
+      We noticed that your project threw an IndexError on q1.
+      While many things may cause this, it may have been from
+      assuming a certain number of successors from a state space
+      or assuming a certain number of actions available from a given
+      state. Try making your code more general (no hardcoded indices)
+      and submit again!
+    """
+  },
+  'q3': {
+      "<type 'exceptions.AttributeError'>": """
+        We noticed that your project threw an AttributeError on q3.
+        While many things may cause this, it may have been from assuming
+        a certain size or structure to the state space. For example, if you have
+        a line of code assuming that the state is (x, y) and we run your code
+        on a state space with (x, y, z), this error could be thrown. Try
+        making your code more general and submit again!
+
+    """
+  }
+}
+
+import pprint
+
+def splitStrings(d):
+    d2 = dict(d)
+    for k in d:
+        if k[0:2] == "__":
+            del d2[k]
+            continue
+        if d2[k].find("\n") >= 0:
+            d2[k] = d2[k].split("\n")
+    return d2
+
+
+def printTest(testDict, solutionDict):
+    pp = pprint.PrettyPrinter(indent=4)
+    print("Test case:")
+    for line in testDict["__raw_lines__"]:
+        print("   |", line)
+    print("Solution:")
+    for line in solutionDict["__raw_lines__"]:
+        print("   |", line)
+
+
+def runTest(testName, moduleDict, printTestCase=False, display=None):
+    import testParser
+    import testClasses
+    for module in moduleDict:
+        setattr(sys.modules[__name__], module, moduleDict[module])
+
+    testDict = testParser.TestParser(testName + ".test").parse()
+    solutionDict = testParser.TestParser(testName + ".solution").parse()
+    test_out_file = os.path.join('%s.test_output' % testName)
+    testDict['test_out_file'] = test_out_file
+    testClass = getattr(projectTestClasses, testDict['class'])
+
+    questionClass = getattr(testClasses, 'Question')
+    question = questionClass({'max_points': 0}, display)
+    testCase = testClass(question, testDict)
+
+    if printTestCase:
+        printTest(testDict, solutionDict)
+
+    # This is a fragile hack to create a stub grades object
+    grades = grading.Grades(projectParams.PROJECT_NAME, [(None,0)])
+    testCase.execute(grades, moduleDict, solutionDict)
+
+
+# returns all the tests you need to run in order to run question
+def getDepends(testParser, testRoot, question):
+    allDeps = [question]
+    questionDict = testParser.TestParser(os.path.join(testRoot, question, 'CONFIG')).parse()
+    if 'depends' in questionDict:
+        depends = questionDict['depends'].split()
+        for d in depends:
+            # run dependencies first
+            allDeps = getDepends(testParser, testRoot, d) + allDeps
+    return allDeps
+
+# get list of questions to grade
+def getTestSubdirs(testParser, testRoot, questionToGrade):
+    problemDict = testParser.TestParser(os.path.join(testRoot, 'CONFIG')).parse()
+    if questionToGrade != None:
+        questions = getDepends(testParser, testRoot, questionToGrade)
+        if len(questions) > 1:
+            print('Note: due to dependencies, the following tests will be run: %s' % ' '.join(questions))
+        return questions
+    if 'order' in problemDict:
+        return problemDict['order'].split()
+    return sorted(os.listdir(testRoot))
+
+
+# evaluate student code
+def evaluate(generateSolutions, testRoot, moduleDict, exceptionMap=ERROR_HINT_MAP,
+             edxOutput=False, muteOutput=False, gsOutput=False,
+            printTestCase=False, questionToGrade=None, display=None):
+    # imports of testbench code.  note that the testClasses import must follow
+    # the import of student code due to dependencies
+    import testParser
+    import testClasses
+    for module in moduleDict:
+        setattr(sys.modules[__name__], module, moduleDict[module])
+
+    questions = []
+    questionDicts = {}
+    test_subdirs = getTestSubdirs(testParser, testRoot, questionToGrade)
+    for q in test_subdirs:
+        subdir_path = os.path.join(testRoot, q)
+        if not os.path.isdir(subdir_path) or q[0] == '.':
+            continue
+
+        # create a question object
+        questionDict = testParser.TestParser(os.path.join(subdir_path, 'CONFIG')).parse()
+        questionClass = getattr(testClasses, questionDict['class'])
+        question = questionClass(questionDict, display)
+        questionDicts[q] = questionDict
+
+        # load test cases into question
+        tests = list(filter(lambda t: re.match(r'[^#~.].*\.test\Z', t), os.listdir(subdir_path)))
+        tests = list(map(lambda t: re.match(r'(.*)\.test\Z', t).group(1), tests))
+        for t in sorted(tests):
+            test_file = os.path.join(subdir_path, '%s.test' % t)
+            solution_file = os.path.join(subdir_path, '%s.solution' % t)
+            test_out_file = os.path.join(subdir_path, '%s.test_output' % t)
+            testDict = testParser.TestParser(test_file).parse()
+            if testDict.get("disabled", "false").lower() == "true":
+                continue
+            testDict['test_out_file'] = test_out_file
+            testClass = getattr(projectTestClasses, testDict['class'])
+            testCase = testClass(question, testDict)
+            def makefun(testCase, solution_file):
+                if generateSolutions:
+                    # write solution file to disk
+                    return lambda grades: testCase.writeSolution(moduleDict, solution_file)
+                else:
+                    # read in solution dictionary and pass as an argument
+                    testDict = testParser.TestParser(test_file).parse()
+                    solutionDict = testParser.TestParser(solution_file).parse()
+                    if printTestCase:
+                        return lambda grades: printTest(testDict, solutionDict) or testCase.execute(grades, moduleDict, solutionDict)
+                    else:
+                        return lambda grades: testCase.execute(grades, moduleDict, solutionDict)
+            question.addTestCase(testCase, makefun(testCase, solution_file))
+
+        # Note extra function is necessary for scoping reasons
+        def makefun(question):
+            return lambda grades: question.execute(grades)
+        setattr(sys.modules[__name__], q, makefun(question))
+        questions.append((q, question.getMaxPoints()))
+
+    grades = grading.Grades(projectParams.PROJECT_NAME, questions,
+                            gsOutput=gsOutput, edxOutput=edxOutput, muteOutput=muteOutput)
+    if questionToGrade == None:
+        for q in questionDicts:
+            for prereq in questionDicts[q].get('depends', '').split():
+                grades.addPrereq(q, prereq)
+
+    grades.grade(sys.modules[__name__], bonusPic = projectParams.BONUS_PIC)
+    return grades.points
+
+
+
+def getDisplay(graphicsByDefault, options=None):
+    graphics = graphicsByDefault
+    if options is not None and options.noGraphics:
+        graphics = False
+    if graphics:
+        try:
+            import graphicsDisplay
+            return graphicsDisplay.PacmanGraphics(1, frameTime=.05)
+        except ImportError:
+            pass
+    import textDisplay
+    return textDisplay.NullGraphics()
+
+# BEGIN SOLUTION NO PROMPT
+import shutil
+
+def copy(srcDir, destDir, filename):
+    srcFilename = os.path.join(srcDir, filename)
+    destFilename = os.path.join(destDir, filename)
+    print("Copying %s -> %s" % (srcFilename, destFilename))
+    shutil.copy(srcFilename, destFilename)
+    # with open(os.path.join(srcDir, filename), 'r') as f1:
+    #     with open(os.path.join(destDir, filename), 'w') as f2:
+    #         f2.write(f1.read())
+
+def generatePublicTests(moduleDict, privateRoot='private_test_cases', publicRoot='test_cases'):
+    import testParser
+    import testClasses
+    for module in moduleDict:
+        setattr(sys.modules[__name__], module, moduleDict[module])
+
+    if not os.path.exists(publicRoot): os.mkdir(publicRoot)
+    copy(privateRoot, publicRoot, 'CONFIG')
+    for q in sorted(os.listdir(privateRoot)):
+        private_subdir_path = os.path.join(privateRoot, q)
+        public_subdir_path = os.path.join(publicRoot, q)
+        if not os.path.exists(public_subdir_path): os.mkdir(public_subdir_path)
+
+        if not os.path.isdir(private_subdir_path) or q[0] == '.':
+            continue
+
+        copy(private_subdir_path, public_subdir_path, 'CONFIG')
+
+        # create a question object
+        questionDict = testParser.TestParser(os.path.join(public_subdir_path, 'CONFIG')).parse()
+        questionClass = getattr(testClasses, questionDict['class'])
+        question = questionClass(questionDict, getDisplay(False))
+
+        tests = list(filter(lambda t: re.match(r'[^#~.].*\.test\Z', t), os.listdir(private_subdir_path)))
+        tests = list(map(lambda t: re.match(r'(.*)\.test\Z', t).group(1), tests))
+        for t in sorted(tests):
+            test_file = os.path.join(private_subdir_path, '%s.test' % t)
+            public_test_file = os.path.join(public_subdir_path, '%s.test' % t)
+            test_out_file = os.path.join(public_subdir_path, '%s.test_output' % t)
+            print("Creating public test case %s from %s" % (public_test_file, test_file))
+
+            testDict = testParser.TestParser(test_file).parse()
+            if testDict.get("disabled", "false").lower() == "true":
+                continue
+            testDict['test_out_file'] = test_out_file
+            testClass = getattr(projectTestClasses, testDict['class'])
+            testCase = testClass(question, testDict)
+
+            testCase.createPublicVersion()
+            testCase.emitPublicVersion(public_test_file)
+# END SOLUTION NO PROMPT
+
+
+
+if __name__ == '__main__':
+    options = readCommand(sys.argv)
+    if options.generateSolutions:
+        confirmGenerate()
+    codePaths = options.studentCode.split(',')
+    # moduleCodeDict = {}
+    # for cp in codePaths:
+    #     moduleName = re.match(r'.*?([^/]*)\.py', cp).group(1)
+    #     moduleCodeDict[moduleName] = readFile(cp, root=options.codeRoot)
+    # moduleCodeDict['projectTestClasses'] = readFile(options.testCaseCode, root=options.codeRoot)
+    # moduleDict = loadModuleDict(moduleCodeDict)
+
+    moduleDict = {}
+    for cp in codePaths:
+        moduleName = re.match(r'.*?([^/]*)\.py', cp).group(1)
+        moduleDict[moduleName] = loadModuleFile(moduleName, os.path.join(options.codeRoot, cp))
+    moduleName = re.match(r'.*?([^/]*)\.py', options.testCaseCode).group(1)
+    moduleDict['projectTestClasses'] = loadModuleFile(moduleName, os.path.join(options.codeRoot, options.testCaseCode))
+
+    # BEGIN SOLUTION NO PROMPT
+    if options.generatePublicTests:
+        generatePublicTests(moduleDict)
+        sys.exit()
+    # END SOLUTION NO PROMPT
+
+    if options.runTest != None:
+        runTest(options.runTest, moduleDict, printTestCase=options.printTestCase, display=getDisplay(True, options))
+    else:
+        evaluate(options.generateSolutions, options.testRoot, moduleDict,
+            gsOutput=options.gsOutput,
+            edxOutput=options.edxOutput, muteOutput=options.muteOutput, printTestCase=options.printTestCase,
+            questionToGrade=options.gradeQuestion, display=getDisplay(options.gradeQuestion!=None, options))

tracking/bayesNet.py

@@ -0,0 +1,886 @@
+# bayesNet.py
+# -----------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+import itertools
+from collections import defaultdict
+import random
+from copy import deepcopy, copy
+
+class BayesNet(object):
+
+    def __init__(self, variables, inputInEdges, inputOutEdges, inputVariableDomains):
+        """
+        Bare bones constructor for bayes nets.
+        Use constructEmptyBayesNet for a nicer interface.
+
+        variables:       An iterable of all of the variables.
+        inEdges:         A dict that maps variable to otherVariable
+                         when there is an edge from otherVariable to
+                         variable 
+        outEdges:        A dict that maps variable to otherVariable
+                         where there is an edge from variable to
+                         otherVariable  
+        variableDomains: A dict mapping each variable to its domain (list like).
+
+        Constructs a bayes net with edges given by inEdges and
+        outEdges for each variable.
+        Doesn't initialize the conditional probability table for any variables.
+        """
+        # Each variable is unique (so that they can be keys in dicts)
+        self.__variablesSet = set(variables)
+        self.__variables = sorted(list(variables))
+        # self.__inEdges[v] = [u if the edge (u, v) exists]
+        self.__inEdges  = inputInEdges 
+        # self.__outEdges[u] = [v if the edge (u, v) exists]
+        self.__outEdges = inputOutEdges
+
+        # make sure that the edge maps contain all variables
+        for variable in self.__variablesSet:
+            if variable not in self.__inEdges:
+                self.__inEdges[variable]  = set()
+            if variable not in self.__outEdges:
+                self.__outEdges[variable] = set()
+
+        self.__variableDomainsDict = inputVariableDomains
+        self.__CPTDict = {}
+
+    def variablesSet(self):
+        " Returns a copy of the set of variables in the bayes net "
+        return copy(self.__variablesSet)
+
+    def variableDomainsDict(self):
+        " Returns a copy of the variable domains in the bayes net "
+        return deepcopy(self.__variableDomainsDict)
+
+    def inEdges(self):
+        " Returns a copy of the incoming edges in the bayes net "
+        return deepcopy(self.__inEdges)
+
+    def outEdges(self):
+        " Returns a copy of the outgoing edges in the bayes net "
+        return deepcopy(self.__outEdges)
+
+    def __str__(self):
+        """
+        Human-readable representation of a bayes net. 
+        Prints each variable, each edge, and then each conditional probability table.
+        """
+        netString = "Variables: " + ", ".join([str(var) for var in self.__variablesSet]) + "\n" + \
+                    "Edges: " + ", ".join([str(fromVar) + " -> " + str(toVar) \
+                    for toVar in self.__variablesSet \
+                    for fromVar in self.__inEdges[toVar]])
+        try:
+            factorsString = "Conditional Probability Tables:\n\n" + \
+                            "\n ======================= \n\n".join([str(factor) for factor in self.getAllCPTsWithEvidence()])
+            return netString + '\n\n' + factorsString
+        except KeyError:
+            return netString + '\n' + repr(self.variableDomainsDict())
+
+    def sameGraph(self, other):
+        sameVars = self.__variables == other.__variables
+        sameInEdges = sorted(self.__inEdges) == sorted(other.__inEdges)
+        sameOutEdges = sorted(self.__outEdges) == sorted(other.__outEdges)
+        return sameVars and sameInEdges and sameOutEdges
+    
+    def sameValuesDict(self, other):
+        if sorted(self.__variableDomainsDict.keys()) != sorted(other.__variableDomainsDict.keys()):
+            return False
+        for variable, assignments in self.__variableDomainsDict.items():
+            if sorted(other.__variableDomainsDict[variable]) != sorted(assignments):
+                return False
+        return True
+
+    def linearizeVariables(self):
+        """
+        Returns a list of the variables in the bayes net, but in a
+        linearized order (parents of a variable in the graph always
+        precede it in the order).
+
+        Useful for sampling.
+        """
+        inEdgesIncremental = dict([(var, edgeSet.copy()) for (var, edgeSet) in self.__inEdges.items()])
+        noIncomingList = [var for var in self.__variables if len(self.__inEdges[var]) == 0]
+
+        linearizedList = []
+
+        while len(noIncomingList) > 0:
+            currentVar = noIncomingList.pop()
+            linearizedList.append(currentVar)
+            for outGoingVariable in self.__outEdges[currentVar]:
+                inEdgesIncremental[outGoingVariable].remove(currentVar)
+                if len(inEdgesIncremental[outGoingVariable]) == 0:
+                    noIncomingList.append(outGoingVariable)
+
+        hasEdgesLeftOver = any([len(inEdgesIncremental[var]) > 0 for var in self.__variables])
+        if hasEdgesLeftOver:
+            raise ValueError("Graph has at least one cycle (not a bayes net) " + \
+                               str(inEdgesIncremental))
+        else:
+            return linearizedList
+
+    def getCPT(self, variable):
+        """
+        Returns a copy of the conditional probability table in the bayes net
+        for variable.  This is instantiated as a factor.
+        """
+        if variable not in self.__variablesSet:
+            raise ValueError("Variable not in bayes net: " + str(variable))
+        else:
+            return deepcopy(self.__CPTDict[variable])
+
+    def setCPT(self, variable, CPT):
+        """
+        Sets the conditional probability table in the bayes net for
+        variable.
+
+        CPT is a Factor of the conditional probability table with variable 
+        as the only unconditioned variable, and each conditioned variable
+        must have an edge going into variable.
+        """
+        if variable not in self.__variablesSet:
+            raise ValueError("Variable not in bayes net: " + str(variable))
+        else:
+            unconditionedVariables = CPT.unconditionedVariables()
+            conditionedVariables = CPT.conditionedVariables()
+
+            if len(unconditionedVariables) != 1:
+                raise ValueError("Unconditioned variables must contain a single element for an entry" + \
+                                   " in the conditional probability tables for this Bayes net\n" + \
+                                  str(unconditionedVariables))
+
+
+            unconditionedVariable = list(unconditionedVariables)[0]
+            
+            if unconditionedVariable != variable:
+                raise ValueError("Variable in the input and the " 
+                                  + "unconditionedVariable in the factor must \nagree. " +
+                                  "Input variable: " + str(variable) + \
+                                  " unconditioned variable: " + str(unconditionedVariable))
+
+            for var in conditionedVariables:
+                if var not in self.__inEdges[unconditionedVariable]:
+                    raise ValueError("Conditioned variables must be all have an edge " +
+                                       "going into \n the unconditionedVariable. \n" +
+                                       "conditionedVariables: " + str(conditionedVariables) + \
+                                       "\nparent: " + str(var))
+
+            self.__CPTDict[variable] = deepcopy(CPT)
+
+    def getReducedVariableDomains(self, evidenceDict):
+        """
+        evidenceDict: A dict with an assignment for each
+                                evidence variable.
+
+        Returns a new variableDomainsDict where each evidence
+        variable's domain is the single value that it is being
+        assigned to (and is otherwise unchanged).
+        """
+        reducedVariableDomainsDict = self.variableDomainsDict()
+        for (evidenceVariable, value) in evidenceDict.items():
+            reducedVariableDomainsDict[evidenceVariable] = [value]
+        return reducedVariableDomainsDict
+
+    def getCPTWithEvidence(self, variable, evidenceDict=None):
+        """
+        Gets a conditional probability table for a variable, where the
+        assignments in evidenceDict have been performed, so
+        the CPT may have less rows than what
+        would be returned from getCPT.
+
+        Input evidenceDict is optional.
+        If it is not provided, the CPTs for all variables without 
+        specializing the domains is provided.
+        """
+        if evidenceDict is None or len(evidenceDict.items()) == 0:
+            return self.getCPT(variable)
+        else:
+            reducedVariableDomains = self.getReducedVariableDomains(evidenceDict)
+            variableCPT = self.getCPT(variable)
+            return variableCPT.specializeVariableDomains(reducedVariableDomains)
+
+    def getAllCPTsWithEvidence(self, evidenceDict=None):
+        """
+        Returns a list of conditional probability tables (taking into
+        account evidence) for all variables in the bayes net.
+
+        Input evidenceDict is optional.
+        If it is not provided, the CPTs for all variables without 
+        specializing the domains is provided.
+        """
+        return [self.getCPTWithEvidence(var, evidenceDict) for var in self.__variablesSet]
+
+    def easierToParseString(self, printVariableDomainsDict=False):
+        " Used internally for computer-readable printing "
+        returnStrings = []
+        for CPT in self.getAllCPTsWithEvidence():
+            # CPT has only one unconditioned variable, extract it and use as a prefix
+            prefix = next(iter(CPT.unconditionedVariables()))
+            returnStrings.append(CPT.easierToParseString(prefix=prefix, printVariableDomainsDict=printVariableDomainsDict))
+            printVariableDomainsDict = False
+        return "\n".join(returnStrings)
+
+
+class Factor(object):
+
+    def __init__(self, inputUnconditionedVariables, inputConditionedVariables, inputVariableDomainsDict):
+        """
+        Constructor for factors.
+
+        Takes in as input an iterable unconditionedVariables, an iterable 
+        conditionedVariables, and a variableDomainsDict as a mapping from 
+        variables to domains.
+
+        inputUnconditionedVariables is an iterable of variables (represented as strings)
+            that contains the variables that are unconditioned in this factor 
+        inputConditionedVariables is an iterable of variables (represented as strings)
+            that contains the variables that are conditioned in this factor 
+        inputVariableDomainsDict is a dictionary from variables to domains of those 
+            variables (typically represented as a list but could be any iterable)
+
+        Initializes the probability entries of all possible assignmentDicts to be 0.0
+        """
+        # if only one variable is passed in (not in a list), wrap it in a list
+        if isinstance(inputUnconditionedVariables, str):
+            inputUnconditionedVariables = [inputUnconditionedVariables]
+
+        if isinstance(inputConditionedVariables, str):
+            inputConditionedVariables = [inputConditionedVariables]
+
+        repeatedVariables = set(inputUnconditionedVariables) & set(inputConditionedVariables)
+        if repeatedVariables:
+            raise ValueError("unconditionedVariables and conditionedVariables "\
+                               "can't have repeated \n variables. Repeats:\n" +  str(repeatedVariables))
+
+
+        self.__variables = tuple(inputUnconditionedVariables) + tuple(inputConditionedVariables) # variables are unique string identifiersk
+        self.__variablesSet = set(self.__variables)
+
+        if not self.__variablesSet.issubset(set(inputVariableDomainsDict.keys())): # it's okay for variableDomainsDict to have more items than needed
+            raise ValueError("variableDomainsDict doesn't have all the input variables \n" \
+                               + str(self.__variablesSet))
+
+        self.__unconditionedVariables = set(inputUnconditionedVariables)
+        self.__conditionedVariables = set(inputConditionedVariables)
+        self.__variableDomainsDict = deepcopy(inputVariableDomainsDict) # dict that maps {variable : variableDomain}
+
+        self.__variableOrders = dict([(variable, i) for i, variable in enumerate(self.__variables)]) # internal order of the variables
+        self.__probDict = {} # probability values are stored in an {assignmentValuesTuple : probability} dict,
+                            # since we can't index using assignmentDicts. this is why we have to sort
+        products = list(itertools.product(*[inputVariableDomainsDict[variable] for variable in self.__variables]))
+        for assignmentsInOrder in products:
+            self.__probDict[tuple(assignmentsInOrder)] = 0.0
+
+    def variableDomainsDict(self):
+        " Retuns a copy of the variable domains in the factor "
+        return deepcopy(self.__variableDomainsDict)
+
+    def variables(self):
+        " Retuns a copy of the tuple of variables in the factor "
+        return copy(self.__variables)
+
+    def variablesSet(self):
+        " Retuns a copy of the set of variables in the factor "
+        return copy(self.__variablesSet)
+
+    def unconditionedVariables(self):
+        " Retuns a copy of the unconditioned variables in the factor "
+        return copy(self.__unconditionedVariables)
+
+    def conditionedVariables(self):
+        " Retuns a copy of the conditioned variables in the factor "
+        return copy(self.__conditionedVariables)
+
+    def __eq__(self, other):
+        """
+        Tests equality of two factors.
+
+        Makes sure the unconditionedVariables,
+        conditionedVariables of the two factors are the same.
+        Then makes sure each table in the first is the same 
+        (up to some tolerance) as the ones in the second and vice versa.
+        Returns true if they are the same.
+        """
+        variablesEqual = (self.variablesSet() == other.variablesSet()) \
+                and (set(self.unconditionedVariables()) == set(other.unconditionedVariables())) \
+                and (set(self.conditionedVariables()) == set(other.conditionedVariables()))
+
+        if not variablesEqual:
+            return False
+
+        for assignmentDict in self.getAllPossibleAssignmentDicts():
+            selfProb = self.getProbability(assignmentDict)
+            try:
+                otherProb = other.getProbability(assignmentDict)
+            except ValueError:
+                return False # have different variable domains
+            if abs(selfProb - otherProb) > 10e-13:
+                return False
+
+        for assignmentDict in other.getAllPossibleAssignmentDicts():
+            otherProb = other.getProbability(assignmentDict)
+            try:
+                selfProb = self.getProbability(assignmentDict)
+            except ValueError:
+                return False # have different variable domains
+            if abs(selfProb - otherProb) > 10e-13:
+                return False
+        return True
+
+    def __ne__(self, other):
+        " Tests if two factors are not equal "
+        return not self.__eq__(other)
+
+    def getProbability(self, assignmentDict):
+        """ 
+        Retrieval function for probability values in the factor.
+
+        Input assignmentDict should be of the form {variable : variableValue} for all
+        variables in the factor.
+
+        assignmentDict can have more variables than the factor contains 
+        (for instance, it could have an assignment for all the 
+        variables in a bayes net), and it will select the right row 
+        from this factor, ignoring the variables not contained within. 
+
+        Returns the probability entry stored in the factor for that 
+        combination of variable assignments.
+        """
+        assignmentsInOrder = self.__getAssignmentsInOrder(assignmentDict)
+        if assignmentsInOrder not in self.__probDict:
+            raise ValueError("The input assignmentDict is not contained in this factor: \n" \
+                                +  str(self) + str(assignmentDict))
+        else:
+            return self.__probDict[assignmentsInOrder]
+
+    def setProbability(self, assignmentDict, probability):
+        """ 
+        Setting function for probability values in the factor.
+
+        Input assignmentDict should be of the form {variable : variableValue} 
+        for all variables in the factor.
+        assignmentDict can have more variables than the factor contains 
+        (for instance, it could have an assignment for all the variables in a bayes net),
+        and it will select the right row from this factor, ignoring the variables 
+        not contained within. 
+
+        Input probability is the probability that will be set within the table.
+        It must be non-negative.
+
+        Returns None
+        """
+        if probability < 0: 
+            raise ValueError("Probabilty entries can't be set to negative values: " + \
+                               str(probability))
+        else:
+
+            assignmentsInOrder = self.__getAssignmentsInOrder(assignmentDict)
+            if assignmentsInOrder not in self.__probDict:
+                raise ValueError("The input assignmentDict is not contained in this factor: \n" \
+                                  +  str(self) + str(assignmentDict))
+            else:
+                self.__probDict[assignmentsInOrder] = probability
+
+    def __getAssignmentsInOrder(self, assignmentDict):
+        """
+        Internal utility function for interacting with the stored
+        probability dictionary.
+
+        We would like to store a probability value for each
+        assignmentDict, but dicts aren't hashable since they're
+        mutable, so we can't have a dict with dicts as keys.  
+        So we store the probability table in a dict where the keys are
+        tuples of variable values, without the variable name
+        associated with the value.
+
+        This function takes an assignmentDict and processes it into an
+        ordered tuple of values where the values are the assignments
+        in assignmentDict.
+        We can then use this tuple to directly index into the
+        probability table dict.
+
+        Use factor.getProbability and factor.setProbability instead,
+        for a better interface.
+        """
+        reducedAssignmentDict = dict([(var, val) for (var, val) \
+                                      in assignmentDict.items() if var in self.__variablesSet])
+        variablesAndAssignments = reducedAssignmentDict.items()
+        variablesAndAssignments = sorted(variablesAndAssignments, \
+                                         key=lambda var_val_tuple : self.__variableOrders[var_val_tuple[0]])
+        return tuple([val for (var, val) in variablesAndAssignments])
+
+    def getAllPossibleAssignmentDicts(self):
+        """
+        Use this function to get the assignmentDict for each 
+        possible assignment for the combination of variables contained
+        in the factor.
+
+        Returns a list of all assignmentDicts that the factor contains
+        rows for, allowing you to iterate through each row in the
+        factor when combined with getProbability and setProbability).
+        """
+        cartesianProductOfAssignments = itertools.product(*[self.__variableDomainsDict[variable] for variable in reversed(self.__variables)])
+        return [dict(zip(reversed(self.__variables), product)) for product in cartesianProductOfAssignments]
+
+
+    def __str__(self):
+        """
+        Print a human-readable tabular representation of a factor.
+        """
+        printSizeDict = {}
+        for variable in self.__variablesSet:
+            maxPrintSize = max(len(variable), max([len(str(variableValue)) for variableValue in self.__variableDomainsDict[variable]]))
+            printSizeDict[variable] = maxPrintSize
+
+        returnString = ""
+
+        # header with involved variables and unconditioned or unconditioned
+        returnString += "P("
+        returnString += ", ".join([str(unconditionedVariable) for unconditionedVariable in self.__unconditionedVariables])
+
+        if len(self.__conditionedVariables) > 0:
+            returnString += " | "
+            returnString += ", ".join([str(conditionedVariable) for conditionedVariable in self.__conditionedVariables])
+
+        returnString += ")\n\n"
+
+        # first line of table with variable names
+        varLine = " | " + " | ".join([str(unconditionedVariable)[:printSizeDict[unconditionedVariable]].center(printSizeDict[unconditionedVariable], ' ') 
+                                      for unconditionedVariable in self.__unconditionedVariables])
+        if len(self.__conditionedVariables) > 0:
+            varLine += " | " + " | ".join([str(conditionedVariable)[:printSizeDict[conditionedVariable]].center(printSizeDict[conditionedVariable], ' ') 
+                                                for conditionedVariable in self.__conditionedVariables])
+        varLine += " | " + "Prob:".center(7, " ") + " |"
+
+        varLineLength = len(varLine)
+
+        returnString += varLine + "\n"
+
+
+        # code for checking whether or not to print horizontal line
+        previousConditionedAssignments = []
+        if len(self.__conditionedVariables) == 0:
+            returnString += " " + "".join(["-" for _ in range(varLineLength - 1)]) + "\n"
+
+        # print out each row of table
+        for assignmentDict in self.getAllPossibleAssignmentDicts():
+            # variable assignments
+            if len(self.__conditionedVariables) > 0:
+                conditionedAssignments = [assignmentDict[conditionedVariable] for conditionedVariable in self.__conditionedVariables]
+                if conditionedAssignments != previousConditionedAssignments:
+                    returnString += " " + "".join(["-" for _ in range(varLineLength - 1)]) + "\n"
+                previousConditionedAssignments = conditionedAssignments
+            probability = self.getProbability(assignmentDict)
+            returnString += " | " + " | ".join([str(assignmentDict[unconditionedVariable])[:printSizeDict[unconditionedVariable]].center(printSizeDict[unconditionedVariable], ' ')
+                                                for unconditionedVariable in self.__unconditionedVariables])
+            if len(self.__conditionedVariables) > 0:
+                returnString += " | " + " | ".join([str(assignmentDict[conditionedVariable])[:printSizeDict[conditionedVariable]].center(printSizeDict[conditionedVariable], ' ')
+                                                    for conditionedVariable in self.__conditionedVariables])
+
+            # formatting for printing probability
+            if probability is None:
+                formattedProb = 'None'.center(7, ' ')
+            else:
+                digits = len(str(round(probability)))
+                formattedProb = "%.1e" % probability if probability < 10e-2 else ("%." + str(8 - digits) + "f") % probability
+            returnString += " | " + formattedProb
+            returnString += " |\n"
+        return returnString
+
+    def __repr__(self):
+        returnRepr = "Factor("
+        initArgs = [self.__unconditionedVariables, self.__conditionedVariables, self.__variableDomainsDict]
+        returnRepr += ", ".join([repr(arg) for arg in initArgs])
+        returnRepr += ")"
+        return returnRepr
+
+    def easierToParseString(self, prefix=None, printVariableDomainsDict=True):
+        """
+        Print a representation of the bayes net that we have a parser for (in bayesNetTestClasses).
+        """
+        if prefix is None:
+            prefix = ''
+        returnString = ""
+        if printVariableDomainsDict:
+            returnString += 'variableDomainsDict: """\n'
+            for (key, domain) in self.__variableDomainsDict.items():
+                returnString += str(key) + ' : ' + " ".join([value for value in domain]) + '\n'
+            returnString += '"""\n\n'
+
+        returnString += prefix + 'unconditionedVariables: "'
+        returnString += " ".join([unconditionedVariable for unconditionedVariable in self.__unconditionedVariables])
+        returnString += '"\n\n'
+
+        returnString += prefix + 'conditionedVariables: "'
+        returnString += " ".join([conditionedVariable for conditionedVariable in self.__conditionedVariables])
+        returnString += '"\n\n'
+
+        returnString += prefix + 'FactorTable: """\n'
+        for assignmentDict in self.getAllPossibleAssignmentDicts():
+            probability = self.getProbability(assignmentDict)
+            returnString += ", ".join([variable + " : " + str(assignmentDict[variable]) \
+                                       for variable in self.__variables])
+            returnString += " = " + str(probability) + "\n"
+        returnString += '"""\n\n'
+        return returnString
+
+    def specializeVariableDomains(self, newVariableDomainsDict):
+        """
+        Returns a factor with the same variables as this factor
+        but with the reduced variable domains given by
+        newVariableDomainsDict.
+
+        The entries in the probability are taken from the
+        corresponding entries in this factor.
+        """
+
+        # Make sure that newVariableDomainsDict has smaller or equal
+        # domain to factor.variableDomainsDict for all variables that
+        # this factor contains.    
+        oldVariableDomains = self.variableDomainsDict()
+        for (variable, domain) in newVariableDomainsDict.items():
+            if variable in self.variablesSet():
+                oldVariableDomain = oldVariableDomains[variable]
+                for value in domain:
+                    if value not in oldVariableDomain:
+                        raise ValueError("newVariableDomainsDict is not a subset of factor.variableDomainsDict ",
+                                            "for variables contained in factor. " + "factor: " +  str(self) + 
+                                            " newVariableDomainsDict: " + str(newVariableDomainsDict) +
+                                            " factor.variableDomainsDict: " + str(self.variableDomainsDict()) +
+                                            " variable: " + str(variable) +
+                                            " value: " + str(value))
+
+        newFactor = Factor(self.unconditionedVariables(), self.conditionedVariables(), newVariableDomainsDict)
+
+        for assignmentDict in newFactor.getAllPossibleAssignmentDicts():
+            newFactor.setProbability(assignmentDict, self.getProbability(assignmentDict))
+
+        return newFactor
+
+
+### bayes net construction utils
+
+def constructEmptyBayesNet(variableList, edgeTuplesList, variableDomainsDict):
+    " More convenient constructor for Bayes nets "
+    variablesSet = set(variableList)
+    inEdges  = defaultdict(set)   
+    outEdges = defaultdict(set)   
+    for (parent, child) in edgeTuplesList:
+        # add the variables to the variables set
+        inEdges[child].add(parent)
+        outEdges[parent].add(child)
+
+    newBayesNet = BayesNet(variablesSet, inEdges, outEdges, variableDomainsDict)
+    return newBayesNet
+
+def constructEmptyBayesNetFromString(bayesNetString):
+    variables = bayesNetString.split('\n')[0][len('Variables: '):].split(', ')
+    edgeStrings = bayesNetString.split('\n')[1][len('Edges: '):].split(', ')
+    edgeList = [(u, v) for u, _, v in map(tuple, map(str.split, edgeStrings))]
+    variableDomainsDict = eval(bayesNetString.split('\n')[2])
+    return constructEmptyBayesNet(variables, edgeList, variableDomainsDict)
+
+def constructRandomlyFilledBayesNet(variableList, edgeTuplesList, variableDomainsDict):
+    " Random Bayes net constructor "
+    bayesNet = constructEmptyBayesNet(variableList, edgeTuplesList, variableDomainsDict)
+    fillTablesRandomly(bayesNet)
+    return bayesNet
+
+
+def fillTablesRandomly(bayesNet):
+    " Fills a Bayes net with random variables "
+    for variable in bayesNet.variablesSet():
+        conditionedVariables = bayesNet.inEdges()[variable]
+        conditionedVariablesList = list(conditionedVariables)
+        CPT = constructAndFillFactorRandomly([variable], conditionedVariablesList, bayesNet.variableDomainsDict())
+        bayesNet.setCPT(variable, CPT)
+
+def fillWithOneConditionedAssignmentRandomly(factor, unconditionedVariables, conditionedVariables, product, variableDomainsDict):
+    """ 
+    Fills one subtable of a factor (given one conditional assignment).
+    Makes this subtable sum to 1.
+    """
+    cartesianProductOfUnConditionalAssignments = itertools.product(*[variableDomainsDict[unconditionedVariable] 
+                                                                   for unconditionedVariable in unconditionedVariables])
+    randomFills = [max(0.0, random.uniform(-0.4, 0.8)) for variableValue in cartesianProductOfUnConditionalAssignments]
+    conditionalProbabilitySum = sum(randomFills)
+
+    # needs to sum to 1
+    if abs(conditionalProbabilitySum) < 10e-13:
+        randomFills[0] = 1.0
+        conditionalProbabilitySum = sum(randomFills)
+
+    cartesianProductOfUnConditionalAssignments = itertools.product(*[variableDomainsDict[unconditionedVariable] 
+                                                                   for unconditionedVariable in unconditionedVariables])
+
+    for (randomFill, variableValue) in zip(randomFills, cartesianProductOfUnConditionalAssignments):
+        assignmentDict = dict(zip(list(unconditionedVariables) + list(conditionedVariables), list(variableValue) + list(product)))
+        factor.setProbability(assignmentDict, randomFill / conditionalProbabilitySum)
+
+
+def constructAndFillFactorRandomly(unconditionedVariables, conditionedVariables, variableDomainsDict):
+    " Wrapper around Factor constructor that fills the table randomly "
+    newFactor = Factor(unconditionedVariables, conditionedVariables, variableDomainsDict)
+    if len(conditionedVariables) > 0:
+        cartesianProductOfConditionalAssignments = itertools.product(*[variableDomainsDict[conditionedVariable] for conditionedVariable in conditionedVariables])
+        for product in cartesianProductOfConditionalAssignments:
+            fillWithOneConditionedAssignmentRandomly(newFactor, unconditionedVariables, conditionedVariables, product, variableDomainsDict)
+    else:
+        fillWithOneConditionedAssignmentRandomly(newFactor, unconditionedVariables, [], [], variableDomainsDict)
+    return newFactor
+
+def reduceBayesNetVariablesWithEvidence(bayesNet, variablesToRemove,
+                                        evidenceDict):
+    """
+    Prunes the variables in variablesToRemove away from the Bayes net 
+    and returns a new Bayes net without variablesToRemove
+    """
+    variablesToRemoveSet = set(variablesToRemove)
+    evidenceVariables = set(evidenceDict.keys())
+    if len(variablesToRemoveSet & evidenceVariables) > 0:
+        raise ValueError("Evidence variables are in the list of variable to " + \
+                           "be removed from the Bayes' net.  This is " + \
+                           "undefined. Evidence: " + str(evidenceDict) + \
+                           ". Variables to remove: " + str(variablesToRemoveSet))
+
+    newVariables = bayesNet.variablesSet() - variablesToRemoveSet
+    oldOutEdges = bayesNet.outEdges()
+    oldInEdges  = bayesNet.inEdges()
+    newOutEdges = dict()
+    newInEdges  = dict()
+    for variable in newVariables:
+        newOutEdges[variable] = set([y for y in oldOutEdges[variable] if y in newVariables])
+        newInEdges[variable]  = set([y for y in oldInEdges[variable]  if y in newVariables])
+
+    newVariableDomainsDict = bayesNet.getReducedVariableDomains(evidenceDict)
+
+    newBayesNet = BayesNet(newVariables, newInEdges, newOutEdges,
+                           newVariableDomainsDict)
+
+    unconditionedVariables = newVariables - evidenceVariables
+    for variable in bayesNet.variablesSet():
+        if variable in newVariables:
+            oldCPT = bayesNet.getCPT(variable)
+            evidenceVariablesParents = []
+            removedVariablesParents = []
+            unconditionedVariablesParents = []
+            for parentVariable in oldCPT.conditionedVariables():
+                if parentVariable in variablesToRemoveSet:
+                    removedVariablesParents.append(parentVariable)
+                elif parentVariable in evidenceVariables:
+                    evidenceVariablesParents.append(parentVariable)
+                else:
+                    unconditionedVariablesParents.append(parentVariable)
+
+            if variable in evidenceVariables and \
+                    len(unconditionedVariablesParents) == 0:
+                newCPT = Factor([variable], evidenceVariablesParents, \
+                                newVariableDomainsDict)
+                # only one entry in this CPT since all parents are 
+                # removed or evidence variables (and thus have one entry)
+                newCPT.setProbability(evidenceDict, 1.0)
+
+            else:
+                if len(removedVariablesParents) == 0:
+                    newCPT = oldCPT.specializeVariableDomains(newVariableDomainsDict)
+                else:
+                    raise ValueError("Variable: " + str(variable) + \
+                                       "'s parent: " + str(parentVariable) + \
+                                       " is not in the reduced bayes net, " + \
+                                       "so we can't unambiguously reduce the " + \
+                                       "Bayes' net.")
+
+            newBayesNet.setCPT(variable, newCPT)
+        else:
+            oldCPT = bayesNet.getCPT(variable)
+            #for parentVariable in oldCPT.conditionedVariables():
+                #if parentVariable in unconditionedVariables:
+                    #raise ValueError("Variable " + str(variable) + \
+                                       #" is to be removed but its parent " \
+                                        #+ str(parentVariable) + \
+                                        #" is an unconditioned variable in the " \
+                                        #+ "reduced bayes net, " + \
+                                        #"so we can't reduce the " + \
+                                        #"Bayes' net.")
+
+
+    return newBayesNet
+
+
+def printStarterBayesNet():
+    """
+    Exploring Bayes net functions, printing, and creation.
+    Pay close attention to how factors are created and modified.
+    """
+
+    # This is the example V structured Bayes' net from the lecture 
+    # on Bayes' nets independence.
+    # Constructing Bayes' nets: variables list
+    variableList = ['Raining', 'Ballgame', 'Traffic']
+
+    # Constructing Bayes' nets, edge list: (x, y) means edge from x to y
+    edgeTuplesList = [('Raining', 'Traffic'), ('Ballgame', 'Traffic')]
+
+    # Construct the domain for each variable (a list like)
+    variableDomainsDict = {}
+    variableDomainsDict['Raining']  = ['yes', 'no']
+    variableDomainsDict['Ballgame'] = ['yes', 'no']
+    variableDomainsDict['Traffic']  = ['yes', 'no']
+
+    # None of the conditional probability tables are assigned yet in our Bayes' net
+    bayesNet = constructEmptyBayesNet(variableList, edgeTuplesList, variableDomainsDict)
+
+    # Create a factor for each CPT.  
+    # The first input is the list of unconditioned variables in your factor,
+    # the second input is the list of conditioned variables in your factor,
+    # and the third input is the dict of domains for your variables.
+    rainingCPT  = Factor(['Raining'], [], variableDomainsDict)
+
+    print("Print a conditional probability table (henceforth known as a CPT) " + \
+          "to see a pretty print of the variables in a factor and its " + \
+          "probability table in your terminal. " + \
+          "CPTs come initialized with 0 for each row in the table: \n")
+    print(rainingCPT)
+
+    # We use assignmentDicts to set and get probability entries from Factors.
+    # An assignmentDict is a dict {variable : variableValue} of assignments 
+    # of variables to values (where the variableValue must be in 
+    # variableDomainsDict[variable]
+
+    rainAssignmentDict = {'Raining' : 'yes'}
+    rainingCPT.setProbability(rainAssignmentDict, 0.3)
+
+    rainAssignmentDict = {'Raining' : 'no'}
+    rainingCPT.setProbability(rainAssignmentDict, 0.7)
+    
+    print('After setting entries: \n')
+
+    print(rainingCPT)
+
+    # The traffic factor has two conditioned variables and one unconditioned 
+    # variable.  Each variable has a domain size of 2, so we have 
+    # 2^3 = 8 possible assignments (and thus 8 rows in our probability table).
+
+    trafficCPT  = Factor(['Traffic'], ['Raining', 'Ballgame'], variableDomainsDict)
+
+    TRB = {'Traffic' : 'yes', 'Raining' : 'yes', 'Ballgame' : 'yes'}
+    tRB = {'Traffic' : 'no',  'Raining' : 'yes', 'Ballgame' : 'yes'}
+    TrB = {'Traffic' : 'yes', 'Raining' : 'no',  'Ballgame' : 'yes'}
+    trB = {'Traffic' : 'no',  'Raining' : 'no',  'Ballgame' : 'yes'}
+    TRb = {'Traffic' : 'yes', 'Raining' : 'yes', 'Ballgame' : 'no' }
+    tRb = {'Traffic' : 'no',  'Raining' : 'yes', 'Ballgame' : 'no' }
+    Trb = {'Traffic' : 'yes', 'Raining' : 'no',  'Ballgame' : 'no' }
+    trb = {'Traffic' : 'no',  'Raining' : 'no',  'Ballgame' : 'no' }
+
+    # For a CPT, we must have that the sum of the probability of all the 
+    # unconditionedVariables for a given assignment of conditioned 
+    # variables must sum to 1
+    trafficCPT.setProbability(TRB, 0.95)
+    trafficCPT.setProbability(tRB, 0.05)
+    trafficCPT.setProbability(TrB, 0.90)
+    trafficCPT.setProbability(trB, 0.10)
+    trafficCPT.setProbability(TRb, 0.70)
+    trafficCPT.setProbability(tRb, 0.30)
+    trafficCPT.setProbability(Trb, 0.15)
+    trafficCPT.setProbability(trb, 0.85)
+
+    print("Note that in the table output of print for factors with conditioned " + \
+          "variables, each region with a different assignment of conditioned " + \
+          "variables is divided into a region in the table, separated from " + \
+          "other conditioned assignments by a horizontal bar. " + \
+          "If a factor is a CPT, each sub table of that factor will sum to 1. \n")
+    print(trafficCPT)
+
+    print("You can use factor.getAllPossibleAssignmentDicts() " + \
+          "to iterate through all combinations of assignments:\n")
+    for assignmentDict in trafficCPT.getAllPossibleAssignmentDicts():
+        print(assignmentDict)
+
+    # Fill in the ballGame CPT, very similar to raining
+
+    ballgameCPT = Factor(['Ballgame'], [], variableDomainsDict)
+
+    # Note that we can use assignmentDicts that contain assignments for 
+    # more variables than a factor mentions.
+    # Here, we pass in an assignmentDict that has 3 variable assignments 
+    # but ballgameCPT only contains variable Ballgame
+    ballgameCPT.setProbability(TRB, 0.05)
+
+    ballgameCPT.setProbability(TRb, 0.95)
+
+    print("\nLast CPT: \n")
+
+    print(ballgameCPT)
+
+    # Set the factors for the bayes net to be these CPTs
+    bayesNet.setCPT('Raining',  rainingCPT)
+    bayesNet.setCPT('Ballgame', ballgameCPT)
+    bayesNet.setCPT('Traffic',  trafficCPT)
+
+    print("Print a Bayes' net to see its variables, edges, and " + \
+          "the CPT for each variable.\n")
+    print(bayesNet)
+
+    print("You can get a list of all CPTs from a Bayes' net, instantiated with " + \
+          "evidence, with the getAllCPTsWithEvidence function. " + \
+          "The evidenceDict input is an assignmentDict of " + \
+          "(evidenceVariable, evidenceValue) pairs. " + \
+          "Instantiation with evidence reduces the variable domains and thus " + \
+          "selects a subset of entries from the probability table.")
+
+    evidenceDict = {'Raining' : 'yes'}
+    for CPT in bayesNet.getAllCPTsWithEvidence(evidenceDict):
+        print(CPT)
+
+    print('If it is empty or None, the full CPTs will be returned. \n')
+
+    for CPT in bayesNet.getAllCPTsWithEvidence():
+        print(CPT)
+
+    print("If only one variable's CPT is desired, you can get just that particular " + \
+          "CPT with the bayesNet.getCPT function. \n")
+
+    print(bayesNet.getCPT('Traffic'))
+
+    print(bayesNet.easierToParseString())
+
+def normalize(factor):
+    """
+    Normalizes, assumes the operation is mathematically valid on the passed in factor.
+    """
+    variableDomainsDict = factor.variableDomainsDict()
+    for conditionedVariable in factor.conditionedVariables():
+        if len(variableDomainsDict[conditionedVariable]) > 1:
+            print("Factor failed normalize typecheck: ", factor)
+            raise ValueError("The factor to be normalized must have only one " + \
+                            "assignment of the \n" + "conditional variables, " + \
+                            "so that total probability will sum to 1\n" + 
+                            str(factor))
+    newUCVars, newCVars = factor.unconditionedVariables(), factor.conditionedVariables()
+    for var in variableDomainsDict:
+        if len(variableDomainsDict[var]) == 1 and var in newUCVars:
+            newUCVars.remove(var)
+            newCVars.add(var)
+    newFactor = Factor(newUCVars, newCVars, variableDomainsDict)
+    probSum = sum([factor.getProbability(a) for a in factor.getAllPossibleAssignmentDicts()])
+    if not probSum:
+        return None
+    for a in factor.getAllPossibleAssignmentDicts():
+        normalizedProb = factor.getProbability(a) / probSum
+        newFactor.setProbability(a, normalizedProb)
+
+    return newFactor
+
+
+if __name__ == "__main__":
+    printStarterBayesNet()

tracking/busters.py

@@ -0,0 +1,568 @@
+# busters.py
+# ----------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+"""
+Busters.py is a vengeful variant of Pacman where Pacman hunts ghosts, but
+cannot see them.  Numbers at the bottom of the display are noisy distance
+readings to each remaining ghost.
+
+To play your first game, type 'python pacman.py' from the command line.
+The keys are 'a', 's', 'd', and 'w' to move (or arrow keys).  Have fun!
+"""
+from game import GameStateData
+from game import Game
+from game import Directions
+from game import Actions
+from game import Configuration
+from util import nearestPoint
+from util import manhattanDistance
+import sys, util, types, time, random, layout, os
+
+########################################
+# Parameters for noisy sensor readings #
+########################################
+
+SONAR_NOISE_RANGE = 15 # Must be odd
+SONAR_MAX = (SONAR_NOISE_RANGE - 1)/2
+SONAR_NOISE_VALUES = [i - SONAR_MAX for i in range(SONAR_NOISE_RANGE)]
+SONAR_DENOMINATOR = 2 ** SONAR_MAX  + 2 ** (SONAR_MAX + 1) - 2.0
+SONAR_NOISE_PROBS = [2 ** (SONAR_MAX-abs(v)) / SONAR_DENOMINATOR  for v in SONAR_NOISE_VALUES]
+
+def getNoisyDistance(pos1, pos2):
+    if pos2[1] == 1: return None
+    distance = util.manhattanDistance(pos1, pos2)
+    return max(0, distance + util.sample(SONAR_NOISE_PROBS, SONAR_NOISE_VALUES))
+
+observationDistributions = {}
+def getObservationProbability(noisyDistance, trueDistance):
+    """
+    Returns the probability P( noisyDistance | trueDistance ).
+    """
+    global observationDistributions
+    if noisyDistance not in observationDistributions:
+        distribution = util.Counter()
+        for error , prob in zip(SONAR_NOISE_VALUES, SONAR_NOISE_PROBS):
+            distribution[max(1, noisyDistance - error)] += prob
+        observationDistributions[noisyDistance] = distribution
+    return observationDistributions[noisyDistance][trueDistance]
+
+###################################################
+# YOUR INTERFACE TO THE PACMAN WORLD: A GameState #
+###################################################
+
+class GameState:
+    """
+    A GameState specifies the full game state, including the food, capsules,
+    agent configurations and score changes.
+
+    GameStates are used by the Game object to capture the actual state of the game and
+    can be used by agents to reason about the game.
+
+    Much of the information in a GameState is stored in a GameStateData object.  We
+    strongly suggest that you access that data via the accessor methods below rather
+    than referring to the GameStateData object directly.
+
+    Note that in classic Pacman, Pacman is always agent 0.
+    """
+
+    ####################################################
+    # Accessor methods: use these to access state data #
+    ####################################################
+
+    def getLegalActions( self, agentIndex=0 ):
+        """
+        Returns the legal actions for the agent specified.
+        """
+        if self.isWin() or self.isLose(): return []
+
+        if agentIndex == 0:  # Pacman is moving
+            return PacmanRules.getLegalActions( self )
+        else:
+            return GhostRules.getLegalActions( self, agentIndex )
+
+    def getResult( self, agentIndex, action):
+        """
+        Returns the state after the specified agent takes the action.
+        """
+        # Check that successors exist
+        if self.isWin() or self.isLose(): raise Exception('Can\'t generate a result of a terminal state.')
+
+        # Copy current state
+        state = GameState(self)
+
+        # Let agent's logic deal with its action's effects on the board
+        if agentIndex == 0:  # Pacman is moving
+            state.data._eaten = [False for i in range(state.getNumAgents())]
+            PacmanRules.applyAction( state, action )
+        else:                # A ghost is moving
+            GhostRules.applyAction( state, action, agentIndex )
+
+        # Time passes
+        if agentIndex == 0:
+            state.data.scoreChange += -TIME_PENALTY # Penalty for waiting around
+        else:
+            GhostRules.decrementTimer( state.data.agentStates[agentIndex] )
+
+        # Resolve multi-agent effects
+        GhostRules.checkDeath( state, agentIndex )
+
+        # Book keeping
+        state.data._agentMoved = agentIndex
+        state.data.score += state.data.scoreChange
+        p = state.getPacmanPosition()
+        state.data.ghostDistances = [getNoisyDistance(p, state.getGhostPosition(i)) for i in range(1,state.getNumAgents())]
+        if agentIndex == self.getNumAgents() - 1:
+            state.numMoves += 1
+        return state
+
+    def getLegalPacmanActions( self ):
+        return self.getLegalActions( 0 )
+
+    def getPacmanResult( self, action ):
+        """
+        Generates the result state after the specified pacman action
+        """
+        return self.getResult( 0, action )
+
+    def getPacmanState( self ):
+        """
+        Returns an AgentState object for pacman (in game.py)
+
+        state.pos gives the current position
+        state.direction gives the travel vector
+        """
+        return self.data.agentStates[0].copy()
+
+    def getPacmanPosition( self ):
+        return self.data.agentStates[0].getPosition()
+
+    def getNumAgents( self ):
+        return len( self.data.agentStates )
+
+    def getScore( self ):
+        return self.data.score
+
+    def getCapsules(self):
+        """
+        Returns a list of positions (x,y) of the remaining capsules.
+        """
+        return self.data.capsules
+
+    def getNumFood( self ):
+        return self.data.food.count()
+
+    def getFood(self):
+        """
+        Returns a Grid of boolean food indicator variables.
+
+        Grids can be accessed via list notation, so to check
+        if there is food at (x,y), just call
+
+        currentFood = state.getFood()
+        if currentFood[x][y] == True: ...
+        """
+        return self.data.food
+
+    def getWalls(self):
+        """
+        Returns a Grid of boolean wall indicator variables.
+
+        Grids can be accessed via list notation, so to check
+        if there is food at (x,y), just call
+
+        walls = state.getWalls()
+        if walls[x][y] == True: ...
+        """
+        return self.data.layout.walls
+
+    def hasFood(self, x, y):
+        return self.data.food[x][y]
+
+    def hasWall(self, x, y):
+        return self.data.layout.walls[x][y]
+
+    ##############################
+    # Additions for Busters Pacman #
+    ##############################
+
+    def getLivingGhosts(self):
+        """
+        Returns a list of booleans indicating which ghosts are not yet captured.
+
+        The first entry (a placeholder for Pacman's index) is always False.
+        """
+        return self.livingGhosts
+
+    def setGhostNotLiving(self, index):
+        self.livingGhosts[index] = False
+
+    def isLose( self ):
+        return self.maxMoves > 0 and self.numMoves >= self.maxMoves
+
+    def isWin( self ):
+        return self.livingGhosts.count(True) == 0
+
+    def getNoisyGhostDistances(self):
+        """
+        Returns a noisy distance to each ghost.
+        """
+        return self.data.ghostDistances
+
+    #############################################
+    #             Helper methods:               #
+    # You shouldn't need to call these directly #
+    #############################################
+
+    def __init__( self, prevState = None ):
+        """
+        Generates a new state by copying information from its predecessor.
+        """
+        if prevState != None:
+            self.data = GameStateData(prevState.data)
+            self.livingGhosts = prevState.livingGhosts[:]
+            self.numMoves = prevState.numMoves;
+            self.maxMoves = prevState.maxMoves;
+        else: # Initial state
+            self.data = GameStateData()
+            self.numMoves = 0;
+            self.maxMoves = -1;
+        self.data.ghostDistances = []
+
+    def deepCopy( self ):
+        state = GameState( self )
+        state.data = self.data.deepCopy()
+        state.data.ghostDistances = self.data.ghostDistances
+        return state
+
+    def __eq__( self, other ):
+        """
+        Allows two states to be compared.
+        """
+        if other is None:
+            return False
+        return self.data == other.data
+
+    def __hash__( self ):
+        """
+        Allows states to be keys of dictionaries.
+        """
+        return hash( str( self ) )
+
+    def __str__( self ):
+
+        return str(self.data)
+
+    def initialize( self, layout, numGhostAgents=1000 ):
+        """
+        Creates an initial game state from a layout array (see layout.py).
+        """
+        self.data.initialize(layout, numGhostAgents)
+        self.livingGhosts = [False] + [True for i in range(numGhostAgents)]
+        self.data.ghostDistances = [getNoisyDistance(self.getPacmanPosition(), self.getGhostPosition(i)) for i in range(1, self.getNumAgents())]
+
+    def getGhostPosition( self, agentIndex ):
+        if agentIndex == 0:
+            raise "Pacman's index passed to getGhostPosition"
+        return self.data.agentStates[agentIndex].getPosition()
+
+    def getGhostState( self, agentIndex ):
+        if agentIndex == 0:
+            raise "Pacman's index passed to getGhostPosition"
+        return self.data.agentStates[agentIndex]
+
+############################################################################
+#                     THE HIDDEN SECRETS OF PACMAN                         #
+#                                                                          #
+# You shouldn't need to look through the code in this section of the file. #
+############################################################################
+
+COLLISION_TOLERANCE = 0.7 # How close ghosts must be to Pacman to kill
+TIME_PENALTY = 1 # Number of points lost each round
+
+class BustersGameRules:
+    """
+    These game rules manage the control flow of a game, deciding when
+    and how the game starts and ends.
+    """
+
+    def newGame( self, layout, pacmanAgent, ghostAgents, display, maxMoves= -1 ):
+        agents = [pacmanAgent] + ghostAgents
+        initState = GameState()
+        initState.initialize( layout, len(ghostAgents))
+        game = Game(agents, display, self)
+        game.state = initState
+        game.state.maxMoves = maxMoves
+        return game
+
+    def process(self, state, game):
+        """
+        Checks to see whether it is time to end the game.
+        """
+        if state.isWin(): self.win(state, game)
+        if state.isLose(): self.lose(state, game)
+
+    def win( self, state, game ):
+        game.gameOver = True
+
+    def lose( self, state, game ):
+        game.gameOver = True
+
+class PacmanRules:
+    """
+    These functions govern how pacman interacts with his environment under
+    the classic game rules.
+    """
+    def getLegalActions( state ):
+        """
+        Returns a list of possible actions.
+        """
+        return Actions.getPossibleActions( state.getPacmanState().configuration, state.data.layout.walls )
+    getLegalActions = staticmethod( getLegalActions )
+
+    def applyAction( state, action ):
+        """
+        Edits the state to reflect the results of the action.
+        """
+        legal = PacmanRules.getLegalActions( state )
+        if action not in legal:
+            raise Exception("Illegal action {}".format(action))
+
+        pacmanState = state.data.agentStates[0]
+
+        # Update Configuration
+        vector = Actions.directionToVector( action, 1)
+        pacmanState.configuration = pacmanState.configuration.generateSuccessor( vector )
+
+    applyAction = staticmethod( applyAction )
+
+class GhostRules:
+    """
+    These functions dictate how ghosts interact with their environment.
+    """
+    def getLegalActions( state, ghostIndex ):
+        conf = state.getGhostState( ghostIndex ).configuration
+        return Actions.getPossibleActions( conf, state.data.layout.walls )
+    getLegalActions = staticmethod( getLegalActions )
+
+    def applyAction( state, action, ghostIndex):
+        legal = GhostRules.getLegalActions( state, ghostIndex )
+        if action not in legal:
+            raise Exception("Illegal ghost action: " + str(action))
+
+        ghostState = state.data.agentStates[ghostIndex]
+        vector = Actions.directionToVector( action, 1 )
+        ghostState.configuration = ghostState.configuration.generateSuccessor( vector )
+    applyAction = staticmethod( applyAction )
+
+    def decrementTimer( ghostState):
+        timer = ghostState.scaredTimer
+        if timer == 1:
+            ghostState.configuration.pos = nearestPoint( ghostState.configuration.pos )
+        ghostState.scaredTimer = max( 0, timer - 1 )
+    decrementTimer = staticmethod( decrementTimer )
+
+    def checkDeath( state, agentIndex):
+        pacmanPosition = state.getPacmanPosition()
+        if agentIndex == 0: # Pacman just moved; Anyone can kill him
+            for index in range( 1, len( state.data.agentStates ) ):
+                ghostState = state.data.agentStates[index]
+                ghostPosition = ghostState.configuration.getPosition()
+                if GhostRules.canKill( pacmanPosition, ghostPosition ):
+                    GhostRules.collide( state, ghostState, index )
+        else:
+            ghostState = state.data.agentStates[agentIndex]
+            ghostPosition = ghostState.configuration.getPosition()
+            if GhostRules.canKill( pacmanPosition, ghostPosition ):
+                GhostRules.collide( state, ghostState, agentIndex )
+    checkDeath = staticmethod( checkDeath )
+
+    def collide( state, ghostState, agentIndex):
+        state.data.scoreChange += 200
+        GhostRules.placeGhost(ghostState, agentIndex)
+        # Added for first-person
+        state.data._eaten[agentIndex] = True
+        state.setGhostNotLiving(agentIndex)
+    collide = staticmethod( collide )
+
+    def canKill( pacmanPosition, ghostPosition ):
+        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE
+    canKill = staticmethod( canKill )
+
+    def placeGhost(ghostState, agentIndex):
+        pos = (agentIndex * 2 - 1, 1)
+        direction = Directions.STOP
+        ghostState.configuration = Configuration(pos, direction)
+    placeGhost = staticmethod( placeGhost )
+
+class RandomGhost:
+    def __init__( self, index ):
+        self.index = index
+
+    def getAction( self, state ):
+        return random.choice( state.getLegalActions( self.index ) )
+
+    def getDistribution( self, state ):
+        actions = state.getLegalActions( self.index )
+        prob = 1.0 / len( actions )
+        return [( prob, action ) for action in actions]
+
+#############################
+# FRAMEWORK TO START A GAME #
+#############################
+
+def default(str):
+    return str + ' [Default: %default]'
+
+def parseAgentArgs(str):
+    if str == None: return {}
+    pieces = str.split(',')
+    opts = {}
+    for p in pieces:
+        if '=' in p:
+            key, val = p.split('=')
+        else:
+            key,val = p, 1
+        opts[key] = val
+    return opts
+
+def readCommand( argv ):
+    """
+    Processes the command used to run pacman from the command line.
+    """
+    from optparse import OptionParser
+    usageStr = """
+    USAGE:      python busters.py <options>
+    EXAMPLE:    python busters.py --layout bigHunt
+                  - starts an interactive game on a big board
+    """
+    parser = OptionParser(usageStr)
+
+    parser.add_option('-n', '--numGames', dest='numGames', type='int',
+                      help=default('the number of GAMES to play'), metavar='GAMES', default=1)
+    parser.add_option('-l', '--layout', dest='layout',
+                      help=default('the LAYOUT_FILE from which to load the map layout'),
+                      metavar='LAYOUT_FILE', default='oneHunt')
+    parser.add_option('-p', '--pacman', dest='pacman',
+                      help=default('the agent TYPE in the pacmanAgents module to use'),
+                      metavar='TYPE', default='BustersKeyboardAgent')
+    parser.add_option('-a','--agentArgs',dest='agentArgs',
+                      help='Comma seperated values sent to agent. e.g. "opt1=val1,opt2,opt3=val3"')
+    parser.add_option('-g', '--ghosts', dest='ghost',
+                      help=default('the ghost agent TYPE in the ghostAgents module to use'),
+                      metavar = 'TYPE', default='RandomGhost')
+    parser.add_option('-q', '--quietTextGraphics', action='store_true', dest='quietGraphics',
+                      help='Generate minimal output and no graphics', default=False)
+    parser.add_option('-k', '--numghosts', type='int', dest='numGhosts',
+                      help=default('The maximum number of ghosts to use'), default=4)
+    parser.add_option('-z', '--zoom', type='float', dest='zoom',
+                      help=default('Zoom the size of the graphics window'), default=1.0)
+    parser.add_option('-f', '--fixRandomSeed', action='store_true', dest='fixRandomSeed',
+                      help='Fixes the random seed to always play the same game', default=False)
+    parser.add_option('-s', '--showGhosts', action='store_true', dest='showGhosts',
+                      help='Renders the ghosts in the display (cheating)', default=False)
+    parser.add_option('-t', '--frameTime', dest='frameTime', type='float',
+                      help=default('Time to delay between frames; <0 means keyboard'), default=0.1)
+
+    options, otherjunk = parser.parse_args()
+    if len(otherjunk) != 0:
+        raise Exception('Command line input not understood: ' + otherjunk)
+    args = dict()
+
+    # Fix the random seed
+    if options.fixRandomSeed: random.seed('bustersPacman')
+
+    # Choose a layout
+    args['layout'] = layout.getLayout( options.layout )
+    if args['layout'] == None: raise Exception("The layout " + options.layout + " cannot be found")
+
+    # Choose a ghost agent
+    ghostType = loadAgent(options.ghost, options.quietGraphics)
+    args['ghosts'] = [ghostType( i+1 ) for i in range( options.numGhosts )]
+
+    # Choose a Pacman agent
+    noKeyboard = options.quietGraphics
+    pacmanType = loadAgent(options.pacman, noKeyboard)
+    agentOpts = parseAgentArgs(options.agentArgs)
+    agentOpts['ghostAgents'] = args['ghosts']
+    pacman = pacmanType(**agentOpts) # Instantiate Pacman with agentArgs
+    args['pacman'] = pacman
+
+    import graphicsDisplay
+    args['display'] = graphicsDisplay.FirstPersonPacmanGraphics(options.zoom, \
+                                                                  options.showGhosts, \
+                                                                  frameTime = options.frameTime)
+    args['numGames'] = options.numGames
+
+    return args
+
+def loadAgent(pacman, nographics):
+    # Looks through all pythonPath Directories for the right module,
+    pythonPathStr = os.path.expandvars("$PYTHONPATH")
+    if pythonPathStr.find(';') == -1:
+        pythonPathDirs = pythonPathStr.split(':')
+    else:
+        pythonPathDirs = pythonPathStr.split(';')
+    pythonPathDirs.append('.')
+
+    for moduleDir in pythonPathDirs:
+        if not os.path.isdir(moduleDir): continue
+        moduleNames = [f for f in os.listdir(moduleDir) if f.endswith('gents.py')]
+        for modulename in moduleNames:
+            try:
+                module = __import__(modulename[:-3])
+            except ImportError:
+                continue
+            if pacman in dir(module):
+                if nographics and modulename == 'keyboardAgents.py':
+                    raise Exception('Using the keyboard requires graphics (not text display)')
+                return getattr(module, pacman)
+    raise Exception('The agent ' + pacman + ' is not specified in any *Agents.py.')
+
+def runGames( layout, pacman, ghosts, display, numGames, maxMoves=-1):
+    # Hack for agents writing to the display
+    import __main__
+    __main__.__dict__['_display'] = display
+
+    rules = BustersGameRules()
+    games = []
+
+    for i in range( numGames ):
+        game = rules.newGame( layout, pacman, ghosts, display, maxMoves )
+        game.run()
+        games.append(game)
+
+    if numGames > 1:
+        scores = [game.state.getScore() for game in games]
+        wins = [game.state.isWin() for game in games]
+        winRate = wins.count(True)/ float(len(wins))
+        print('Average Score:', sum(scores) / float(len(scores)))
+        print('Scores:       ', ', '.join([str(score) for score in scores]))
+        print('Win Rate:      %d/%d (%.2f)' % (wins.count(True), len(wins), winRate))
+        print('Record:       ', ', '.join([ ['Loss', 'Win'][int(w)] for w in wins]))
+
+    return games
+
+if __name__ == '__main__':
+    """
+    The main function called when pacman.py is run
+    from the command line:
+
+    > python pacman.py
+
+    See the usage string for more details.
+
+    > python pacman.py --help
+    """
+    args = readCommand( sys.argv[1:] ) # Get game components based on input
+    runGames( **args )

tracking/bustersAgents.py

@@ -0,0 +1,153 @@
+# bustersAgents.py
+# ----------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+import util
+from util import raiseNotDefined
+from game import Agent
+from game import Directions
+from keyboardAgents import KeyboardAgent
+import inference
+import busters
+
+class NullGraphics:
+    "Placeholder for graphics"
+    def initialize(self, state, isBlue = False):
+        pass
+    def update(self, state):
+        pass
+    def pause(self):
+        pass
+    def draw(self, state):
+        pass
+    def updateDistributions(self, dist):
+        pass
+    def finish(self):
+        pass
+
+class KeyboardInference(inference.InferenceModule):
+    """
+    Basic inference module for use with the keyboard.
+    """
+    def initializeUniformly(self, gameState):
+        "Begin with a uniform distribution over ghost positions."
+        self.beliefs = util.Counter()
+        for p in self.legalPositions: self.beliefs[p] = 1.0
+        self.beliefs.normalize()
+
+    def observeUpdate(self, observation, gameState):
+        noisyDistance = observation
+        pacmanPosition = gameState.getPacmanPosition()
+        allPossible = util.Counter()
+        for p in self.legalPositions:
+            trueDistance = util.manhattanDistance(p, pacmanPosition)
+            if noisyDistance != None and \
+                    busters.getObservationProbability(noisyDistance, trueDistance) > 0:
+                allPossible[p] = 1.0
+        allPossible.normalize()
+        self.beliefs = allPossible
+
+    def elapseTime(self, gameState):
+        pass
+
+    def getBeliefDistribution(self):
+        return self.beliefs
+
+
+class BustersAgent:
+    "An agent that tracks and displays its beliefs about ghost positions."
+
+    def __init__( self, index = 0, inference = "ExactInference", ghostAgents = None, observeEnable = True, elapseTimeEnable = True):
+        try:
+            inferenceType = util.lookup(inference, globals())
+        except Exception:
+            inferenceType = util.lookup('inference.' + inference, globals())
+        self.inferenceModules = [inferenceType(a) for a in ghostAgents]
+        self.observeEnable = observeEnable
+        self.elapseTimeEnable = elapseTimeEnable
+
+    def registerInitialState(self, gameState):
+        "Initializes beliefs and inference modules"
+        import __main__
+        self.display = __main__._display
+        for inference in self.inferenceModules:
+            inference.initialize(gameState)
+        self.ghostBeliefs = [inf.getBeliefDistribution() for inf in self.inferenceModules]
+        self.firstMove = True
+
+    def observationFunction(self, gameState):
+        "Removes the ghost states from the gameState"
+        agents = gameState.data.agentStates
+        gameState.data.agentStates = [agents[0]] + [None for i in range(1, len(agents))]
+        return gameState
+
+    def getAction(self, gameState):
+        "Updates beliefs, then chooses an action based on updated beliefs."
+        for index, inf in enumerate(self.inferenceModules):
+            if not self.firstMove and self.elapseTimeEnable:
+                inf.elapseTime(gameState)
+            self.firstMove = False
+            if self.observeEnable:
+                inf.observe(gameState)
+            self.ghostBeliefs[index] = inf.getBeliefDistribution()
+        self.display.updateDistributions(self.ghostBeliefs)
+        return self.chooseAction(gameState)
+
+    def chooseAction(self, gameState):
+        "By default, a BustersAgent just stops.  This should be overridden."
+        return Directions.STOP
+
+class BustersKeyboardAgent(BustersAgent, KeyboardAgent):
+    "An agent controlled by the keyboard that displays beliefs about ghost positions."
+
+    def __init__(self, index = 0, inference = "KeyboardInference", ghostAgents = None):
+        KeyboardAgent.__init__(self, index)
+        BustersAgent.__init__(self, index, inference, ghostAgents)
+
+    def getAction(self, gameState):
+        return BustersAgent.getAction(self, gameState)
+
+    def chooseAction(self, gameState):
+        return KeyboardAgent.getAction(self, gameState)
+
+from distanceCalculator import Distancer
+from game import Actions
+from game import Directions
+
+class GreedyBustersAgent(BustersAgent):
+    "An agent that charges the closest ghost."
+
+    def registerInitialState(self, gameState: busters.GameState):
+        "Pre-computes the distance between every two points."
+        BustersAgent.registerInitialState(self, gameState)
+        self.distancer = Distancer(gameState.data.layout, False)
+    
+    ########### ########### ###########
+    ########### QUESTION 8  ###########
+    ########### ########### ###########
+
+    def chooseAction(self, gameState: busters.GameState):
+        """
+        First computes the most likely position of each ghost that has
+        not yet been captured, then chooses an action that brings
+        Pacman closest to the closest ghost (according to mazeDistance!).
+        """
+        pacmanPosition = gameState.getPacmanPosition()
+        legal = [a for a in gameState.getLegalPacmanActions()]
+        livingGhosts = gameState.getLivingGhosts()
+        livingGhostPositionDistributions = \
+            [beliefs for i, beliefs in enumerate(self.ghostBeliefs)
+             if livingGhosts[i+1]]
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"

tracking/bustersGhostAgents.py

@@ -0,0 +1,60 @@
+# bustersGhostAgents.py
+# ---------------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+import ghostAgents
+from game import Directions
+from game import Actions
+from util import manhattanDistance
+import util
+
+class StationaryGhost( ghostAgents.GhostAgent ):
+    def getDistribution( self, state ):
+        dist = util.Counter()
+        dist[Directions.STOP] = 1.0
+        return dist
+
+class DispersingGhost( ghostAgents.GhostAgent ):
+    "Chooses an action that distances the ghost from the other ghosts with probability spreadProb."
+    def __init__( self, index, spreadProb=0.5):
+        self.index = index
+        self.spreadProb = spreadProb
+
+    def getDistribution( self, state ):
+        ghostState = state.getGhostState( self.index )
+        legalActions = state.getLegalActions( self.index )
+        pos = state.getGhostPosition( self.index )
+        isScared = ghostState.scaredTimer > 0
+
+        speed = 1
+        if isScared: speed = 0.5
+        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
+        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
+
+        # get other ghost positions
+        others = [i for i in range(1,state.getNumAgents()) if i != self.index]
+        for a in others: assert state.getGhostState(a) != None, "Ghost position unspecified in state!"
+        otherGhostPositions = [state.getGhostPosition(a) for a in others if state.getGhostPosition(a)[1] > 1]
+
+        # for each action, get the sum of inverse squared distances to the other ghosts
+        sumOfDistances = []
+        for pos in newPositions:
+            sumOfDistances.append( sum([(1+manhattanDistance(pos, g))**(-2) for g in otherGhostPositions]) )
+
+        bestDistance = min(sumOfDistances)
+        numBest = [bestDistance == dist for dist in sumOfDistances].count(True)
+        distribution = util.Counter()
+        for action, distance in zip(legalActions, sumOfDistances):
+            if distance == bestDistance: distribution[action] += self.spreadProb / numBest
+            distribution[action] += (1 - self.spreadProb) / len(legalActions)
+        return distribution

tracking/distanceCalculator.py

@@ -0,0 +1,191 @@
+# distanceCalculator.py
+# ---------------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+"""
+This file contains a Distancer object which computes and
+caches the shortest path between any two points in the maze. It
+returns a Manhattan distance between two points if the maze distance
+has not yet been calculated.
+
+Example:
+distancer = Distancer(gameState.data.layout)
+distancer.getDistance( (1,1), (10,10) )
+
+The Distancer object also serves as an example of sharing data
+safely among agents via a global dictionary (distanceMap),
+and performing asynchronous computation via threads. These
+examples may help you in designing your own objects, but you
+shouldn't need to modify the Distancer code in order to use its
+distances.
+"""
+
+import threading, sys, time, random
+
+class Distancer:
+  def __init__(self, layout, background=True, default=10000):
+    """
+    Initialize with Distancer(layout).  Changing default is unnecessary.
+
+    This will start computing maze distances in the background and use them
+    as soon as they are ready.  In the meantime, it returns manhattan distance.
+
+    To compute all maze distances on initialization, set background=False
+    """
+    self._distances = None
+    self.default = default
+
+    # Start computing distances in the background; when the dc finishes,
+    # it will fill in self._distances for us.
+    dc = DistanceCalculator()
+    dc.setAttr(layout, self)
+    dc.setDaemon(True)
+    if background:
+      dc.start()
+    else:
+      dc.run()
+
+  def getDistance(self, pos1, pos2):
+    """
+    The getDistance function is the only one you'll need after you create the object.
+    """
+    if self._distances == None:
+      return manhattanDistance(pos1, pos2)
+    if isInt(pos1) and isInt(pos2):
+      return self.getDistanceOnGrid(pos1, pos2)
+    pos1Grids = getGrids2D(pos1)
+    pos2Grids = getGrids2D(pos2)
+    bestDistance = self.default
+    for pos1Snap, snap1Distance in pos1Grids:
+      for pos2Snap, snap2Distance in pos2Grids:
+        gridDistance = self.getDistanceOnGrid(pos1Snap, pos2Snap)
+        distance = gridDistance + snap1Distance + snap2Distance
+        if bestDistance > distance:
+          bestDistance = distance
+    return bestDistance
+
+  def getDistanceOnGrid(self, pos1, pos2):
+    key = (pos1, pos2)
+    if key in self._distances:
+      return self._distances[key]
+    else:
+      raise Exception("Positions not in grid: " + str(key))
+
+  def isReadyForMazeDistance(self):
+    return self._distances != None
+
+def manhattanDistance(x, y ):
+  return abs( x[0] - y[0] ) + abs( x[1] - y[1] )
+
+def isInt(pos):
+  x, y = pos
+  return x == int(x) and y == int(y)
+
+def getGrids2D(pos):
+  grids = []
+  for x, xDistance in getGrids1D(pos[0]):
+    for y, yDistance in getGrids1D(pos[1]):
+      grids.append(((x, y), xDistance + yDistance))
+  return grids
+
+def getGrids1D(x):
+  intX = int(x)
+  if x == int(x):
+    return [(x, 0)]
+  return [(intX, x-intX), (intX+1, intX+1-x)]
+
+##########################################
+# MACHINERY FOR COMPUTING MAZE DISTANCES #
+##########################################
+
+distanceMap = {}
+distanceMapSemaphore = threading.Semaphore(1)
+distanceThread = None
+
+def waitOnDistanceCalculator(t):
+  global distanceThread
+  if distanceThread != None:
+    time.sleep(t)
+
+class DistanceCalculator(threading.Thread):
+  def setAttr(self, layout, distancer, default = 10000):
+    self.layout = layout
+    self.distancer = distancer
+    self.default = default
+
+  def run(self):
+    global distanceMap, distanceThread
+    distanceMapSemaphore.acquire()
+
+    if self.layout.walls not in distanceMap:
+      if distanceThread != None: raise Exception('Multiple distance threads')
+      distanceThread = self
+
+      distances = computeDistances(self.layout)
+      print('[Distancer]: Switching to maze distances',file=sys.stdout)
+
+      distanceMap[self.layout.walls] = distances
+      distanceThread = None
+    else:
+      distances = distanceMap[self.layout.walls]
+
+    distanceMapSemaphore.release()
+    self.distancer._distances = distances
+
+def computeDistances(layout):
+    distances = {}
+    allNodes = layout.walls.asList(False)
+    for source in allNodes:
+        dist = {}
+        closed = {}
+        for node in allNodes:
+            dist[node] = 1000000000
+        import util
+        queue = util.PriorityQueue()
+        queue.push(source, 0)
+        dist[source] = 0
+        while not queue.isEmpty():
+            node = queue.pop()
+            if node in closed:
+                continue
+            closed[node] = True
+            nodeDist = dist[node]
+            adjacent = []
+            x, y = node
+            if not layout.isWall((x,y+1)):
+                adjacent.append((x,y+1))
+            if not layout.isWall((x,y-1)):
+                adjacent.append((x,y-1) )
+            if not layout.isWall((x+1,y)):
+                adjacent.append((x+1,y) )
+            if not layout.isWall((x-1,y)):
+                adjacent.append((x-1,y))
+            for other in adjacent:
+                if not other in dist:
+                    continue
+                oldDist = dist[other]
+                newDist = nodeDist+1
+                if newDist < oldDist:
+                    dist[other] = newDist
+                    queue.push(other, newDist)
+        for target in allNodes:
+            distances[(target, source)] = dist[target]
+    return distances
+
+
+def getDistanceOnGrid(distances, pos1, pos2):
+    key = (pos1, pos2)
+    if key in distances:
+      return distances[key]
+    return 100000
+

tracking/factorOperations.py

@@ -0,0 +1,162 @@
+# factorOperations.py
+# -------------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+from typing import List
+from bayesNet import Factor
+import functools
+from util import raiseNotDefined
+
+def joinFactorsByVariableWithCallTracking(callTrackingList=None):
+
+
+    def joinFactorsByVariable(factors: List[Factor], joinVariable: str):
+        """
+        Input factors is a list of factors.
+        Input joinVariable is the variable to join on.
+
+        This function performs a check that the variable that is being joined on 
+        appears as an unconditioned variable in only one of the input factors.
+
+        Then, it calls your joinFactors on all of the factors in factors that 
+        contain that variable.
+
+        Returns a tuple of 
+        (factors not joined, resulting factor from joinFactors)
+        """
+
+        if not (callTrackingList is None):
+            callTrackingList.append(('join', joinVariable))
+
+        currentFactorsToJoin =    [factor for factor in factors if joinVariable in factor.variablesSet()]
+        currentFactorsNotToJoin = [factor for factor in factors if joinVariable not in factor.variablesSet()]
+
+        # typecheck portion
+        numVariableOnLeft = len([factor for factor in currentFactorsToJoin if joinVariable in factor.unconditionedVariables()])
+        if numVariableOnLeft > 1:
+            print("Factor failed joinFactorsByVariable typecheck: ", factor)
+            raise ValueError("The joinBy variable can only appear in one factor as an \nunconditioned variable. \n" +  
+                               "joinVariable: " + str(joinVariable) + "\n" +
+                               ", ".join(map(str, [factor.unconditionedVariables() for factor in currentFactorsToJoin])))
+        
+        joinedFactor = joinFactors(currentFactorsToJoin)
+        return currentFactorsNotToJoin, joinedFactor
+
+    return joinFactorsByVariable
+
+joinFactorsByVariable = joinFactorsByVariableWithCallTracking()
+
+########### ########### ###########
+########### QUESTION 2  ###########
+########### ########### ###########
+
+def joinFactors(factors: List[Factor]):
+    """
+    Input factors is a list of factors.  
+    
+    You should calculate the set of unconditioned variables and conditioned 
+    variables for the join of those factors.
+
+    Return a new factor that has those variables and whose probability entries 
+    are product of the corresponding rows of the input factors.
+
+    You may assume that the variableDomainsDict for all the input 
+    factors are the same, since they come from the same BayesNet.
+
+    joinFactors will only allow unconditionedVariables to appear in 
+    one input factor (so their join is well defined).
+
+    Hint: Factor methods that take an assignmentDict as input 
+    (such as getProbability and setProbability) can handle 
+    assignmentDicts that assign more variables than are in that factor.
+
+    Useful functions:
+    Factor.getAllPossibleAssignmentDicts
+    Factor.getProbability
+    Factor.setProbability
+    Factor.unconditionedVariables
+    Factor.conditionedVariables
+    Factor.variableDomainsDict
+    """
+
+    # typecheck portion
+    setsOfUnconditioned = [set(factor.unconditionedVariables()) for factor in factors]
+    if len(factors) > 1:
+        intersect = functools.reduce(lambda x, y: x & y, setsOfUnconditioned)
+        if len(intersect) > 0:
+            print("Factor failed joinFactors typecheck: ", factor)
+            raise ValueError("unconditionedVariables can only appear in one factor. \n"
+                    + "unconditionedVariables: " + str(intersect) + 
+                    "\nappear in more than one input factor.\n" + 
+                    "Input factors: \n" +
+                    "\n".join(map(str, factors)))
+
+
+    "*** YOUR CODE HERE ***"
+    raiseNotDefined()
+    "*** END YOUR CODE HERE ***"
+
+########### ########### ###########
+########### QUESTION 3  ###########
+########### ########### ###########
+
+def eliminateWithCallTracking(callTrackingList=None):
+
+    def eliminate(factor: Factor, eliminationVariable: str):
+        """
+        Input factor is a single factor.
+        Input eliminationVariable is the variable to eliminate from factor.
+        eliminationVariable must be an unconditioned variable in factor.
+        
+        You should calculate the set of unconditioned variables and conditioned 
+        variables for the factor obtained by eliminating the variable
+        eliminationVariable.
+
+        Return a new factor where all of the rows mentioning
+        eliminationVariable are summed with rows that match
+        assignments on the other variables.
+
+        Useful functions:
+        Factor.getAllPossibleAssignmentDicts
+        Factor.getProbability
+        Factor.setProbability
+        Factor.unconditionedVariables
+        Factor.conditionedVariables
+        Factor.variableDomainsDict
+        """
+        # autograder tracking -- don't remove
+        if not (callTrackingList is None):
+            callTrackingList.append(('eliminate', eliminationVariable))
+
+        # typecheck portion
+        if eliminationVariable not in factor.unconditionedVariables():
+            print("Factor failed eliminate typecheck: ", factor)
+            raise ValueError("Elimination variable is not an unconditioned variable " \
+                            + "in this factor\n" + 
+                            "eliminationVariable: " + str(eliminationVariable) + \
+                            "\nunconditionedVariables:" + str(factor.unconditionedVariables()))
+        
+        if len(factor.unconditionedVariables()) == 1:
+            print("Factor failed eliminate typecheck: ", factor)
+            raise ValueError("Factor has only one unconditioned variable, so you " \
+                    + "can't eliminate \nthat variable.\n" + \
+                    "eliminationVariable:" + str(eliminationVariable) + "\n" +\
+                    "unconditionedVariables: " + str(factor.unconditionedVariables()))
+
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+
+    return eliminate
+
+eliminate = eliminateWithCallTracking()
+

tracking/game.py

@@ -0,0 +1,729 @@
+# game.py
+# -------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+# game.py
+# -------
+# Licensing Information: Please do not distribute or publish solutions to this
+# project. You are free to use and extend these projects for educational
+# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
+# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html
+
+from util import *
+import time, os
+import traceback
+import sys
+
+#######################
+# Parts worth reading #
+#######################
+
+class Agent:
+    """
+    An agent must define a getAction method, but may also define the
+    following methods which will be called if they exist:
+
+    def registerInitialState(self, state): # inspects the starting state
+    """
+    def __init__(self, index=0):
+        self.index = index
+
+    def getAction(self, state):
+        """
+        The Agent will receive a GameState (from either {pacman, capture, sonar}.py) and
+        must return an action from Directions.{North, South, East, West, Stop}
+        """
+        raiseNotDefined()
+
+class Directions:
+    NORTH = 'North'
+    SOUTH = 'South'
+    EAST = 'East'
+    WEST = 'West'
+    STOP = 'Stop'
+
+    LEFT =       {NORTH: WEST,
+                   SOUTH: EAST,
+                   EAST:  NORTH,
+                   WEST:  SOUTH,
+                   STOP:  STOP}
+
+    RIGHT =      dict([(y,x) for x, y in LEFT.items()])
+
+    REVERSE = {NORTH: SOUTH,
+               SOUTH: NORTH,
+               EAST: WEST,
+               WEST: EAST,
+               STOP: STOP}
+
+class Configuration:
+    """
+    A Configuration holds the (x,y) coordinate of a character, along with its
+    traveling direction.
+
+    The convention for positions, like a graph, is that (0,0) is the lower left corner, x increases
+    horizontally and y increases vertically.  Therefore, north is the direction of increasing y, or (0,1).
+    """
+
+    def __init__(self, pos, direction):
+        self.pos = pos
+        self.direction = direction
+
+    def getPosition(self):
+        return (self.pos)
+
+    def getDirection(self):
+        return self.direction
+
+    def isInteger(self):
+        x,y = self.pos
+        return x == int(x) and y == int(y)
+
+    def __eq__(self, other):
+        if other == None: return False
+        return (self.pos == other.pos and self.direction == other.direction)
+
+    def __hash__(self):
+        x = hash(self.pos)
+        y = hash(self.direction)
+        return hash(x + 13 * y)
+
+    def __str__(self):
+        return "(x,y)="+str(self.pos)+", "+str(self.direction)
+
+    def generateSuccessor(self, vector):
+        """
+        Generates a new configuration reached by translating the current
+        configuration by the action vector.  This is a low-level call and does
+        not attempt to respect the legality of the movement.
+
+        Actions are movement vectors.
+        """
+        x, y= self.pos
+        dx, dy = vector
+        direction = Actions.vectorToDirection(vector)
+        if direction == Directions.STOP:
+            direction = self.direction # There is no stop direction
+        return Configuration((x + dx, y+dy), direction)
+
+class AgentState:
+    """
+    AgentStates hold the state of an agent (configuration, speed, scared, etc).
+    """
+
+    def __init__( self, startConfiguration, isPacman ):
+        self.start = startConfiguration
+        self.configuration = startConfiguration
+        self.isPacman = isPacman
+        self.scaredTimer = 0
+        self.numCarrying = 0
+        self.numReturned = 0
+
+    def __str__( self ):
+        if self.isPacman:
+            return "Pacman: " + str( self.configuration )
+        else:
+            return "Ghost: " + str( self.configuration )
+
+    def __eq__( self, other ):
+        if other == None:
+            return False
+        return self.configuration == other.configuration and self.scaredTimer == other.scaredTimer
+
+    def __hash__(self):
+        return hash(hash(self.configuration) + 13 * hash(self.scaredTimer))
+
+    def copy( self ):
+        state = AgentState( self.start, self.isPacman )
+        state.configuration = self.configuration
+        state.scaredTimer = self.scaredTimer
+        state.numCarrying = self.numCarrying
+        state.numReturned = self.numReturned
+        return state
+
+    def getPosition(self):
+        if self.configuration == None: return None
+        return self.configuration.getPosition()
+
+    def getDirection(self):
+        return self.configuration.getDirection()
+
+class Grid:
+    """
+    A 2-dimensional array of objects backed by a list of lists.  Data is accessed
+    via grid[x][y] where (x,y) are positions on a Pacman map with x horizontal,
+    y vertical and the origin (0,0) in the bottom left corner.
+
+    The __str__ method constructs an output that is oriented like a pacman board.
+    """
+    def __init__(self, width, height, initialValue=False, bitRepresentation=None):
+        if initialValue not in [False, True]: raise Exception('Grids can only contain booleans')
+        self.CELLS_PER_INT = 30
+
+        self.width = width
+        self.height = height
+        self.data = [[initialValue for y in range(height)] for x in range(width)]
+        if bitRepresentation:
+            self._unpackBits(bitRepresentation)
+
+    def __getitem__(self, i):
+        return self.data[i]
+
+    def __setitem__(self, key, item):
+        self.data[key] = item
+
+    def __str__(self):
+        out = [[str(self.data[x][y])[0] for x in range(self.width)] for y in range(self.height)]
+        out.reverse()
+        return '\n'.join([''.join(x) for x in out])
+
+    def __eq__(self, other):
+        if other == None: return False
+        return self.data == other.data
+
+    def __hash__(self):
+        # return hash(str(self))
+        base = 1
+        h = 0
+        for l in self.data:
+            for i in l:
+                if i:
+                    h += base
+                base *= 2
+        return hash(h)
+
+    def copy(self):
+        g = Grid(self.width, self.height)
+        g.data = [x[:] for x in self.data]
+        return g
+
+    def deepCopy(self):
+        return self.copy()
+
+    def shallowCopy(self):
+        g = Grid(self.width, self.height)
+        g.data = self.data
+        return g
+
+    def count(self, item =True ):
+        return sum([x.count(item) for x in self.data])
+
+    def asList(self, key = True):
+        list = []
+        for x in range(self.width):
+            for y in range(self.height):
+                if self[x][y] == key: list.append( (x,y) )
+        return list
+
+    def packBits(self):
+        """
+        Returns an efficient int list representation
+
+        (width, height, bitPackedInts...)
+        """
+        bits = [self.width, self.height]
+        currentInt = 0
+        for i in range(self.height * self.width):
+            bit = self.CELLS_PER_INT - (i % self.CELLS_PER_INT) - 1
+            x, y = self._cellIndexToPosition(i)
+            if self[x][y]:
+                currentInt += 2 ** bit
+            if (i + 1) % self.CELLS_PER_INT == 0:
+                bits.append(currentInt)
+                currentInt = 0
+        bits.append(currentInt)
+        return tuple(bits)
+
+    def _cellIndexToPosition(self, index):
+        x = index / self.height
+        y = index % self.height
+        return x, y
+
+    def _unpackBits(self, bits):
+        """
+        Fills in data from a bit-level representation
+        """
+        cell = 0
+        for packed in bits:
+            for bit in self._unpackInt(packed, self.CELLS_PER_INT):
+                if cell == self.width * self.height: break
+                x, y = self._cellIndexToPosition(cell)
+                self[x][y] = bit
+                cell += 1
+
+    def _unpackInt(self, packed, size):
+        bools = []
+        if packed < 0: raise ValueError("must be a positive integer")
+        for i in range(size):
+            n = 2 ** (self.CELLS_PER_INT - i - 1)
+            if packed >= n:
+                bools.append(True)
+                packed -= n
+            else:
+                bools.append(False)
+        return bools
+
+def reconstituteGrid(bitRep):
+    if type(bitRep) is not type((1,2)):
+        return bitRep
+    width, height = bitRep[:2]
+    return Grid(width, height, bitRepresentation= bitRep[2:])
+
+####################################
+# Parts you shouldn't have to read #
+####################################
+
+class Actions:
+    """
+    A collection of static methods for manipulating move actions.
+    """
+    # Directions
+    _directions = {Directions.NORTH: (0, 1),
+                   Directions.SOUTH: (0, -1),
+                   Directions.EAST:  (1, 0),
+                   Directions.WEST:  (-1, 0),
+                   Directions.STOP:  (0, 0)}
+
+    _directionsAsList = _directions.items()
+
+    TOLERANCE = .001
+
+    def reverseDirection(action):
+        if action == Directions.NORTH:
+            return Directions.SOUTH
+        if action == Directions.SOUTH:
+            return Directions.NORTH
+        if action == Directions.EAST:
+            return Directions.WEST
+        if action == Directions.WEST:
+            return Directions.EAST
+        return action
+    reverseDirection = staticmethod(reverseDirection)
+
+    def vectorToDirection(vector):
+        dx, dy = vector
+        if dy > 0:
+            return Directions.NORTH
+        if dy < 0:
+            return Directions.SOUTH
+        if dx < 0:
+            return Directions.WEST
+        if dx > 0:
+            return Directions.EAST
+        return Directions.STOP
+    vectorToDirection = staticmethod(vectorToDirection)
+
+    def directionToVector(direction, speed = 1.0):
+        dx, dy =  Actions._directions[direction]
+        return (dx * speed, dy * speed)
+    directionToVector = staticmethod(directionToVector)
+
+    def getPossibleActions(config, walls):
+        possible = []
+        x, y = config.pos
+        x_int, y_int = int(x + 0.5), int(y + 0.5)
+
+        # In between grid points, all agents must continue straight
+        if (abs(x - x_int) + abs(y - y_int)  > Actions.TOLERANCE):
+            return [config.getDirection()]
+
+        for dir, vec in Actions._directionsAsList:
+            dx, dy = vec
+            next_y = y_int + dy
+            next_x = x_int + dx
+            if not walls[next_x][next_y]: possible.append(dir)
+
+        return possible
+
+    getPossibleActions = staticmethod(getPossibleActions)
+
+    def getLegalNeighbors(position, walls):
+        x,y = position
+        x_int, y_int = int(x + 0.5), int(y + 0.5)
+        neighbors = []
+        for dir, vec in Actions._directionsAsList:
+            dx, dy = vec
+            next_x = x_int + dx
+            if next_x < 0 or next_x == walls.width: continue
+            next_y = y_int + dy
+            if next_y < 0 or next_y == walls.height: continue
+            if not walls[next_x][next_y]: neighbors.append((next_x, next_y))
+        return neighbors
+    getLegalNeighbors = staticmethod(getLegalNeighbors)
+
+    def getSuccessor(position, action):
+        dx, dy = Actions.directionToVector(action)
+        x, y = position
+        return (x + dx, y + dy)
+    getSuccessor = staticmethod(getSuccessor)
+
+class GameStateData:
+    """
+
+    """
+    def __init__( self, prevState = None ):
+        """
+        Generates a new data packet by copying information from its predecessor.
+        """
+        if prevState != None:
+            self.food = prevState.food.shallowCopy()
+            self.capsules = prevState.capsules[:]
+            self.agentStates = self.copyAgentStates( prevState.agentStates )
+            self.layout = prevState.layout
+            self._eaten = prevState._eaten
+            self.score = prevState.score
+
+        self._foodEaten = None
+        self._foodAdded = None
+        self._capsuleEaten = None
+        self._agentMoved = None
+        self._lose = False
+        self._win = False
+        self.scoreChange = 0
+
+    def deepCopy( self ):
+        state = GameStateData( self )
+        state.food = self.food.deepCopy()
+        state.layout = self.layout.deepCopy()
+        state._agentMoved = self._agentMoved
+        state._foodEaten = self._foodEaten
+        state._foodAdded = self._foodAdded
+        state._capsuleEaten = self._capsuleEaten
+        return state
+
+    def copyAgentStates( self, agentStates ):
+        copiedStates = []
+        for agentState in agentStates:
+            copiedStates.append( agentState.copy() )
+        return copiedStates
+
+    def __eq__( self, other ):
+        """
+        Allows two states to be compared.
+        """
+        if other == None: return False
+        # TODO Check for type of other
+        if not self.agentStates == other.agentStates: return False
+        if not self.food == other.food: return False
+        if not self.capsules == other.capsules: return False
+        if not self.score == other.score: return False
+        return True
+
+    def __hash__( self ):
+        """
+        Allows states to be keys of dictionaries.
+        """
+        for i, state in enumerate( self.agentStates ):
+            try:
+                int(hash(state))
+            except TypeError as e:
+                print(e)
+                #hash(state)
+        return int((hash(tuple(self.agentStates)) + 13*hash(self.food) + 113* hash(tuple(self.capsules)) + 7 * hash(self.score)) % 1048575 )
+
+    def __str__( self ):
+        width, height = self.layout.width, self.layout.height
+        map = Grid(width, height)
+        if type(self.food) == type((1,2)):
+            self.food = reconstituteGrid(self.food)
+        for x in range(width):
+            for y in range(height):
+                food, walls = self.food, self.layout.walls
+                map[x][y] = self._foodWallStr(food[x][y], walls[x][y])
+
+        for agentState in self.agentStates:
+            if agentState == None: continue
+            if agentState.configuration == None: continue
+            x,y = [int( i ) for i in nearestPoint( agentState.configuration.pos )]
+            agent_dir = agentState.configuration.direction
+            if agentState.isPacman:
+                map[x][y] = self._pacStr( agent_dir )
+            else:
+                map[x][y] = self._ghostStr( agent_dir )
+
+        for x, y in self.capsules:
+            map[x][y] = 'o'
+
+        return str(map) + ("\nScore: %d\n" % self.score)
+
+    def _foodWallStr( self, hasFood, hasWall ):
+        if hasFood:
+            return '.'
+        elif hasWall:
+            return '%'
+        else:
+            return ' '
+
+    def _pacStr( self, dir ):
+        if dir == Directions.NORTH:
+            return 'v'
+        if dir == Directions.SOUTH:
+            return '^'
+        if dir == Directions.WEST:
+            return '>'
+        return '<'
+
+    def _ghostStr( self, dir ):
+        return 'G'
+        if dir == Directions.NORTH:
+            return 'M'
+        if dir == Directions.SOUTH:
+            return 'W'
+        if dir == Directions.WEST:
+            return '3'
+        return 'E'
+
+    def initialize( self, layout, numGhostAgents ):
+        """
+        Creates an initial game state from a layout array (see layout.py).
+        """
+        self.food = layout.food.copy()
+        #self.capsules = []
+        self.capsules = layout.capsules[:]
+        self.layout = layout
+        self.score = 0
+        self.scoreChange = 0
+
+        self.agentStates = []
+        numGhosts = 0
+        for isPacman, pos in layout.agentPositions:
+            if not isPacman:
+                if numGhosts == numGhostAgents: continue # Max ghosts reached already
+                else: numGhosts += 1
+            self.agentStates.append( AgentState( Configuration( pos, Directions.STOP), isPacman) )
+        self._eaten = [False for a in self.agentStates]
+
+try:
+    import boinc
+    _BOINC_ENABLED = True
+except:
+    _BOINC_ENABLED = False
+
+class Game:
+    """
+    The Game manages the control flow, soliciting actions from agents.
+    """
+
+    def __init__( self, agents, display, rules, startingIndex=0, muteAgents=False, catchExceptions=False ):
+        self.agentCrashed = False
+        self.agents = agents
+        self.display = display
+        self.rules = rules
+        self.startingIndex = startingIndex
+        self.gameOver = False
+        self.muteAgents = muteAgents
+        self.catchExceptions = catchExceptions
+        self.moveHistory = []
+        self.totalAgentTimes = [0 for agent in agents]
+        self.totalAgentTimeWarnings = [0 for agent in agents]
+        self.agentTimeout = False
+        import io
+        self.agentOutput = [io.StringIO() for agent in agents]
+
+    def getProgress(self):
+        if self.gameOver:
+            return 1.0
+        else:
+            return self.rules.getProgress(self)
+
+    def _agentCrash( self, agentIndex, quiet=False):
+        "Helper method for handling agent crashes"
+        if not quiet: traceback.print_exc()
+        self.gameOver = True
+        self.agentCrashed = True
+        self.rules.agentCrash(self, agentIndex)
+
+    OLD_STDOUT = None
+    OLD_STDERR = None
+
+    def mute(self, agentIndex):
+        if not self.muteAgents: return
+        global OLD_STDOUT, OLD_STDERR
+        import io
+        OLD_STDOUT = sys.stdout
+        OLD_STDERR = sys.stderr
+        sys.stdout = self.agentOutput[agentIndex]
+        sys.stderr = self.agentOutput[agentIndex]
+
+    def unmute(self):
+        if not self.muteAgents: return
+        global OLD_STDOUT, OLD_STDERR
+        # Revert stdout/stderr to originals
+        sys.stdout = OLD_STDOUT
+        sys.stderr = OLD_STDERR
+
+
+    def run( self ):
+        """
+        Main control loop for game play.
+        """
+        self.display.initialize(self.state.data)
+        self.numMoves = 0
+
+        ###self.display.initialize(self.state.makeObservation(1).data)
+        # inform learning agents of the game start
+        for i in range(len(self.agents)):
+            agent = self.agents[i]
+            if not agent:
+                self.mute(i)
+                # this is a null agent, meaning it failed to load
+                # the other team wins
+                print("Agent %d failed to load" % i,file=sys.stderr)
+                self.unmute()
+                self._agentCrash(i, quiet=True)
+                return
+            if ("registerInitialState" in dir(agent)):
+                self.mute(i)
+                if self.catchExceptions:
+                    try:
+                        timed_func = TimeoutFunction(agent.registerInitialState, int(self.rules.getMaxStartupTime(i)))
+                        try:
+                            start_time = time.time()
+                            timed_func(self.state.deepCopy())
+                            time_taken = time.time() - start_time
+                            self.totalAgentTimes[i] += time_taken
+                        except TimeoutFunctionException:
+                            print("Agent %d ran out of time on startup!" % i, file=sys.stderr)
+                            self.unmute()
+                            self.agentTimeout = True
+                            self._agentCrash(i, quiet=True)
+                            return
+                    except Exception as data:
+                        self._agentCrash(i, quiet=False)
+                        self.unmute()
+                        return
+                else:
+                    agent.registerInitialState(self.state.deepCopy())
+                ## TODO: could this exceed the total time
+                self.unmute()
+
+        agentIndex = self.startingIndex
+        numAgents = len( self.agents )
+
+        while not self.gameOver:
+            # Fetch the next agent
+            agent = self.agents[agentIndex]
+            move_time = 0
+            skip_action = False
+            # Generate an observation of the state
+            if 'observationFunction' in dir( agent ):
+                self.mute(agentIndex)
+                if self.catchExceptions:
+                    try:
+                        timed_func = TimeoutFunction(agent.observationFunction, int(self.rules.getMoveTimeout(agentIndex)))
+                        try:
+                            start_time = time.time()
+                            observation = timed_func(self.state.deepCopy())
+                        except TimeoutFunctionException:
+                            skip_action = True
+                        move_time += time.time() - start_time
+                        self.unmute()
+                    except Exception as data:
+                        self._agentCrash(agentIndex, quiet=False)
+                        self.unmute()
+                        return
+                else:
+                    observation = agent.observationFunction(self.state.deepCopy())
+                self.unmute()
+            else:
+                observation = self.state.deepCopy()
+
+            # Solicit an action
+            action = None
+            self.mute(agentIndex)
+            if self.catchExceptions:
+                try:
+                    timed_func = TimeoutFunction(agent.getAction, int(self.rules.getMoveTimeout(agentIndex)) - int(move_time))
+                    try:
+                        start_time = time.time()
+                        if skip_action:
+                            raise TimeoutFunctionException()
+                        action = timed_func( observation )
+                    except TimeoutFunctionException:
+                        print("Agent %d timed out on a single move!" % agentIndex, file=sys.stderr)
+                        self.agentTimeout = True
+                        self._agentCrash(agentIndex, quiet=True)
+                        self.unmute()
+                        return
+
+                    move_time += time.time() - start_time
+
+                    if move_time > self.rules.getMoveWarningTime(agentIndex):
+                        self.totalAgentTimeWarnings[agentIndex] += 1
+                        print("Agent %d took too long to make a move! This is warning %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex]), file=sys.stderr)
+                        if self.totalAgentTimeWarnings[agentIndex] > self.rules.getMaxTimeWarnings(agentIndex):
+                            print("Agent %d exceeded the maximum number of warnings: %d" % (agentIndex, self.totalAgentTimeWarnings[agentIndex]), file=sys.stderr)
+                            self.agentTimeout = True
+                            self._agentCrash(agentIndex, quiet=True)
+                            self.unmute()
+                            return
+
+                    self.totalAgentTimes[agentIndex] += move_time
+                    #print "Agent: %d, time: %f, total: %f" % (agentIndex, move_time, self.totalAgentTimes[agentIndex])
+                    if self.totalAgentTimes[agentIndex] > self.rules.getMaxTotalTime(agentIndex):
+                        print("Agent %d ran out of time! (time: %1.2f)" % (agentIndex, self.totalAgentTimes[agentIndex]), file=sys.stderr)
+                        self.agentTimeout = True
+                        self._agentCrash(agentIndex, quiet=True)
+                        self.unmute()
+                        return
+                    self.unmute()
+                except Exception as data:
+                    self._agentCrash(agentIndex)
+                    self.unmute()
+                    return
+            else:
+                action = agent.getAction(observation)
+            self.unmute()
+
+            # Execute the action
+            self.moveHistory.append( (agentIndex, action) )
+            if self.catchExceptions:
+                try:
+                    self.state = self.state.getResult( agentIndex, action )
+                except Exception as data:
+                    self.mute(agentIndex)
+                    self._agentCrash(agentIndex)
+                    self.unmute()
+                    return
+            else:
+                self.state = self.state.getResult( agentIndex, action )
+
+            # Change the display
+            self.display.update( self.state.data )
+            ###idx = agentIndex - agentIndex % 2 + 1
+            ###self.display.update( self.state.makeObservation(idx).data )
+
+            # Allow for game specific conditions (winning, losing, etc.)
+            self.rules.process(self.state, self)
+            # Track progress
+            if agentIndex == numAgents + 1: self.numMoves += 1
+            # Next agent
+            agentIndex = ( agentIndex + 1 ) % numAgents
+
+            if _BOINC_ENABLED:
+                boinc.set_fraction_done(self.getProgress())
+
+        # inform a learning agent of the game result
+        for agentIndex, agent in enumerate(self.agents):
+            if "final" in dir( agent ) :
+                try:
+                    self.mute(agentIndex)
+                    agent.final( self.state )
+                    self.unmute()
+                except Exception as data:
+                    if not self.catchExceptions: raise
+                    self._agentCrash(agentIndex)
+                    self.unmute()
+                    return
+        self.display.finish()

tracking/ghostAgents.py

@@ -0,0 +1,81 @@
+# ghostAgents.py
+# --------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+from game import Agent
+from game import Actions
+from game import Directions
+import random
+from util import manhattanDistance
+import util
+
+class GhostAgent( Agent ):
+    def __init__( self, index ):
+        self.index = index
+
+    def getAction( self, state ):
+        dist = self.getDistribution(state)
+        if len(dist) == 0:
+            return Directions.STOP
+        else:
+            return util.chooseFromDistribution( dist )
+
+    def getDistribution(self, state):
+        "Returns a Counter encoding a distribution over actions from the provided state."
+        util.raiseNotDefined()
+
+class RandomGhost( GhostAgent ):
+    "A ghost that chooses a legal action uniformly at random."
+    def getDistribution( self, state ):
+        dist = util.Counter()
+        for a in state.getLegalActions( self.index ): dist[a] = 1.0
+        dist.normalize()
+        return dist
+
+class DirectionalGhost( GhostAgent ):
+    "A ghost that prefers to rush Pacman, or flee when scared."
+    def __init__( self, index, prob_attack=0.8, prob_scaredFlee=0.8 ):
+        self.index = index
+        self.prob_attack = prob_attack
+        self.prob_scaredFlee = prob_scaredFlee
+
+    def getDistribution( self, state ):
+        # Read variables from state
+        ghostState = state.getGhostState( self.index )
+        legalActions = state.getLegalActions( self.index )
+        pos = state.getGhostPosition( self.index )
+        isScared = ghostState.scaredTimer > 0
+
+        speed = 1
+        if isScared: speed = 0.5
+
+        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
+        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
+        pacmanPosition = state.getPacmanPosition()
+
+        # Select best actions given the state
+        distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
+        if isScared:
+            bestScore = max( distancesToPacman )
+            bestProb = self.prob_scaredFlee
+        else:
+            bestScore = min( distancesToPacman )
+            bestProb = self.prob_attack
+        bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]
+
+        # Construct distribution
+        dist = util.Counter()
+        for a in bestActions: dist[a] = bestProb / len(bestActions)
+        for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
+        dist.normalize()
+        return dist

tracking/grading.py

@@ -0,0 +1,323 @@
+# grading.py
+# ----------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+"Common code for autograders"
+
+import html
+import time
+import sys
+import json
+import traceback
+import pdb
+from collections import defaultdict
+import util
+
+class Grades:
+  "A data structure for project grades, along with formatting code to display them"
+  def __init__(self, projectName, questionsAndMaxesList,
+               gsOutput=False, edxOutput=False, muteOutput=False):
+    """
+    Defines the grading scheme for a project
+      projectName: project name
+      questionsAndMaxesDict: a list of (question name, max points per question)
+    """
+    self.questions = [el[0] for el in questionsAndMaxesList]
+    self.maxes = dict(questionsAndMaxesList)
+    self.points = Counter()
+    self.messages = dict([(q, []) for q in self.questions])
+    self.project = projectName
+    self.start = time.localtime()[1:6]
+    self.sane = True # Sanity checks
+    self.currentQuestion = None # Which question we're grading
+    self.edxOutput = edxOutput
+    self.gsOutput = gsOutput  # GradeScope output
+    self.mute = muteOutput
+    self.prereqs = defaultdict(set)
+
+    #print 'Autograder transcript for %s' % self.project
+    print('Starting on %d-%d at %d:%02d:%02d' % self.start)
+
+  def addPrereq(self, question, prereq):
+    self.prereqs[question].add(prereq)
+
+  def grade(self, gradingModule, exceptionMap = {}, bonusPic = False):
+    """
+    Grades each question
+      gradingModule: the module with all the grading functions (pass in with sys.modules[__name__])
+    """
+
+    completedQuestions = set([])
+    for q in self.questions:
+      print('\nQuestion %s' % q)
+      print('=' * (9 + len(q)))
+      print
+      self.currentQuestion = q
+
+      incompleted = self.prereqs[q].difference(completedQuestions)
+      if len(incompleted) > 0:
+          prereq = incompleted.pop()
+          print(\
+"""*** NOTE: Make sure to complete Question %s before working on Question %s,
+*** because Question %s builds upon your answer for Question %s.
+""" % (prereq, q, q, prereq))
+          continue
+
+      if self.mute: util.mutePrint()
+      try:
+        util.TimeoutFunction(getattr(gradingModule, q),1800)(self) # Call the question's function
+        #TimeoutFunction(getattr(gradingModule, q),1200)(self) # Call the question's function
+      except Exception as inst:
+        self.addExceptionMessage(q, inst, traceback)
+        self.addErrorHints(exceptionMap, inst, q[1])
+      except:
+        self.fail('FAIL: Terminated with a string exception.')
+      finally:
+        if self.mute: util.unmutePrint()
+
+      if self.points[q] >= self.maxes[q]:
+        completedQuestions.add(q)
+
+      print('\n### Question %s: %d/%d ###\n' % (q, self.points[q], self.maxes[q]))
+
+
+    print('\nFinished at %d:%02d:%02d' % time.localtime()[3:6])
+    print("\nProvisional grades\n==================")
+
+    for q in self.questions:
+      print('Question %s: %d/%d' % (q, self.points[q], self.maxes[q]))
+    print('------------------')
+    print('Total: %d/%d' % (self.points.totalCount(), sum(self.maxes.values())))
+    if bonusPic and self.points.totalCount() == 25:
+      print("""
+
+                     ALL HAIL GRANDPAC.
+              LONG LIVE THE GHOSTBUSTING KING.
+
+                  ---      ----      ---
+                  |  \    /  + \    /  |
+                  | + \--/      \--/ + |
+                  |   +     +          |
+                  | +     +        +   |
+                @@@@@@@@@@@@@@@@@@@@@@@@@@
+              @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            \   @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+             \ /  @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+              V   \   @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+                   \ /  @@@@@@@@@@@@@@@@@@@@@@@@@@
+                    V     @@@@@@@@@@@@@@@@@@@@@@@@
+                            @@@@@@@@@@@@@@@@@@@@@@
+                    /\      @@@@@@@@@@@@@@@@@@@@@@
+                   /  \  @@@@@@@@@@@@@@@@@@@@@@@@@
+              /\  /    @@@@@@@@@@@@@@@@@@@@@@@@@@@
+             /  \ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            /    @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+            @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+              @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+                @@@@@@@@@@@@@@@@@@@@@@@@@@
+                    @@@@@@@@@@@@@@@@@@
+
+""")
+    print("""
+Your grades are NOT yet registered.  To register your grades, make sure
+to follow your instructor's guidelines to receive credit on your project.
+""")
+
+    if self.edxOutput:
+        self.produceOutput()
+    if self.gsOutput:
+        self.produceGradeScopeOutput()
+
+  def addExceptionMessage(self, q, inst, traceback):
+    """
+    Method to format the exception message, this is more complicated because
+    we need to html.escape the traceback but wrap the exception in a <pre> tag
+    """
+    self.fail('FAIL: Exception raised: %s' % inst)
+    self.addMessage('')
+    for line in traceback.format_exc().split('\n'):
+        self.addMessage(line)
+
+  def addErrorHints(self, exceptionMap, errorInstance, questionNum):
+    typeOf = str(type(errorInstance))
+    questionName = 'q' + questionNum
+    errorHint = ''
+
+    # question specific error hints
+    if exceptionMap.get(questionName):
+      questionMap = exceptionMap.get(questionName)
+      if (questionMap.get(typeOf)):
+        errorHint = questionMap.get(typeOf)
+    # fall back to general error messages if a question specific
+    # one does not exist
+    if (exceptionMap.get(typeOf)):
+      errorHint = exceptionMap.get(typeOf)
+
+    # dont include the HTML if we have no error hint
+    if not errorHint:
+      return ''
+
+    for line in errorHint.split('\n'):
+      self.addMessage(line)
+
+  def produceGradeScopeOutput(self):
+    out_dct = {}
+
+    # total of entire submission
+    total_possible = sum(self.maxes.values())
+    total_score = sum(self.points.values())
+    out_dct['score'] = total_score
+    out_dct['max_score'] = total_possible
+    out_dct['output'] = "Total score (%d / %d)" % (total_score, total_possible)
+
+    # individual tests
+    tests_out = []
+    for name in self.questions:
+      test_out = {}
+      # test name
+      test_out['name'] = name
+      # test score
+      test_out['score'] = self.points[name]
+      test_out['max_score'] = self.maxes[name]
+      # others
+      is_correct = self.points[name] >= self.maxes[name]
+      test_out['output'] = "  Question {num} ({points}/{max}) {correct}".format(
+          num=(name[1] if len(name) == 2 else name),
+          points=test_out['score'],
+          max=test_out['max_score'],
+          correct=('X' if not is_correct else ''),
+      )
+      test_out['tags'] = []
+      tests_out.append(test_out)
+    out_dct['tests'] = tests_out
+
+    # file output
+    with open('gradescope_response.json', 'w') as outfile:
+        json.dump(out_dct, outfile)
+    return
+
+  def produceOutput(self):
+    edxOutput = open('edx_response.html', 'w')
+    edxOutput.write("<div>")
+
+    # first sum
+    total_possible = sum(self.maxes.values())
+    total_score = sum(self.points.values())
+    checkOrX = '<span class="incorrect"/>'
+    if (total_score >= total_possible):
+        checkOrX = '<span class="correct"/>'
+    header = """
+        <h3>
+            Total score ({total_score} / {total_possible})
+        </h3>
+    """.format(total_score = total_score,
+      total_possible = total_possible,
+      checkOrX = checkOrX
+    )
+    edxOutput.write(header)
+
+    for q in self.questions:
+      if len(q) == 2:
+          name = q[1]
+      else:
+          name = q
+      checkOrX = '<span class="incorrect"/>'
+      if (self.points[q] >= self.maxes[q]):
+        checkOrX = '<span class="correct"/>'
+      #messages = '\n<br/>\n'.join(self.messages[q])
+      messages = "<pre>%s</pre>" % '\n'.join(self.messages[q])
+      output = """
+        <div class="test">
+          <section>
+          <div class="shortform">
+            Question {q} ({points}/{max}) {checkOrX}
+          </div>
+        <div class="longform">
+          {messages}
+        </div>
+        </section>
+      </div>
+      """.format(q = name,
+        max = self.maxes[q],
+        messages = messages,
+        checkOrX = checkOrX,
+        points = self.points[q]
+      )
+      # print "*** output for Question %s " % q[1]
+      # print output
+      edxOutput.write(output)
+    edxOutput.write("</div>")
+    edxOutput.close()
+    edxOutput = open('edx_grade', 'w')
+    edxOutput.write(str(self.points.totalCount()))
+    edxOutput.close()
+
+  def fail(self, message, raw=False):
+    "Sets sanity check bit to false and outputs a message"
+    self.sane = False
+    self.assignZeroCredit()
+    self.addMessage(message, raw)
+
+  def assignZeroCredit(self):
+    self.points[self.currentQuestion] = 0
+
+  def addPoints(self, amt):
+    self.points[self.currentQuestion] += amt
+
+  def deductPoints(self, amt):
+    self.points[self.currentQuestion] -= amt
+
+  def assignFullCredit(self, message="", raw=False):
+    self.points[self.currentQuestion] = self.maxes[self.currentQuestion]
+    if message != "":
+      self.addMessage(message, raw)
+
+  def addMessage(self, message, raw=False):
+    if not raw:
+        # We assume raw messages, formatted for HTML, are printed separately
+        if self.mute: util.unmutePrint()
+        print('*** ' + message)
+        if self.mute: util.mutePrint()
+        message = html.escape(message)
+    self.messages[self.currentQuestion].append(message)
+
+  def addMessageToEmail(self, message):
+    print("WARNING**** addMessageToEmail is deprecated %s" % message)
+    for line in message.split('\n'):
+      pass
+      #print '%%% ' + line + ' %%%'
+      #self.messages[self.currentQuestion].append(line)
+
+
+
+
+
+class Counter(dict):
+  """
+  Dict with default 0
+  """
+  def __getitem__(self, idx):
+    try:
+      return dict.__getitem__(self, idx)
+    except KeyError:
+      return 0
+
+  def totalCount(self):
+    """
+    Returns the sum of counts for all keys.
+    """
+    return sum(self.values())
+

tracking/graphicsDisplay.py

@@ -0,0 +1,679 @@
+# graphicsDisplay.py
+# ------------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+from graphicsUtils import *
+import math, time
+from game import Directions
+
+###########################
+#  GRAPHICS DISPLAY CODE  #
+###########################
+
+# Most code by Dan Klein and John Denero written or rewritten for cs188, UC Berkeley.
+# Some code from a Pacman implementation by LiveWires, and used / modified with permission.
+
+DEFAULT_GRID_SIZE = 30.0
+INFO_PANE_HEIGHT = 35
+BACKGROUND_COLOR = formatColor(0,0,0)
+WALL_COLOR = formatColor(0.0/255.0, 51.0/255.0, 255.0/255.0)
+INFO_PANE_COLOR = formatColor(.4,.4,0)
+SCORE_COLOR = formatColor(.9, .9, .9)
+PACMAN_OUTLINE_WIDTH = 2
+PACMAN_CAPTURE_OUTLINE_WIDTH = 4
+
+GHOST_COLORS = []
+GHOST_COLORS.append(formatColor(.9,0,0)) # Red
+GHOST_COLORS.append(formatColor(0,.3,.9)) # Blue
+GHOST_COLORS.append(formatColor(.98,.41,.07)) # Orange
+GHOST_COLORS.append(formatColor(.1,.75,.7)) # Green
+GHOST_COLORS.append(formatColor(1.0,0.6,0.0)) # Yellow
+GHOST_COLORS.append(formatColor(.4,0.13,0.91)) # Purple
+
+TEAM_COLORS = GHOST_COLORS[:2]
+
+GHOST_SHAPE = [
+    ( 0,    0.3 ),
+    ( 0.25, 0.75 ),
+    ( 0.5,  0.3 ),
+    ( 0.75, 0.75 ),
+    ( 0.75, -0.5 ),
+    ( 0.5,  -0.75 ),
+    (-0.5,  -0.75 ),
+    (-0.75, -0.5 ),
+    (-0.75, 0.75 ),
+    (-0.5,  0.3 ),
+    (-0.25, 0.75 )
+  ]
+GHOST_SIZE = 0.65
+SCARED_COLOR = formatColor(1,1,1)
+
+GHOST_VEC_COLORS = list(map(colorToVector, GHOST_COLORS))
+
+PACMAN_COLOR = formatColor(255.0/255.0,255.0/255.0,61.0/255)
+PACMAN_SCALE = 0.5
+#pacman_speed = 0.25
+
+# Food
+FOOD_COLOR = formatColor(1,1,1)
+FOOD_SIZE = 0.1
+
+# Laser
+LASER_COLOR = formatColor(1,0,0)
+LASER_SIZE = 0.02
+
+# Capsule graphics
+CAPSULE_COLOR = formatColor(1,1,1)
+CAPSULE_SIZE = 0.25
+
+# Drawing walls
+WALL_RADIUS = 0.15
+
+class InfoPane:
+    def __init__(self, layout, gridSize):
+        self.gridSize = gridSize
+        self.width = (layout.width) * gridSize
+        self.base = (layout.height + 1) * gridSize
+        self.height = INFO_PANE_HEIGHT
+        self.fontSize = 24
+        self.textColor = PACMAN_COLOR
+        self.drawPane()
+
+    def toScreen(self, pos, y = None):
+        """
+          Translates a point relative from the bottom left of the info pane.
+        """
+        if y == None:
+            x,y = pos
+        else:
+            x = pos
+
+        x = self.gridSize + x # Margin
+        y = self.base + y
+        return x,y
+
+    def drawPane(self):
+        self.scoreText = text( self.toScreen(0, 0  ), self.textColor, "SCORE:    0", "Times", self.fontSize, "bold")
+
+    def initializeGhostDistances(self, distances):
+        self.ghostDistanceText = []
+
+        size = 20
+        if self.width < 240:
+            size = 12
+        if self.width < 160:
+            size = 10
+
+        for i, d in enumerate(distances):
+            t = text( self.toScreen(self.width//2 + self.width//8 * i, 0), GHOST_COLORS[i+1], d, "Times", size, "bold")
+            self.ghostDistanceText.append(t)
+
+    def updateScore(self, score):
+        changeText(self.scoreText, "SCORE: % 4d" % score)
+
+    def setTeam(self, isBlue):
+        text = "RED TEAM"
+        if isBlue: text = "BLUE TEAM"
+        self.teamText = text( self.toScreen(300, 0  ), self.textColor, text, "Times", self.fontSize, "bold")
+
+    def updateGhostDistances(self, distances):
+        if len(distances) == 0: return
+        if 'ghostDistanceText' not in dir(self): self.initializeGhostDistances(distances)
+        else:
+            for i, d in enumerate(distances):
+                changeText(self.ghostDistanceText[i], d)
+
+    def drawGhost(self):
+        pass
+
+    def drawPacman(self):
+        pass
+
+    def drawWarning(self):
+        pass
+
+    def clearIcon(self):
+        pass
+
+    def updateMessage(self, message):
+        pass
+
+    def clearMessage(self):
+        pass
+
+
+class PacmanGraphics:
+    def __init__(self, zoom=1.0, frameTime=0.0, capture=False):
+        self.have_window = 0
+        self.currentGhostImages = {}
+        self.pacmanImage = None
+        self.zoom = zoom
+        self.gridSize = DEFAULT_GRID_SIZE * zoom
+        self.capture = capture
+        self.frameTime = frameTime
+
+    def checkNullDisplay(self):
+        return False
+
+    def initialize(self, state, isBlue = False):
+        self.isBlue = isBlue
+        self.startGraphics(state)
+
+        # self.drawDistributions(state)
+        self.distributionImages = None  # Initialized lazily
+        self.drawStaticObjects(state)
+        self.drawAgentObjects(state)
+
+        # Information
+        self.previousState = state
+
+    def startGraphics(self, state):
+        self.layout = state.layout
+        layout = self.layout
+        self.width = layout.width
+        self.height = layout.height
+        self.make_window(self.width, self.height)
+        self.infoPane = InfoPane(layout, self.gridSize)
+        self.currentState = layout
+
+    def drawDistributions(self, state):
+        walls = state.layout.walls
+        dist = []
+        for x in range(walls.width):
+            distx = []
+            dist.append(distx)
+            for y in range(walls.height):
+                ( screen_x, screen_y ) = self.to_screen( (x, y) )
+                block = square( (screen_x, screen_y),
+                                0.5 * self.gridSize,
+                                color = BACKGROUND_COLOR,
+                                filled = 1, behind=2)
+                distx.append(block)
+        self.distributionImages = dist
+
+    def drawStaticObjects(self, state):
+        layout = self.layout
+        self.drawWalls(layout.walls)
+        self.food = self.drawFood(layout.food)
+        self.capsules = self.drawCapsules(layout.capsules)
+        refresh()
+
+    def drawAgentObjects(self, state):
+        self.agentImages = [] # (agentState, image)
+        for index, agent in enumerate(state.agentStates):
+            if agent.isPacman:
+                image = self.drawPacman(agent, index)
+                self.agentImages.append( (agent, image) )
+            else:
+                image = self.drawGhost(agent, index)
+                self.agentImages.append( (agent, image) )
+        refresh()
+
+    def swapImages(self, agentIndex, newState):
+        """
+          Changes an image from a ghost to a pacman or vis versa (for capture)
+        """
+        prevState, prevImage = self.agentImages[agentIndex]
+        for item in prevImage: remove_from_screen(item)
+        if newState.isPacman:
+            image = self.drawPacman(newState, agentIndex)
+            self.agentImages[agentIndex] = (newState, image )
+        else:
+            image = self.drawGhost(newState, agentIndex)
+            self.agentImages[agentIndex] = (newState, image )
+        refresh()
+
+    def update(self, newState):
+        agentIndex = newState._agentMoved
+        agentState = newState.agentStates[agentIndex]
+
+        if self.agentImages[agentIndex][0].isPacman != agentState.isPacman: self.swapImages(agentIndex, agentState)
+        prevState, prevImage = self.agentImages[agentIndex]
+        if agentState.isPacman:
+            self.animatePacman(agentState, prevState, prevImage)
+        else:
+            self.moveGhost(agentState, agentIndex, prevState, prevImage)
+        self.agentImages[agentIndex] = (agentState, prevImage)
+
+        if newState._foodEaten != None:
+            self.removeFood(newState._foodEaten, self.food)
+        if newState._capsuleEaten != None:
+            self.removeCapsule(newState._capsuleEaten, self.capsules)
+        self.infoPane.updateScore(newState.score)
+        if 'ghostDistances' in dir(newState):
+            self.infoPane.updateGhostDistances(newState.ghostDistances)
+
+    def make_window(self, width, height):
+        grid_width = (width-1) * self.gridSize
+        grid_height = (height-1) * self.gridSize
+        screen_width = 2*self.gridSize + grid_width
+        screen_height = 2*self.gridSize + grid_height + INFO_PANE_HEIGHT
+
+        begin_graphics(screen_width,
+                       screen_height,
+                       BACKGROUND_COLOR,
+                       "CS188 Pacman")
+
+    def drawPacman(self, pacman, index):
+        position = self.getPosition(pacman)
+        screen_point = self.to_screen(position)
+        endpoints = self.getEndpoints(self.getDirection(pacman))
+
+        width = PACMAN_OUTLINE_WIDTH
+        outlineColor = PACMAN_COLOR
+        fillColor = PACMAN_COLOR
+
+        if self.capture:
+            outlineColor = TEAM_COLORS[index % 2]
+            fillColor = GHOST_COLORS[index]
+            width = PACMAN_CAPTURE_OUTLINE_WIDTH
+
+        return [circle(screen_point, PACMAN_SCALE * self.gridSize,
+                       fillColor = fillColor, outlineColor = outlineColor,
+                       endpoints = endpoints,
+                       width = width)]
+
+    def getEndpoints(self, direction, position=(0,0)):
+        x, y = position
+        pos = x - int(x) + y - int(y)
+        width = 30 + 80 * math.sin(math.pi* pos)
+
+        delta = width / 2
+        if (direction == 'West'):
+            endpoints = (180+delta, 180-delta)
+        elif (direction == 'North'):
+            endpoints = (90+delta, 90-delta)
+        elif (direction == 'South'):
+            endpoints = (270+delta, 270-delta)
+        else:
+            endpoints = (0+delta, 0-delta)
+        return endpoints
+
+    def movePacman(self, position, direction, image):
+        screenPosition = self.to_screen(position)
+        endpoints = self.getEndpoints( direction, position )
+        r = PACMAN_SCALE * self.gridSize
+        moveCircle(image[0], screenPosition, r, endpoints)
+        refresh()
+
+    def animatePacman(self, pacman, prevPacman, image):
+        if self.frameTime < 0:
+            print('Press any key to step forward, "q" to play')
+            keys = wait_for_keys()
+            if 'q' in keys:
+                self.frameTime = 0.1
+        if self.frameTime > 0.01 or self.frameTime < 0:
+            start = time.time()
+            fx, fy = self.getPosition(prevPacman)
+            px, py = self.getPosition(pacman)
+            frames = 4.0
+            for i in range(1,int(frames) + 1):
+                pos = px*i/frames + fx*(frames-i)/frames, py*i/frames + fy*(frames-i)/frames
+                self.movePacman(pos, self.getDirection(pacman), image)
+                refresh()
+                sleep(abs(self.frameTime) / frames)
+        else:
+            self.movePacman(self.getPosition(pacman), self.getDirection(pacman), image)
+        refresh()
+
+    def getGhostColor(self, ghost, ghostIndex):
+        if ghost.scaredTimer > 0:
+            return SCARED_COLOR
+        else:
+            return GHOST_COLORS[ghostIndex]
+
+    def drawGhost(self, ghost, agentIndex):
+        pos = self.getPosition(ghost)
+        dir = self.getDirection(ghost)
+        (screen_x, screen_y) = (self.to_screen(pos) )
+        coords = []
+        for (x, y) in GHOST_SHAPE:
+            coords.append((x*self.gridSize*GHOST_SIZE + screen_x, y*self.gridSize*GHOST_SIZE + screen_y))
+
+        colour = self.getGhostColor(ghost, agentIndex)
+        body = polygon(coords, colour, filled = 1)
+        WHITE = formatColor(1.0, 1.0, 1.0)
+        BLACK = formatColor(0.0, 0.0, 0.0)
+
+        dx = 0
+        dy = 0
+        if dir == 'North':
+            dy = -0.2
+        if dir == 'South':
+            dy = 0.2
+        if dir == 'East':
+            dx = 0.2
+        if dir == 'West':
+            dx = -0.2
+        leftEye = circle((screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2, WHITE, WHITE)
+        rightEye = circle((screen_x+self.gridSize*GHOST_SIZE*(0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2, WHITE, WHITE)
+        leftPupil = circle((screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08, BLACK, BLACK)
+        rightPupil = circle((screen_x+self.gridSize*GHOST_SIZE*(0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08, BLACK, BLACK)
+        ghostImageParts = []
+        ghostImageParts.append(body)
+        ghostImageParts.append(leftEye)
+        ghostImageParts.append(rightEye)
+        ghostImageParts.append(leftPupil)
+        ghostImageParts.append(rightPupil)
+
+        return ghostImageParts
+
+    def moveEyes(self, pos, dir, eyes):
+        (screen_x, screen_y) = (self.to_screen(pos) )
+        dx = 0
+        dy = 0
+        if dir == 'North':
+            dy = -0.2
+        if dir == 'South':
+            dy = 0.2
+        if dir == 'East':
+            dx = 0.2
+        if dir == 'West':
+            dx = -0.2
+        moveCircle(eyes[0],(screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2)
+        moveCircle(eyes[1],(screen_x+self.gridSize*GHOST_SIZE*(0.3+dx/1.5), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy/1.5)), self.gridSize*GHOST_SIZE*0.2)
+        moveCircle(eyes[2],(screen_x+self.gridSize*GHOST_SIZE*(-0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08)
+        moveCircle(eyes[3],(screen_x+self.gridSize*GHOST_SIZE*(0.3+dx), screen_y-self.gridSize*GHOST_SIZE*(0.3-dy)), self.gridSize*GHOST_SIZE*0.08)
+
+    def moveGhost(self, ghost, ghostIndex, prevGhost, ghostImageParts):
+        old_x, old_y = self.to_screen(self.getPosition(prevGhost))
+        new_x, new_y = self.to_screen(self.getPosition(ghost))
+        delta = new_x - old_x, new_y - old_y
+
+        for ghostImagePart in ghostImageParts:
+            move_by(ghostImagePart, delta)
+        refresh()
+
+        if ghost.scaredTimer > 0:
+            color = SCARED_COLOR
+        else:
+            color = GHOST_COLORS[ghostIndex]
+        edit(ghostImageParts[0], ('fill', color), ('outline', color))
+        self.moveEyes(self.getPosition(ghost), self.getDirection(ghost), ghostImageParts[-4:])
+        refresh()
+
+    def getPosition(self, agentState):
+        if agentState.configuration == None: return (-1000, -1000)
+        return agentState.getPosition()
+
+    def getDirection(self, agentState):
+        if agentState.configuration == None: return Directions.STOP
+        return agentState.configuration.getDirection()
+
+    def finish(self):
+        end_graphics()
+
+    def to_screen(self, point):
+        ( x, y ) = point
+        #y = self.height - y
+        x = (x + 1)*self.gridSize
+        y = (self.height  - y)*self.gridSize
+        return ( x, y )
+
+    # Fixes some TK issue with off-center circles
+    def to_screen2(self, point):
+        ( x, y ) = point
+        #y = self.height - y
+        x = (x + 1)*self.gridSize
+        y = (self.height  - y)*self.gridSize
+        return ( x, y )
+
+    def drawWalls(self, wallMatrix):
+        wallColor = WALL_COLOR
+        for xNum, x in enumerate(wallMatrix):
+            if self.capture and (xNum * 2) < wallMatrix.width: wallColor = TEAM_COLORS[0]
+            if self.capture and (xNum * 2) >= wallMatrix.width: wallColor = TEAM_COLORS[1]
+
+            for yNum, cell in enumerate(x):
+                if cell: # There's a wall here
+                    pos = (xNum, yNum)
+                    screen = self.to_screen(pos)
+                    screen2 = self.to_screen2(pos)
+
+                    # draw each quadrant of the square based on adjacent walls
+                    wIsWall = self.isWall(xNum-1, yNum, wallMatrix)
+                    eIsWall = self.isWall(xNum+1, yNum, wallMatrix)
+                    nIsWall = self.isWall(xNum, yNum+1, wallMatrix)
+                    sIsWall = self.isWall(xNum, yNum-1, wallMatrix)
+                    nwIsWall = self.isWall(xNum-1, yNum+1, wallMatrix)
+                    swIsWall = self.isWall(xNum-1, yNum-1, wallMatrix)
+                    neIsWall = self.isWall(xNum+1, yNum+1, wallMatrix)
+                    seIsWall = self.isWall(xNum+1, yNum-1, wallMatrix)
+
+                    # NE quadrant
+                    if (not nIsWall) and (not eIsWall):
+                        # inner circle
+                        circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (0,91), 'arc')
+                    if (nIsWall) and (not eIsWall):
+                        # vertical line
+                        line(add(screen, (self.gridSize*WALL_RADIUS, 0)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-0.5)-1)), wallColor)
+                    if (not nIsWall) and (eIsWall):
+                        # horizontal line
+                        line(add(screen, (0, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
+                    if (nIsWall) and (eIsWall) and (not neIsWall):
+                        # outer circle
+                        circle(add(screen2, (self.gridSize*2*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (180,271), 'arc')
+                        line(add(screen, (self.gridSize*2*WALL_RADIUS-1, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
+                        line(add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS+1)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(-0.5))), wallColor)
+
+                    # NW quadrant
+                    if (not nIsWall) and (not wIsWall):
+                        # inner circle
+                        circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (90,181), 'arc')
+                    if (nIsWall) and (not wIsWall):
+                        # vertical line
+                        line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, 0)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-0.5)-1)), wallColor)
+                    if (not nIsWall) and (wIsWall):
+                        # horizontal line
+                        line(add(screen, (0, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5)-1, self.gridSize*(-1)*WALL_RADIUS)), wallColor)
+                    if (nIsWall) and (wIsWall) and (not nwIsWall):
+                        # outer circle
+                        circle(add(screen2, (self.gridSize*(-2)*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (270,361), 'arc')
+                        line(add(screen, (self.gridSize*(-2)*WALL_RADIUS+1, self.gridSize*(-1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5), self.gridSize*(-1)*WALL_RADIUS)), wallColor)
+                        line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-2)*WALL_RADIUS+1)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(-0.5))), wallColor)
+
+                    # SE quadrant
+                    if (not sIsWall) and (not eIsWall):
+                        # inner circle
+                        circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (270,361), 'arc')
+                    if (sIsWall) and (not eIsWall):
+                        # vertical line
+                        line(add(screen, (self.gridSize*WALL_RADIUS, 0)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(0.5)+1)), wallColor)
+                    if (not sIsWall) and (eIsWall):
+                        # horizontal line
+                        line(add(screen, (0, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5+1, self.gridSize*(1)*WALL_RADIUS)), wallColor)
+                    if (sIsWall) and (eIsWall) and (not seIsWall):
+                        # outer circle
+                        circle(add(screen2, (self.gridSize*2*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (90,181), 'arc')
+                        line(add(screen, (self.gridSize*2*WALL_RADIUS-1, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*0.5, self.gridSize*(1)*WALL_RADIUS)), wallColor)
+                        line(add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS-1)), add(screen, (self.gridSize*WALL_RADIUS, self.gridSize*(0.5))), wallColor)
+
+                    # SW quadrant
+                    if (not sIsWall) and (not wIsWall):
+                        # inner circle
+                        circle(screen2, WALL_RADIUS * self.gridSize, wallColor, wallColor, (180,271), 'arc')
+                    if (sIsWall) and (not wIsWall):
+                        # vertical line
+                        line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, 0)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(0.5)+1)), wallColor)
+                    if (not sIsWall) and (wIsWall):
+                        # horizontal line
+                        line(add(screen, (0, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5)-1, self.gridSize*(1)*WALL_RADIUS)), wallColor)
+                    if (sIsWall) and (wIsWall) and (not swIsWall):
+                        # outer circle
+                        circle(add(screen2, (self.gridSize*(-2)*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS)), WALL_RADIUS * self.gridSize-1, wallColor, wallColor, (0,91), 'arc')
+                        line(add(screen, (self.gridSize*(-2)*WALL_RADIUS+1, self.gridSize*(1)*WALL_RADIUS)), add(screen, (self.gridSize*(-0.5), self.gridSize*(1)*WALL_RADIUS)), wallColor)
+                        line(add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(2)*WALL_RADIUS-1)), add(screen, (self.gridSize*(-1)*WALL_RADIUS, self.gridSize*(0.5))), wallColor)
+
+    def isWall(self, x, y, walls):
+        if x < 0 or y < 0:
+            return False
+        if x >= walls.width or y >= walls.height:
+            return False
+        return walls[x][y]
+
+    def drawFood(self, foodMatrix ):
+        foodImages = []
+        color = FOOD_COLOR
+        for xNum, x in enumerate(foodMatrix):
+            if self.capture and (xNum * 2) <= foodMatrix.width: color = TEAM_COLORS[0]
+            if self.capture and (xNum * 2) > foodMatrix.width: color = TEAM_COLORS[1]
+            imageRow = []
+            foodImages.append(imageRow)
+            for yNum, cell in enumerate(x):
+                if cell: # There's food here
+                    screen = self.to_screen((xNum, yNum ))
+                    dot = circle( screen,
+                                  FOOD_SIZE * self.gridSize,
+                                  outlineColor = color, fillColor = color,
+                                  width = 1)
+                    imageRow.append(dot)
+                else:
+                    imageRow.append(None)
+        return foodImages
+
+    def drawCapsules(self, capsules ):
+        capsuleImages = {}
+        for capsule in capsules:
+            ( screen_x, screen_y ) = self.to_screen(capsule)
+            dot = circle( (screen_x, screen_y),
+                              CAPSULE_SIZE * self.gridSize,
+                              outlineColor = CAPSULE_COLOR,
+                              fillColor = CAPSULE_COLOR,
+                              width = 1)
+            capsuleImages[capsule] = dot
+        return capsuleImages
+
+    def removeFood(self, cell, foodImages ):
+        x, y = cell
+        remove_from_screen(foodImages[x][y])
+
+    def removeCapsule(self, cell, capsuleImages ):
+        x, y = cell
+        remove_from_screen(capsuleImages[(x, y)])
+
+    def drawExpandedCells(self, cells):
+        """
+        Draws an overlay of expanded grid positions for search agents
+        """
+        n = float(len(cells))
+        baseColor = [1.0, 0.0, 0.0]
+        self.clearExpandedCells()
+        self.expandedCells = []
+        for k, cell in enumerate(cells):
+            screenPos = self.to_screen( cell)
+            cellColor = formatColor(*[(n-k) * c * .5 / n + .25 for c in baseColor])
+            block = square(screenPos,
+                     0.5 * self.gridSize,
+                     color = cellColor,
+                     filled = 1, behind=2)
+            self.expandedCells.append(block)
+            if self.frameTime < 0:
+                refresh()
+
+    def clearExpandedCells(self):
+        if 'expandedCells' in dir(self) and len(self.expandedCells) > 0:
+            for cell in self.expandedCells:
+                remove_from_screen(cell)
+
+
+    def updateDistributions(self, distributions):
+        "Draws an agent's belief distributions"
+        # copy all distributions so we don't change their state
+        distributions = list(map(lambda x: x.copy(), distributions))
+        if self.distributionImages == None:
+            self.drawDistributions(self.previousState)
+        for x in range(len(self.distributionImages)):
+            for y in range(len(self.distributionImages[0])):
+                image = self.distributionImages[x][y]
+                weights = [dist[ (x,y) ] for dist in distributions]
+
+                if sum(weights) != 0:
+                    pass
+                # Fog of war
+                color = [0.0,0.0,0.0]
+                colors = GHOST_VEC_COLORS[1:] # With Pacman
+                if self.capture: colors = GHOST_VEC_COLORS
+                for weight, gcolor in zip(weights, colors):
+                    color = [min(1.0, c + 0.95 * g * weight ** .3) for c,g in zip(color, gcolor)]
+                changeColor(image, formatColor(*color))
+        refresh()
+
+class FirstPersonPacmanGraphics(PacmanGraphics):
+    def __init__(self, zoom = 1.0, showGhosts = True, capture = False, frameTime=0):
+        PacmanGraphics.__init__(self, zoom, frameTime=frameTime)
+        self.showGhosts = showGhosts
+        self.capture = capture
+
+    def initialize(self, state, isBlue = False):
+
+        self.isBlue = isBlue
+        PacmanGraphics.startGraphics(self, state)
+        # Initialize distribution images
+        walls = state.layout.walls
+        dist = []
+        self.layout = state.layout
+
+        # Draw the rest
+        self.distributionImages = None  # initialize lazily
+        self.drawStaticObjects(state)
+        self.drawAgentObjects(state)
+
+        # Information
+        self.previousState = state
+
+    def lookAhead(self, config, state):
+        if config.getDirection() == 'Stop':
+            return
+        else:
+            pass
+            # Draw relevant ghosts
+            allGhosts = state.getGhostStates()
+            visibleGhosts = state.getVisibleGhosts()
+            for i, ghost in enumerate(allGhosts):
+                if ghost in visibleGhosts:
+                    self.drawGhost(ghost, i)
+                else:
+                    self.currentGhostImages[i] = None
+
+    def getGhostColor(self, ghost, ghostIndex):
+        return GHOST_COLORS[ghostIndex]
+
+    def getPosition(self, ghostState):
+        if not self.showGhosts and not ghostState.isPacman and ghostState.getPosition()[1] > 1:
+            return (-1000, -1000)
+        else:
+            return PacmanGraphics.getPosition(self, ghostState)
+
+def add(x, y):
+    return (x[0] + y[0], x[1] + y[1])
+
+
+# Saving graphical output
+# -----------------------
+# Note: to make an animated gif from this postscript output, try the command:
+# convert -delay 7 -loop 1 -compress lzw -layers optimize frame* out.gif
+# convert is part of imagemagick (freeware)
+
+SAVE_POSTSCRIPT = False
+POSTSCRIPT_OUTPUT_DIR = 'frames'
+FRAME_NUMBER = 0
+import os
+
+def saveFrame():
+    "Saves the current graphical output as a postscript file"
+    global SAVE_POSTSCRIPT, FRAME_NUMBER, POSTSCRIPT_OUTPUT_DIR
+    if not SAVE_POSTSCRIPT: return
+    if not os.path.exists(POSTSCRIPT_OUTPUT_DIR): os.mkdir(POSTSCRIPT_OUTPUT_DIR)
+    name = os.path.join(POSTSCRIPT_OUTPUT_DIR, 'frame_%08d.ps' % FRAME_NUMBER)
+    FRAME_NUMBER += 1
+    writePostscript(name) # writes the current canvas

tracking/graphicsUtils.py

@@ -0,0 +1,405 @@
+# graphicsUtils.py
+# ----------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+import sys
+import math
+import random
+import string
+import time
+import types
+try:
+    import tkinter
+except ImportError:
+    tkinter = None
+import os.path
+
+_Windows = sys.platform == 'win32'  # True if on Win95/98/NT
+
+_root_window = None      # The root window for graphics output
+_canvas = None      # The canvas which holds graphics
+_canvas_xs = None      # Size of canvas object
+_canvas_ys = None
+_canvas_x = None      # Current position on canvas
+_canvas_y = None
+_canvas_col = None      # Current colour (set to black below)
+_canvas_tsize = 12
+_canvas_tserifs = 0
+
+def formatColor(r, g, b):
+    return '#%02x%02x%02x' % (int(r * 255), int(g * 255), int(b * 255))
+
+def colorToVector(color):
+    return list(map(lambda x: int(x, 16) / 256.0, [color[1:3], color[3:5], color[5:7]]))
+
+if _Windows:
+    _canvas_tfonts = ['times new roman', 'lucida console']
+else:
+    _canvas_tfonts = ['times', 'lucidasans-24']
+    pass # XXX need defaults here
+
+def sleep(secs):
+    global _root_window
+    if _root_window == None:
+        time.sleep(secs)
+    else:
+        _root_window.update_idletasks()
+        _root_window.after(int(1000 * secs), _root_window.quit)
+        _root_window.mainloop()
+
+def begin_graphics(width=640, height=480, color=formatColor(0, 0, 0), title=None):
+
+    global _root_window, _canvas, _canvas_x, _canvas_y, _canvas_xs, _canvas_ys, _bg_color
+
+    # Check for duplicate call
+    if _root_window is not None:
+        # Lose the window.
+        _root_window.destroy()
+
+    # Save the canvas size parameters
+    _canvas_xs, _canvas_ys = width - 1, height - 1
+    _canvas_x, _canvas_y = 0, _canvas_ys
+    _bg_color = color
+
+    # Create the root window
+    _root_window = tkinter.Tk()
+    _root_window.protocol('WM_DELETE_WINDOW', _destroy_window)
+    _root_window.title(title or 'Graphics Window')
+    _root_window.resizable(0, 0)
+
+    # Create the canvas object
+    try:
+        _canvas = tkinter.Canvas(_root_window, width=width, height=height)
+        _canvas.pack()
+        draw_background()
+        _canvas.update()
+    except:
+        _root_window = None
+        raise
+
+    # Bind to key-down and key-up events
+    _root_window.bind( "<KeyPress>", _keypress )
+    _root_window.bind( "<KeyRelease>", _keyrelease )
+    _root_window.bind( "<FocusIn>", _clear_keys )
+    _root_window.bind( "<FocusOut>", _clear_keys )
+    _root_window.bind( "<Button-1>", _leftclick )
+    _root_window.bind( "<Button-2>", _rightclick )
+    _root_window.bind( "<Button-3>", _rightclick )
+    _root_window.bind( "<Control-Button-1>", _ctrl_leftclick)
+    _clear_keys()
+
+_leftclick_loc = None
+_rightclick_loc = None
+_ctrl_leftclick_loc = None
+
+def _leftclick(event):
+    global _leftclick_loc
+    _leftclick_loc = (event.x, event.y)
+
+def _rightclick(event):
+    global _rightclick_loc
+    _rightclick_loc = (event.x, event.y)
+
+def _ctrl_leftclick(event):
+    global _ctrl_leftclick_loc
+    _ctrl_leftclick_loc = (event.x, event.y)
+
+def wait_for_click():
+    while True:
+        global _leftclick_loc
+        global _rightclick_loc
+        global _ctrl_leftclick_loc
+        if _leftclick_loc != None:
+            val = _leftclick_loc
+            _leftclick_loc = None
+            return val, 'left'
+        if _rightclick_loc != None:
+            val = _rightclick_loc
+            _rightclick_loc = None
+            return val, 'right'
+        if _ctrl_leftclick_loc != None:
+            val = _ctrl_leftclick_loc
+            _ctrl_leftclick_loc = None
+            return val, 'ctrl_left'
+        sleep(0.05)
+
+def draw_background():
+    corners = [(0,0), (0, _canvas_ys), (_canvas_xs, _canvas_ys), (_canvas_xs, 0)]
+    polygon(corners, _bg_color, fillColor=_bg_color, filled=True, smoothed=False)
+
+def _destroy_window(event=None):
+    sys.exit(0)
+#    global _root_window
+#    _root_window.destroy()
+#    _root_window = None
+    #print "DESTROY"
+
+def end_graphics():
+    global _root_window, _canvas, _mouse_enabled
+    try:
+        try:
+            sleep(1)
+            if _root_window != None:
+                _root_window.destroy()
+        except SystemExit as e:
+            print('Ending graphics raised an exception:', e)
+    finally:
+        _root_window = None
+        _canvas = None
+        _mouse_enabled = 0
+        _clear_keys()
+
+def clear_screen(background=None):
+    global _canvas_x, _canvas_y
+    _canvas.delete('all')
+    draw_background()
+    _canvas_x, _canvas_y = 0, _canvas_ys
+
+def polygon(coords, outlineColor, fillColor=None, filled=1, smoothed=1, behind=0, width=1):
+    c = []
+    for coord in coords:
+        c.append(coord[0])
+        c.append(coord[1])
+    if fillColor == None: fillColor = outlineColor
+    if filled == 0: fillColor = ""
+    poly = _canvas.create_polygon(c, outline=outlineColor, fill=fillColor, smooth=smoothed, width=width)
+    if behind > 0:
+        _canvas.tag_lower(poly, behind) # Higher should be more visible
+    return poly
+
+def square(pos, r, color, filled=1, behind=0):
+    x, y = pos
+    coords = [(x - r, y - r), (x + r, y - r), (x + r, y + r), (x - r, y + r)]
+    return polygon(coords, color, color, filled, 0, behind=behind)
+
+def circle(pos, r, outlineColor, fillColor, endpoints=None, style='pieslice', width=2):
+    x, y = pos
+    x0, x1 = x - r - 1, x + r
+    y0, y1 = y - r - 1, y + r
+    if endpoints == None:
+        e = [0, 359]
+    else:
+        e = list(endpoints)
+    while e[0] > e[1]: e[1] = e[1] + 360
+
+    return _canvas.create_arc(x0, y0, x1, y1, outline=outlineColor, fill=fillColor,
+                              extent=e[1] - e[0], start=e[0], style=style, width=width)
+
+def image(pos, file="../../blueghost.gif"):
+    x, y = pos
+    # img = PhotoImage(file=file)
+    return _canvas.create_image(x, y, image = tkinter.PhotoImage(file=file), anchor = tkinter.NW)
+
+
+def refresh():
+    _canvas.update_idletasks()
+
+def moveCircle(id, pos, r, endpoints=None):
+    global _canvas_x, _canvas_y
+
+    x, y = pos
+#    x0, x1 = x - r, x + r + 1
+#    y0, y1 = y - r, y + r + 1
+    x0, x1 = x - r - 1, x + r
+    y0, y1 = y - r - 1, y + r
+    if endpoints == None:
+        e = [0, 359]
+    else:
+        e = list(endpoints)
+    while e[0] > e[1]: e[1] = e[1] + 360
+
+    if os.path.isfile('flag'):
+        edit(id, ('extent', e[1] - e[0]))
+    else:
+        edit(id, ('start', e[0]), ('extent', e[1] - e[0]))
+    move_to(id, x0, y0)
+
+def edit(id, *args):
+    _canvas.itemconfigure(id, **dict(args))
+
+def text(pos, color, contents, font='Helvetica', size=12, style='normal', anchor="nw"):
+    global _canvas_x, _canvas_y
+    x, y = pos
+    font = (font, str(size), style)
+    return _canvas.create_text(x, y, fill=color, text=contents, font=font, anchor=anchor)
+
+def changeText(id, newText, font=None, size=12, style='normal'):
+    _canvas.itemconfigure(id, text=newText)
+    if font != None:
+        _canvas.itemconfigure(id, font=(font, '-%d' % size, style))
+
+def changeColor(id, newColor):
+    _canvas.itemconfigure(id, fill=newColor)
+
+def line(here, there, color=formatColor(0, 0, 0), width=2):
+    x0, y0 = here[0], here[1]
+    x1, y1 = there[0], there[1]
+    return _canvas.create_line(x0, y0, x1, y1, fill=color, width=width)
+
+##############################################################################
+### Keypress handling ########################################################
+##############################################################################
+
+# We bind to key-down and key-up events.
+
+_keysdown = {}
+_keyswaiting = {}
+# This holds an unprocessed key release.  We delay key releases by up to
+# one call to keys_pressed() to get round a problem with auto repeat.
+_got_release = None
+
+def _keypress(event):
+    global _got_release
+    #remap_arrows(event)
+    _keysdown[event.keysym] = 1
+    _keyswaiting[event.keysym] = 1
+#    print event.char, event.keycode
+    _got_release = None
+
+def _keyrelease(event):
+    global _got_release
+    #remap_arrows(event)
+    try:
+        del _keysdown[event.keysym]
+    except:
+        pass
+    _got_release = 1
+
+def remap_arrows(event):
+    # TURN ARROW PRESSES INTO LETTERS (SHOULD BE IN KEYBOARD AGENT)
+    if event.char in ['a', 's', 'd', 'w']:
+        return
+    if event.keycode in [37, 101]: # LEFT ARROW (win / x)
+        event.char = 'a'
+    if event.keycode in [38, 99]: # UP ARROW
+        event.char = 'w'
+    if event.keycode in [39, 102]: # RIGHT ARROW
+        event.char = 'd'
+    if event.keycode in [40, 104]: # DOWN ARROW
+        event.char = 's'
+
+def _clear_keys(event=None):
+    global _keysdown, _got_release, _keyswaiting
+    _keysdown = {}
+    _keyswaiting = {}
+    _got_release = None
+
+def keys_pressed(d_o_e=lambda arg: _root_window.dooneevent(arg),
+                 d_w=tkinter._tkinter.DONT_WAIT if tkinter else None):
+    d_o_e(d_w)
+    if _got_release:
+        d_o_e(d_w)
+    return _keysdown.keys()
+
+def keys_waiting():
+    global _keyswaiting
+    keys = _keyswaiting.keys()
+    _keyswaiting = {}
+    return keys
+
+# Block for a list of keys...
+
+def wait_for_keys():
+    keys = []
+    while keys == []:
+        keys = keys_pressed()
+        sleep(0.05)
+    return keys
+
+def remove_from_screen(x,
+                       d_o_e=lambda arg: _root_window.dooneevent(arg),
+                       d_w=tkinter._tkinter.DONT_WAIT if tkinter else None):
+    _canvas.delete(x)
+    d_o_e(d_w)
+
+def _adjust_coords(coord_list, x, y):
+    for i in range(0, len(coord_list), 2):
+        coord_list[i] = coord_list[i] + x
+        coord_list[i + 1] = coord_list[i + 1] + y
+    return coord_list
+
+def move_to(object, x, y=None,
+            d_o_e=lambda arg: _root_window.dooneevent(arg),
+            d_w=tkinter._tkinter.DONT_WAIT if tkinter else None):
+    if y is None:
+        try: x, y = x
+        except: raise  'incomprehensible coordinates'
+
+    horiz = True
+    newCoords = []
+    current_x, current_y = _canvas.coords(object)[0:2] # first point
+    for coord in  _canvas.coords(object):
+        if horiz:
+            inc = x - current_x
+        else:
+            inc = y - current_y
+        horiz = not horiz
+
+        newCoords.append(coord + inc)
+
+    _canvas.coords(object, *newCoords)
+    d_o_e(d_w)
+
+def move_by(object, x, y=None,
+            d_o_e=lambda arg: _root_window.dooneevent(arg),
+            d_w=tkinter._tkinter.DONT_WAIT if tkinter else None, lift=False):
+    if y is None:
+        try: x, y = x
+        except: raise Exception('incomprehensible coordinates')
+
+    horiz = True
+    newCoords = []
+    for coord in  _canvas.coords(object):
+        if horiz:
+            inc = x
+        else:
+            inc = y
+        horiz = not horiz
+
+        newCoords.append(coord + inc)
+
+    _canvas.coords(object, *newCoords)
+    d_o_e(d_w)
+    if lift:
+        _canvas.tag_raise(object)
+
+def writePostscript(filename):
+    "Writes the current canvas to a postscript file."
+    psfile = open(filename, 'w')
+    psfile.write(_canvas.postscript(pageanchor='sw',
+                     y='0.c',
+                     x='0.c'))
+    psfile.close()
+
+ghost_shape = [
+    (0, - 0.5),
+    (0.25, - 0.75),
+    (0.5, - 0.5),
+    (0.75, - 0.75),
+    (0.75, 0.5),
+    (0.5, 0.75),
+    (- 0.5, 0.75),
+    (- 0.75, 0.5),
+    (- 0.75, - 0.75),
+    (- 0.5, - 0.5),
+    (- 0.25, - 0.75)
+  ]
+
+if __name__ == '__main__':
+    begin_graphics()
+    clear_screen()
+    ghost_shape = [(x * 10 + 20, y * 10 + 20) for x, y in ghost_shape]
+    g = polygon(ghost_shape, formatColor(1, 1, 1))
+    move_to(g, (50, 50))
+    circle((150, 150), 20, formatColor(0.7, 0.3, 0.0), endpoints=[15, - 15])
+    sleep(2)

tracking/hunters.py

@@ -0,0 +1,558 @@
+# hunters.py
+# ----------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+"""
+Busters.py is a vengeful variant of Pacman where Pacman hunts ghosts, but
+cannot see them.  Numbers at the bottom of the display are noisy distance
+readings to each remaining ghost.
+
+To play your first game, type 'python pacman.py' from the command line.
+The keys are 'a', 's', 'd', and 'w' to move (or arrow keys).  Have fun!
+"""
+from game import GameStateData
+from game import Game
+from game import Directions
+from game import Actions
+from game import Configuration
+from game import Grid
+from util import nearestPoint
+from util import manhattanDistance
+import sys, util, types, time, random, layout, os
+
+WALL_STRING = '%'
+RED_WALL_STRING = 'R'
+BLUE_WALL_STRING = 'B'
+EMPTY_SQUARE_STRING = ' '
+
+GHOST_COLLISION_REWARD = -500
+WON_GAME_REWARD = 500
+
+###################################################
+# YOUR INTERFACE TO THE PACMAN WORLD: A GameState #
+###################################################
+
+class GameState:
+    """
+    A GameState specifies the full game state, including the food, capsules,
+    agent configurations and score changes.
+
+    GameStates are used by the Game object to capture the actual state of the game and
+    can be used by agents to reason about the game.
+
+    Much of the information in a GameState is stored in a GameStateData object.  We
+    strongly suggest that you access that data via the accessor methods below rather
+    than referring to the GameStateData object directly.
+
+    Note that in classic Pacman, Pacman is always agent 0.
+    """
+
+    ####################################################
+    # Accessor methods: use these to access state data #
+    ####################################################
+
+    def getLegalActions( self, agentIndex=0 ):
+        """
+        Returns the legal actions for the agent specified.
+        """
+        if self.isWin() or self.isLose(): return []
+
+        if agentIndex == 0:  # Pacman is moving
+            return PacmanRules.getLegalActions( self )
+        else:
+            return GhostRules.getLegalActions( self, agentIndex )
+
+    def generateSuccessor( self, agentIndex, action):
+        """
+        Returns the successor state after the specified agent takes the action.
+        """
+        # Check that successors exist
+        if self.isWin() or self.isLose(): raise Exception('Can\'t generate a successor of a terminal state.')
+
+        # Copy current state
+        state = GameState(self)
+
+        # Let agent's logic deal with its action's effects on the board
+        if agentIndex == 0:  # Pacman is moving
+            state.data._eaten = [False for i in range(state.getNumAgents())]
+            PacmanRules.applyAction( state, action )
+        else:                # A ghost is moving
+            GhostRules.applyAction( state, action, agentIndex )
+
+        # Time passes
+        if agentIndex == 0:
+            state.data.scoreChange += -TIME_PENALTY # Penalty for waiting around
+
+        # Resolve multi-agent effects
+        GhostRules.checkDeath( state, agentIndex )
+
+        # Book keeping
+        state.data._agentMoved = agentIndex
+        state.data.score += state.data.scoreChange
+
+        # Pacman observes neighboring squares
+        state.getObservation()
+
+        return state
+
+    def getLegalPacmanActions( self ):
+        return self.getLegalActions( 0 )
+
+    def generatePacmanSuccessor( self, action ):
+        """
+        Generates the successor state after the specified pacman move
+        """
+        return self.generateSuccessor( 0, action )
+
+    def getPacmanState( self ):
+        """
+        Returns an AgentState object for pacman (in game.py)
+
+        state.pos gives the current position
+        state.direction gives the travel vector
+        """
+        return self.data.agentStates[0].copy()
+
+    def getPacmanPosition( self ):
+        return self.data.agentStates[0].getPosition()
+
+    def getNumAgents( self ):
+        return len( self.data.agentStates )
+
+    def getScore( self ):
+        return self.data.score
+
+    def isLose( self ):
+        return self.data._lose
+
+    def isWin( self ):
+        return self.data._win
+
+    ################################
+    # Additions for Hunters Pacman #
+    ################################
+
+    def getObservation(self):
+        x, y = self.getPacmanPosition()
+        adjacent = [(x-1, y), (x+1, y), (x, y-1), (x, y+1)]
+        for x, y in adjacent:
+            self.data.observedPositions[x][y] = True
+        return { (x, y) : self.squareInfo(x, y) for x, y in adjacent }
+
+    def squareInfo(self, x, y):
+        if self.hasWall(x, y):
+            return WALL_STRING
+        elif self.data.layout.redWalls[x][y]:
+            return RED_WALL_STRING
+        elif self.data.layout.blueWalls[x][y]:
+            return BLUE_WALL_STRING
+        else:
+            return EMPTY_SQUARE_STRING
+
+    def getObservedPositions(self):
+        return self.data.observedPositions
+
+    def getHouseWalls(self, house):
+        return layout.buildHouseAroundCenter(*house)
+
+    def getPossibleHouses(self):
+        return layout.pickPossibleLocations(self.data.layout.width, self.data.layout.height)
+
+    def getEvidence(self):
+        evidence = {}
+        possible = {w for h in self.getPossibleHouses() for w in self.getHouseWalls(h)}
+        for pos in self.getObservedPositions().asList():
+            if pos in possible:
+                evidence[pos] = self.squareInfo(*pos)
+        return evidence
+
+    #############################################
+    #             Helper methods:               #
+    # You shouldn't need to call these directly #
+    #############################################
+
+    def __init__( self, prevState = None ):
+        """
+        Generates a new state by copying information from its predecessor.
+        """
+        if prevState is not None:
+            self.data = GameStateData(prevState.data)
+            self.numMoves = prevState.numMoves
+            self.maxMoves = prevState.maxMoves
+        else: # Initial state
+            self.data = GameStateData()
+            self.numMoves = 0
+            self.maxMoves = -1
+
+    def deepCopy( self ):
+        state = GameState( self )
+        state.data = self.data.deepCopy()
+        return state
+
+    def __eq__( self, other ):
+        """
+        Allows two states to be compared.
+        """
+        return self.data == other.data
+
+    def __hash__( self ):
+        """
+        Allows states to be keys of dictionaries.
+        """
+        return hash( str( self ) )
+
+    def __str__( self ):
+
+        return str(self.data)
+
+    def initialize( self, layout, numGhostAgents=1000 ):
+        """
+        Creates an initial game state from a layout array (see layout.py).
+        """
+        self.data.initialize(layout, numGhostAgents)
+
+    def getGhostPosition( self, agentIndex ):
+        if agentIndex == 0:
+            raise "Pacman's index passed to getGhostPosition"
+        return self.data.agentStates[agentIndex].getPosition()
+
+    def getGhostState( self, agentIndex ):
+        if agentIndex == 0:
+            raise "Pacman's index passed to getGhostPosition"
+        return self.data.agentStates[agentIndex]
+
+    def getCapsules(self):
+        """
+        Returns a list of positions (x,y) of the remaining capsules.
+        """
+        return self.data.capsules
+
+    def getNumFood( self ):
+        return self.data.food.count()
+
+    def getFood(self):
+        """
+        Returns a Grid of boolean food indicator variables.
+
+        Grids can be accessed via list notation, so to check
+        if there is food at (x,y), just call
+
+        currentFood = state.getFood()
+        if currentFood[x][y] == True: ...
+        """
+        return self.data.food
+
+    def getWalls(self):
+        """
+        Returns a Grid of boolean wall indicator variables.
+
+        Grids can be accessed via list notation, so to check
+        if there is food at (x,y), just call
+
+        walls = state.getWalls()
+        if walls[x][y] == True: ...
+        """
+        return self.data.layout.walls
+
+    def hasFood(self, x, y):
+        return self.data.food[x][y]
+
+    def hasWall(self, x, y):
+        return self.data.layout.walls[x][y]
+
+############################################################################
+#                     THE HIDDEN SECRETS OF PACMAN                         #
+#                                                                          #
+# You shouldn't need to look through the code in this section of the file. #
+############################################################################
+
+COLLISION_TOLERANCE = 0.7 # How close ghosts must be to Pacman to kill
+TIME_PENALTY = 1 # Number of points lost each round
+
+class HuntersGameRules:
+    """
+    These game rules manage the control flow of a game, deciding when
+    and how the game starts and ends.
+    """
+
+    def newGame( self, layout, pacmanAgent, ghostAgents, display, maxMoves= -1 ):
+        agents = [pacmanAgent] + ghostAgents
+        initState = GameState()
+        initState.initialize( layout, len(ghostAgents) )
+        game = Game(agents, display, self)
+        game.state = initState
+        return game
+
+    def process(self, state, game):
+        """
+        Checks to see whether it is time to end the game.
+        """
+        if state.isWin(): self.win(state, game)
+        if state.isLose(): self.lose(state, game)
+
+    def win( self, state, game ):
+        print("Pacman found the food! Score: %d" % state.data.score)
+        game.gameOver = True
+
+    def lose( self, state, game ):
+        print("Uh oh, You found the Princess! Wraaaang she's in another house go away. Score: %d" % state.data.score)
+        game.gameOver = True
+
+class PacmanRules:
+    """
+    These functions govern how pacman interacts with his environment under
+    the classic game rules.
+    """
+    def getLegalActions( state ):
+        """
+        Returns a list of possible actions.
+        """
+        return Actions.getPossibleActions( state.getPacmanState().configuration, state.data.layout.walls, state.data.layout.redWalls, state.data.layout.blueWalls )
+    getLegalActions = staticmethod( getLegalActions )
+
+    def applyAction( state, action ):
+        """
+        Edits the state to reflect the results of the action.
+        """
+        legal = PacmanRules.getLegalActions( state )
+        if action not in legal:
+            raise Exception("Illegal action " + str(action))
+
+        pacmanState = state.data.agentStates[0]
+
+        # Update Configuration
+        vector = Actions.directionToVector( action, 1 )
+        pacmanState.configuration = pacmanState.configuration.generateSuccessor( vector )
+
+        # Eat
+        next = pacmanState.configuration.getPosition()
+        nearest = nearestPoint( next )
+        if manhattanDistance( nearest, next ) <= 0.5 :
+            # Remove food
+            PacmanRules.consume( nearest, state )
+
+    applyAction = staticmethod( applyAction )
+
+    def consume( position, state ):
+        x,y = position
+        # Eat food
+        if state.data.food[x][y]:
+            state.data.scoreChange += 10
+            state.data.food = state.data.food.copy()
+            state.data.food[x][y] = False
+            state.data._foodEaten = position
+
+            numFood = state.getNumFood()
+            if numFood == 0 and not state.data._lose:
+                state.data.scoreChange += WON_GAME_REWARD
+                state.data._win = True
+
+    consume = staticmethod( consume )
+
+class GhostRules:
+    """
+    These functions dictate how ghosts interact with their environment.
+    """
+    def applyAction( state, action, ghostIndex):
+        if action != Directions.STOP:
+            raise Exception("Illegal ghost action " + str(action))
+
+        ghostState = state.data.agentStates[ghostIndex]
+        vector = Actions.directionToVector( action, 1 )
+        ghostState.configuration = ghostState.configuration.generateSuccessor( vector )
+    applyAction = staticmethod( applyAction )
+
+    def checkDeath( state, agentIndex):
+        pacmanPosition = state.getPacmanPosition()
+        if agentIndex == 0: # Pacman just moved; Anyone can kill him
+            for index in range( 1, len( state.data.agentStates ) ):
+                ghostState = state.data.agentStates[index]
+                ghostPosition = ghostState.configuration.getPosition()
+                if GhostRules.canKill( pacmanPosition, ghostPosition ):
+                    GhostRules.collide( state, ghostState, index )
+        else:
+            ghostState = state.data.agentStates[agentIndex]
+            ghostPosition = ghostState.configuration.getPosition()
+            if GhostRules.canKill( pacmanPosition, ghostPosition ):
+                GhostRules.collide( state, ghostState, agentIndex )
+    checkDeath = staticmethod( checkDeath )
+
+    def collide( state, ghostState, agentIndex):
+        state.data.scoreChange += GHOST_COLLISION_REWARD
+        state.data._lose = True
+
+    collide = staticmethod( collide )
+
+    def canKill( pacmanPosition, ghostPosition ):
+        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE
+    canKill = staticmethod( canKill )
+
+    def placeGhost(state, ghostState):
+        ghostState.configuration = ghostState.start
+    placeGhost = staticmethod( placeGhost )
+
+#############################
+# FRAMEWORK TO START A GAME #
+#############################
+
+def default(str):
+    return str + ' [Default: %default]'
+
+def parseAgentArgs(str):
+    if str == None: return {}
+    pieces = str.split(',')
+    opts = {}
+    for p in pieces:
+        if '=' in p:
+            key, val = p.split('=')
+        else:
+            key,val = p, 1
+        opts[key] = val
+    return opts
+
+def readCommand( argv ):
+    """
+    Processes the command used to run pacman from the command line.
+    """
+    from optparse import OptionParser
+    usageStr = """
+    USAGE:      python busters.py <options>
+    EXAMPLE:    python busters.py --layout bigHunt
+                  - starts an interactive game on a big board
+    """
+    parser = OptionParser(usageStr)
+
+    parser.add_option('-n', '--numGames', dest='numGames', type='int',
+                      help=default('the number of GAMES to play'), metavar='GAMES', default=1)
+    parser.add_option('-l', '--layout', dest='layout',
+                      help=default('the LAYOUT_FILE from which to load the map layout'),
+                      metavar='LAYOUT_FILE', default='treasureHunt')
+    parser.add_option('-p', '--pacman', dest='pacman',
+                      help=default('the agent TYPE in the pacmanAgents module to use'),
+                      metavar='TYPE', default='KeyboardAgent')
+    parser.add_option('-a','--agentArgs',dest='agentArgs',
+                      help='Comma seperated values sent to agent. e.g. "opt1=val1,opt2,opt3=val3"')
+    parser.add_option('-g', '--ghosts', dest='ghost',
+                      help=default('the ghost agent TYPE in the ghostAgents module to use'),
+                      metavar = 'TYPE', default='StationaryGhostAgent')
+    parser.add_option('-q', '--quietTextGraphics', action='store_true', dest='quietGraphics',
+                      help='Generate minimal output and no graphics', default=False)
+    parser.add_option('-k', '--numghosts', type='int', dest='numGhosts',
+                      help=default('The maximum number of ghosts to use'), default=1)
+    parser.add_option('-z', '--zoom', type='float', dest='zoom',
+                      help=default('Zoom the size of the graphics window'), default=1.0)
+    parser.add_option('-d', '--displayGhosts', action='store_true', dest='displayGhosts',
+                      help='Renders the ghosts in the display (cheating)', default=False)
+    parser.add_option('-t', '--frameTime', dest='frameTime', type='float',
+                      help=default('Time to delay between frames; <0 means keyboard'), default=0.1)
+    parser.add_option('-r', '--randomBoard', action='store_true', dest='randomBoard',
+                      help='Generates some random board', default=False)
+    parser.add_option('-v', '--vpiBoard', action='store_true', dest='vpiBoard',
+                      help='Generates a special board for the VPI sub-problem',
+                      default=False)
+    parser.add_option('-s', '--seed', type='str', dest='seed',
+                      help=default('Generates a random board using the specified seed'), default=None)
+
+    options, otherjunk = parser.parse_args()
+    if len(otherjunk) != 0:
+        raise Exception('Command line input not understood: ' + otherjunk)
+    args = dict()
+
+    if options.randomBoard or options.seed:
+        # options.seed defaults to None which uses system time
+        args['layout'] = layout.Layout(options.seed)
+    elif options.vpiBoard:
+        args['layout'] = layout.Layout(options.seed, vpi=True)
+    else:
+        # Choose a layout
+        args['layout'] = layout.getLayout( options.layout )
+        if args['layout'] == None: raise Exception("The layout " + options.layout + " cannot be found")
+
+    # Choose a ghost agent
+    ghostType = loadAgent(options.ghost, options.quietGraphics)
+    args['ghosts'] = [ghostType( i+1 ) for i in range( options.numGhosts )]
+
+    # Choose a Pacman agent
+    noKeyboard = options.quietGraphics
+    pacmanType = loadAgent(options.pacman, noKeyboard)
+    agentOpts = parseAgentArgs(options.agentArgs)
+    # agentOpts['ghostAgents'] = args['ghosts']
+    pacman = pacmanType(**agentOpts) # Instantiate Pacman with agentArgs
+    args['pacman'] = pacman
+
+    import graphicsDisplay
+    args['display'] = graphicsDisplay.FirstPersonPacmanGraphics(options.zoom, options.displayGhosts, frameTime = options.frameTime, hunters=True)
+    args['numGames'] = options.numGames
+
+    return args
+
+def loadAgent(pacman, nographics):
+    # Looks through all pythonPath Directories for the right module,
+    pythonPathStr = os.path.expandvars("$PYTHONPATH")
+    if pythonPathStr.find(';') == -1:
+        pythonPathDirs = pythonPathStr.split(':')
+    else:
+        pythonPathDirs = pythonPathStr.split(';')
+    pythonPathDirs.append('.')
+
+    for moduleDir in pythonPathDirs:
+        if not os.path.isdir(moduleDir): continue
+        moduleNames = [f for f in os.listdir(moduleDir) if f.endswith('gents.py')]
+        for modulename in moduleNames:
+            try:
+                module = __import__(modulename[:-3])
+            except ImportError:
+                continue
+            if pacman in dir(module):
+                if nographics and modulename == 'keyboardAgents.py':
+                    raise Exception('Using the keyboard requires graphics (not text display)')
+                return getattr(module, pacman)
+    raise Exception('The agent ' + pacman + ' is not specified in any *Agents.py.')
+
+def runGames( layout, pacman, ghosts, display, numGames, maxMoves=-1):
+    # Hack for agents writing to the display
+    import __main__
+    __main__.__dict__['_display'] = display
+
+    rules = HuntersGameRules()
+    games = []
+
+    for i in range( numGames ):
+        game = rules.newGame( layout, pacman, ghosts, display, maxMoves )
+        game.run()
+        games.append(game)
+
+    if numGames > 1:
+        scores = [game.state.getScore() for game in games]
+        wins = [game.state.isWin() for game in games]
+        winRate = wins.count(True)/ float(len(wins))
+        print('Average Score:', sum(scores) / float(len(scores)))
+        print('Scores:       ', ', '.join([str(score) for score in scores]))
+        print('Win Rate:      %d/%d (%.2f)' % (wins.count(True), len(wins), winRate))
+        print('Record:       ', ', '.join([ ['Loss', 'Win'][int(w)] for w in wins]))
+
+    return games
+
+if __name__ == '__main__':
+    """
+    The main function called when pacman.py is run
+    from the command line:
+
+    > python pacman.py
+
+    See the usage string for more details.
+
+    > python pacman.py --help
+    """
+    args = readCommand( sys.argv[1:] ) # Get game components based on input
+    runGames( **args )

tracking/inference.py

@@ -0,0 +1,636 @@
+# inference.py
+# ------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+import random
+import itertools
+from typing import List, Dict, Tuple
+import busters
+import game
+import bayesNet as bn
+from bayesNet import normalize
+import hunters
+from util import manhattanDistance, raiseNotDefined
+from factorOperations import joinFactorsByVariableWithCallTracking, joinFactors
+from factorOperations import eliminateWithCallTracking
+
+########### ########### ###########
+########### QUESTION 1  ###########
+########### ########### ###########
+
+def constructBayesNet(gameState: hunters.GameState):
+    """
+    Construct an empty Bayes net according to the structure given in Figure 1
+    of the project description.
+
+    You *must* name all variables using the constants in this function.
+
+    In this method, you should:
+    - populate `variables` with the Bayes Net nodes
+    - populate `edges` with every edge in the Bayes Net. we will represent each
+      edge as a tuple `(from, to)`.
+    - set each `variableDomainsDict[var] = values`, where `values` is a list
+      of the possible assignments to `var`.
+        - each agent position is a tuple (x, y) where x and y are 0-indexed
+        - each observed distance is a noisy Manhattan distance:
+          it's non-negative and |obs - true| <= MAX_NOISE
+    - this uses slightly simplified mechanics vs the ones used later for simplicity
+    """
+    # constants to use
+    PAC = "Pacman"
+    GHOST0 = "Ghost0"
+    GHOST1 = "Ghost1"
+    OBS0 = "Observation0"
+    OBS1 = "Observation1"
+    X_RANGE = gameState.getWalls().width
+    Y_RANGE = gameState.getWalls().height
+    MAX_NOISE = 7
+
+    variables = []
+    edges = []
+    variableDomainsDict = {}
+
+    "*** YOUR CODE HERE ***"
+    raiseNotDefined()
+    "*** END YOUR CODE HERE ***"
+
+    net = bn.constructEmptyBayesNet(variables, edges, variableDomainsDict)
+    return net
+
+
+def inferenceByEnumeration(bayesNet: bn, queryVariables: List[str], evidenceDict: Dict):
+    """
+    An inference by enumeration implementation provided as reference.
+    This function performs a probabilistic inference query that
+    returns the factor:
+
+    P(queryVariables | evidenceDict)
+
+    bayesNet:       The Bayes Net on which we are making a query.
+    queryVariables: A list of the variables which are unconditioned in
+                    the inference query.
+    evidenceDict:   An assignment dict {variable : value} for the
+                    variables which are presented as evidence
+                    (conditioned) in the inference query. 
+    """
+    callTrackingList = []
+    joinFactorsByVariable = joinFactorsByVariableWithCallTracking(callTrackingList)
+    eliminate = eliminateWithCallTracking(callTrackingList)
+
+    # initialize return variables and the variables to eliminate
+    evidenceVariablesSet = set(evidenceDict.keys())
+    queryVariablesSet = set(queryVariables)
+    eliminationVariables = (bayesNet.variablesSet() - evidenceVariablesSet) - queryVariablesSet
+
+    # grab all factors where we know the evidence variables (to reduce the size of the tables)
+    currentFactorsList = bayesNet.getAllCPTsWithEvidence(evidenceDict)
+
+    # join all factors by variable
+    for joinVariable in bayesNet.variablesSet():
+        currentFactorsList, joinedFactor = joinFactorsByVariable(currentFactorsList, joinVariable)
+        currentFactorsList.append(joinedFactor)
+
+    # currentFactorsList should contain the connected components of the graph now as factors, must join the connected components
+    fullJoint = joinFactors(currentFactorsList)
+
+    # marginalize all variables that aren't query or evidence
+    incrementallyMarginalizedJoint = fullJoint
+    for eliminationVariable in eliminationVariables:
+        incrementallyMarginalizedJoint = eliminate(incrementallyMarginalizedJoint, eliminationVariable)
+
+    fullJointOverQueryAndEvidence = incrementallyMarginalizedJoint
+
+    # normalize so that the probability sums to one
+    # the input factor contains only the query variables and the evidence variables, 
+    # both as unconditioned variables
+    queryConditionedOnEvidence = normalize(fullJointOverQueryAndEvidence)
+    # now the factor is conditioned on the evidence variables
+
+    # the order is join on all variables, then eliminate on all elimination variables
+    return queryConditionedOnEvidence
+
+########### ########### ###########
+########### QUESTION 4  ###########
+########### ########### ###########
+
+def inferenceByVariableEliminationWithCallTracking(callTrackingList=None):
+
+    def inferenceByVariableElimination(bayesNet: bn, queryVariables: List[str], evidenceDict: Dict, eliminationOrder: List[str]):
+        """
+        This function should perform a probabilistic inference query that
+        returns the factor:
+
+        P(queryVariables | evidenceDict)
+
+        It should perform inference by interleaving joining on a variable
+        and eliminating that variable, in the order of variables according
+        to eliminationOrder.  See inferenceByEnumeration for an example on
+        how to use these functions.
+
+        You need to use joinFactorsByVariable to join all of the factors 
+        that contain a variable in order for the autograder to 
+        recognize that you performed the correct interleaving of 
+        joins and eliminates.
+
+        If a factor that you are about to eliminate a variable from has 
+        only one unconditioned variable, you should not eliminate it 
+        and instead just discard the factor.  This is since the 
+        result of the eliminate would be 1 (you marginalize 
+        all of the unconditioned variables), but it is not a 
+        valid factor.  So this simplifies using the result of eliminate.
+
+        The sum of the probabilities should sum to one (so that it is a true 
+        conditional probability, conditioned on the evidence).
+
+        bayesNet:         The Bayes Net on which we are making a query.
+        queryVariables:   A list of the variables which are unconditioned
+                          in the inference query.
+        evidenceDict:     An assignment dict {variable : value} for the
+                          variables which are presented as evidence
+                          (conditioned) in the inference query. 
+        eliminationOrder: The order to eliminate the variables in.
+
+        Hint: BayesNet.getAllCPTsWithEvidence will return all the Conditional 
+        Probability Tables even if an empty dict (or None) is passed in for 
+        evidenceDict. In this case it will not specialize any variable domains 
+        in the CPTs.
+
+        Useful functions:
+        BayesNet.getAllCPTsWithEvidence
+        normalize
+        eliminate
+        joinFactorsByVariable
+        joinFactors
+        """
+
+        # this is for autograding -- don't modify
+        joinFactorsByVariable = joinFactorsByVariableWithCallTracking(callTrackingList)
+        eliminate             = eliminateWithCallTracking(callTrackingList)
+        if eliminationOrder is None: # set an arbitrary elimination order if None given
+            eliminationVariables = bayesNet.variablesSet() - set(queryVariables) -\
+                                   set(evidenceDict.keys())
+            eliminationOrder = sorted(list(eliminationVariables))
+
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+
+
+    return inferenceByVariableElimination
+
+inferenceByVariableElimination = inferenceByVariableEliminationWithCallTracking()
+
+def sampleFromFactorRandomSource(randomSource=None):
+    if randomSource is None:
+        randomSource = random.Random()
+
+    def sampleFromFactor(factor, conditionedAssignments=None):
+        """
+        Sample an assignment for unconditioned variables in factor with
+        probability equal to the probability in the row of factor
+        corresponding to that assignment.
+
+        factor:                 The factor to sample from.
+        conditionedAssignments: A dict of assignments for all conditioned
+                                variables in the factor.  Can only be None
+                                if there are no conditioned variables in
+                                factor, otherwise must be nonzero.
+
+        Useful for inferenceByLikelihoodWeightingSampling
+
+        Returns an assignmentDict that contains the conditionedAssignments but 
+        also a random assignment of the unconditioned variables given their 
+        probability.
+        """
+        if conditionedAssignments is None and len(factor.conditionedVariables()) > 0:
+            raise ValueError("Conditioned assignments must be provided since \n" +
+                            "this factor has conditionedVariables: " + "\n" +
+                            str(factor.conditionedVariables()))
+
+        elif conditionedAssignments is not None:
+            conditionedVariables = set([var for var in conditionedAssignments.keys()])
+
+            if not conditionedVariables.issuperset(set(factor.conditionedVariables())):
+                raise ValueError("Factor's conditioned variables need to be a subset of the \n"
+                                    + "conditioned assignments passed in. \n" + \
+                                "conditionedVariables: " + str(conditionedVariables) + "\n" +
+                                "factor.conditionedVariables: " + str(set(factor.conditionedVariables())))
+
+            # Reduce the domains of the variables that have been
+            # conditioned upon for this factor 
+            newVariableDomainsDict = factor.variableDomainsDict()
+            for (var, assignment) in conditionedAssignments.items():
+                newVariableDomainsDict[var] = [assignment]
+
+            # Get the (hopefully) smaller conditional probability table
+            # for this variable 
+            CPT = factor.specializeVariableDomains(newVariableDomainsDict)
+        else:
+            CPT = factor
+        
+        # Get the probability of each row of the table (along with the
+        # assignmentDict that it corresponds to)
+        assignmentDicts = sorted([assignmentDict for assignmentDict in CPT.getAllPossibleAssignmentDicts()])
+        assignmentDictProbabilities = [CPT.getProbability(assignmentDict) for assignmentDict in assignmentDicts]
+
+        # calculate total probability in the factor and index each row by the 
+        # cumulative sum of probability up to and including that row
+        currentProbability = 0.0
+        probabilityRange = []
+        for i in range(len(assignmentDicts)):
+            currentProbability += assignmentDictProbabilities[i]
+            probabilityRange.append(currentProbability)
+
+        totalProbability = probabilityRange[-1]
+
+        # sample an assignment with probability equal to the probability in the row 
+        # for that assignment in the factor
+        pick = randomSource.uniform(0.0, totalProbability)
+        for i in range(len(assignmentDicts)):
+            if pick <= probabilityRange[i]:
+                return assignmentDicts[i]
+
+    return sampleFromFactor
+
+sampleFromFactor = sampleFromFactorRandomSource()
+
+class DiscreteDistribution(dict):
+    """
+    A DiscreteDistribution models belief distributions and weight distributions
+    over a finite set of discrete keys.
+    """
+    def __getitem__(self, key):
+        self.setdefault(key, 0)
+        return dict.__getitem__(self, key)
+
+    def copy(self):
+        """
+        Return a copy of the distribution.
+        """
+        return DiscreteDistribution(dict.copy(self))
+
+    def argMax(self):
+        """
+        Return the key with the highest value.
+        """
+        if len(self.keys()) == 0:
+            return None
+        all = list(self.items())
+        values = [x[1] for x in all]
+        maxIndex = values.index(max(values))
+        return all[maxIndex][0]
+
+    def total(self):
+        """
+        Return the sum of values for all keys.
+        """
+        return float(sum(self.values()))
+    
+    ########### ########### ###########
+    ########### QUESTION 5a ###########
+    ########### ########### ###########
+
+    def normalize(self):
+        """
+        Normalize the distribution such that the total value of all keys sums
+        to 1. The ratio of values for all keys will remain the same. In the case
+        where the total value of the distribution is 0, do nothing.
+
+        >>> dist = DiscreteDistribution()
+        >>> dist['a'] = 1
+        >>> dist['b'] = 2
+        >>> dist['c'] = 2
+        >>> dist['d'] = 0
+        >>> dist.normalize()
+        >>> list(sorted(dist.items()))
+        [('a', 0.2), ('b', 0.4), ('c', 0.4), ('d', 0.0)]
+        >>> dist['e'] = 4
+        >>> list(sorted(dist.items()))
+        [('a', 0.2), ('b', 0.4), ('c', 0.4), ('d', 0.0), ('e', 4)]
+        >>> empty = DiscreteDistribution()
+        >>> empty.normalize()
+        >>> empty
+        {}
+        """
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+
+    def sample(self):
+        """
+        Draw a random sample from the distribution and return the key, weighted
+        by the values associated with each key.
+
+        >>> dist = DiscreteDistribution()
+        >>> dist['a'] = 1
+        >>> dist['b'] = 2
+        >>> dist['c'] = 2
+        >>> dist['d'] = 0
+        >>> N = 100000.0
+        >>> samples = [dist.sample() for _ in range(int(N))]
+        >>> round(samples.count('a') * 1.0/N, 1)  # proportion of 'a'
+        0.2
+        >>> round(samples.count('b') * 1.0/N, 1)
+        0.4
+        >>> round(samples.count('c') * 1.0/N, 1)
+        0.4
+        >>> round(samples.count('d') * 1.0/N, 1)
+        0.0
+        """
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+
+
+class InferenceModule:
+    """
+    An inference module tracks a belief distribution over a ghost's location.
+    """
+    ############################################
+    # Useful methods for all inference modules #
+    ############################################
+
+    def __init__(self, ghostAgent):
+        """
+        Set the ghost agent for later access.
+        """
+        self.ghostAgent = ghostAgent
+        self.index = ghostAgent.index
+        self.obs = []  # most recent observation position
+
+    def getJailPosition(self):
+        return (2 * self.ghostAgent.index - 1, 1)
+
+    def getPositionDistributionHelper(self, gameState, pos, index, agent):
+        try:
+            jail = self.getJailPosition()
+            gameState = self.setGhostPosition(gameState, pos, index + 1)
+        except TypeError:
+            jail = self.getJailPosition(index)
+            gameState = self.setGhostPositions(gameState, pos)
+        pacmanPosition = gameState.getPacmanPosition()
+        ghostPosition = gameState.getGhostPosition(index + 1)  # The position you set
+        dist = DiscreteDistribution()
+        if pacmanPosition == ghostPosition:  # The ghost has been caught!
+            dist[jail] = 1.0
+            return dist
+        pacmanSuccessorStates = game.Actions.getLegalNeighbors(pacmanPosition, \
+                gameState.getWalls())  # Positions Pacman can move to
+        if ghostPosition in pacmanSuccessorStates:  # Ghost could get caught
+            mult = 1.0 / float(len(pacmanSuccessorStates))
+            dist[jail] = mult
+        else:
+            mult = 0.0
+        actionDist = agent.getDistribution(gameState)
+        for action, prob in actionDist.items():
+            successorPosition = game.Actions.getSuccessor(ghostPosition, action)
+            if successorPosition in pacmanSuccessorStates:  # Ghost could get caught
+                denom = float(len(actionDist))
+                dist[jail] += prob * (1.0 / denom) * (1.0 - mult)
+                dist[successorPosition] = prob * ((denom - 1.0) / denom) * (1.0 - mult)
+            else:
+                dist[successorPosition] = prob * (1.0 - mult)
+        return dist
+
+    def getPositionDistribution(self, gameState, pos, index=None, agent=None):
+        """
+        Return a distribution over successor positions of the ghost from the
+        given gameState. You must first place the ghost in the gameState, using
+        setGhostPosition below.
+        """
+        if index == None:
+            index = self.index - 1
+        if agent == None:
+            agent = self.ghostAgent
+        return self.getPositionDistributionHelper(gameState, pos, index, agent)
+    
+    ########### ########### ###########
+    ########### QUESTION 5b ###########
+    ########### ########### ###########
+
+    def getObservationProb(self, noisyDistance: int, pacmanPosition: Tuple, ghostPosition: Tuple, jailPosition: Tuple):
+        """
+        Return the probability P(noisyDistance | pacmanPosition, ghostPosition).
+        """
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+
+    def setGhostPosition(self, gameState, ghostPosition, index):
+        """
+        Set the position of the ghost for this inference module to the specified
+        position in the supplied gameState.
+
+        Note that calling setGhostPosition does not change the position of the
+        ghost in the GameState object used for tracking the true progression of
+        the game.  The code in inference.py only ever receives a deep copy of
+        the GameState object which is responsible for maintaining game state,
+        not a reference to the original object.  Note also that the ghost
+        distance observations are stored at the time the GameState object is
+        created, so changing the position of the ghost will not affect the
+        functioning of observe.
+        """
+        conf = game.Configuration(ghostPosition, game.Directions.STOP)
+        gameState.data.agentStates[index] = game.AgentState(conf, False)
+        return gameState
+
+    def setGhostPositions(self, gameState, ghostPositions):
+        """
+        Sets the position of all ghosts to the values in ghostPositions.
+        """
+        for index, pos in enumerate(ghostPositions):
+            conf = game.Configuration(pos, game.Directions.STOP)
+            gameState.data.agentStates[index + 1] = game.AgentState(conf, False)
+        return gameState
+
+    def observe(self, gameState):
+        """
+        Collect the relevant noisy distance observation and pass it along.
+        """
+        distances = gameState.getNoisyGhostDistances()
+        if len(distances) >= self.index:  # Check for missing observations
+            obs = distances[self.index - 1]
+            self.obs = obs
+            self.observeUpdate(obs, gameState)
+
+    def initialize(self, gameState):
+        """
+        Initialize beliefs to a uniform distribution over all legal positions.
+        """
+        self.legalPositions = [p for p in gameState.getWalls().asList(False) if p[1] > 1]
+        self.allPositions = self.legalPositions + [self.getJailPosition()]
+        self.initializeUniformly(gameState)
+
+    ######################################
+    # Methods that need to be overridden #
+    ######################################
+
+    def initializeUniformly(self, gameState):
+        """
+        Set the belief state to a uniform prior belief over all positions.
+        """
+        raise NotImplementedError
+
+    def observeUpdate(self, observation, gameState):
+        """
+        Update beliefs based on the given distance observation and gameState.
+        """
+        raise NotImplementedError
+
+    def elapseTime(self, gameState):
+        """
+        Predict beliefs for the next time step from a gameState.
+        """
+        raise NotImplementedError
+
+    def getBeliefDistribution(self):
+        """
+        Return the agent's current belief state, a distribution over ghost
+        locations conditioned on all evidence so far.
+        """
+        raise NotImplementedError
+
+
+class ExactInference(InferenceModule):
+    """
+    The exact dynamic inference module should use forward algorithm updates to
+    compute the exact belief function at each time step.
+    """
+    def initializeUniformly(self, gameState):
+        """
+        Begin with a uniform distribution over legal ghost positions (i.e., not
+        including the jail position).
+        """
+        self.beliefs = DiscreteDistribution()
+        for p in self.legalPositions:
+            self.beliefs[p] = 1.0
+        self.beliefs.normalize()
+    
+    ########### ########### ###########
+    ########### QUESTION 6  ###########
+    ########### ########### ###########
+
+    def observeUpdate(self, observation: int, gameState: busters.GameState):
+        """
+        Update beliefs based on the distance observation and Pacman's position.
+
+        The observation is the noisy Manhattan distance to the ghost you are
+        tracking.
+
+        self.allPositions is a list of the possible ghost positions, including
+        the jail position. You should only consider positions that are in
+        self.allPositions.
+
+        The update model is not entirely stationary: it may depend on Pacman's
+        current position. However, this is not a problem, as Pacman's current
+        position is known.
+        """
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+        self.beliefs.normalize()
+    
+    ########### ########### ###########
+    ########### QUESTION 7  ###########
+    ########### ########### ###########
+
+    def elapseTime(self, gameState: busters.GameState):
+        """
+        Predict beliefs in response to a time step passing from the current
+        state.
+
+        The transition model is not entirely stationary: it may depend on
+        Pacman's current position. However, this is not a problem, as Pacman's
+        current position is known.
+        """
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+
+    def getBeliefDistribution(self):
+        return self.beliefs
+
+
+class ParticleFilter(InferenceModule):
+    """
+    A particle filter for approximately tracking a single ghost.
+    """
+    def __init__(self, ghostAgent, numParticles=300):
+        InferenceModule.__init__(self, ghostAgent)
+        self.setNumParticles(numParticles)
+
+    def setNumParticles(self, numParticles):
+        self.numParticles = numParticles
+    
+    ########### ########### ###########
+    ########### QUESTION 9  ###########
+    ########### ########### ###########
+
+    def initializeUniformly(self, gameState: busters.GameState):
+        """
+        Initialize a list of particles. Use self.numParticles for the number of
+        particles. Use self.legalPositions for the legal board positions where
+        a particle could be located. Particles should be evenly (not randomly)
+        distributed across positions in order to ensure a uniform prior. Use
+        self.particles for the list of particles.
+        """
+        self.particles = []
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+
+    def getBeliefDistribution(self):
+        """
+        Return the agent's current belief state, a distribution over ghost
+        locations conditioned on all evidence and time passage. This method
+        essentially converts a list of particles into a belief distribution.
+
+        This function should return a normalized distribution.
+        """
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+    
+    ########### ########### ###########
+    ########### QUESTION 10 ###########
+    ########### ########### ###########
+
+    def observeUpdate(self, observation: int, gameState: busters.GameState):
+        """
+        Update beliefs based on the distance observation and Pacman's position.
+
+        The observation is the noisy Manhattan distance to the ghost you are
+        tracking.
+
+        There is one special case that a correct implementation must handle.
+        When all particles receive zero weight, the list of particles should
+        be reinitialized by calling initializeUniformly. The total method of
+        the DiscreteDistribution may be useful.
+        """
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+    
+    ########### ########### ###########
+    ########### QUESTION 11 ###########
+    ########### ########### ###########
+
+    def elapseTime(self, gameState):
+        """
+        Sample each particle's next state based on its current state and the
+        gameState.
+        """
+        "*** YOUR CODE HERE ***"
+        raiseNotDefined()
+        "*** END YOUR CODE HERE ***"
+

tracking/keyboardAgents.py

@@ -0,0 +1,84 @@
+# keyboardAgents.py
+# -----------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+from game import Agent
+from game import Directions
+import random
+
+class KeyboardAgent(Agent):
+    """
+    An agent controlled by the keyboard.
+    """
+    # NOTE: Arrow keys also work.
+    WEST_KEY  = 'a'
+    EAST_KEY  = 'd'
+    NORTH_KEY = 'w'
+    SOUTH_KEY = 's'
+    STOP_KEY = 'q'
+
+    def __init__( self, index = 0 ):
+
+        self.lastMove = Directions.STOP
+        self.index = index
+        self.keys = []
+
+    def getAction( self, state):
+        from graphicsUtils import keys_waiting
+        from graphicsUtils import keys_pressed
+        keys = list(keys_waiting()) + list(keys_pressed())
+        if keys != []:
+            self.keys = keys
+
+        legal = state.getLegalActions(self.index)
+        move = self.getMove(legal)
+
+        if move == Directions.STOP:
+            # Try to move in the same direction as before
+            if self.lastMove in legal:
+                move = self.lastMove
+
+        if (self.STOP_KEY in self.keys) and Directions.STOP in legal: move = Directions.STOP
+
+        if move not in legal:
+            move = random.choice(legal)
+
+        self.lastMove = move
+        return move
+
+    def getMove(self, legal):
+        move = Directions.STOP
+        if   (self.WEST_KEY in self.keys or 'Left' in self.keys) and Directions.WEST in legal:  move = Directions.WEST
+        if   (self.EAST_KEY in self.keys or 'Right' in self.keys) and Directions.EAST in legal: move = Directions.EAST
+        if   (self.NORTH_KEY in self.keys or 'Up' in self.keys) and Directions.NORTH in legal:   move = Directions.NORTH
+        if   (self.SOUTH_KEY in self.keys or 'Down' in self.keys) and Directions.SOUTH in legal: move = Directions.SOUTH
+        return move
+
+class KeyboardAgent2(KeyboardAgent):
+    """
+    A second agent controlled by the keyboard.
+    """
+    # NOTE: Arrow keys also work.
+    WEST_KEY  = 'j'
+    EAST_KEY  = "l"
+    NORTH_KEY = 'i'
+    SOUTH_KEY = 'k'
+    STOP_KEY = 'u'
+
+    def getMove(self, legal):
+        move = Directions.STOP
+        if   (self.WEST_KEY in self.keys) and Directions.WEST in legal:  move = Directions.WEST
+        if   (self.EAST_KEY in self.keys) and Directions.EAST in legal: move = Directions.EAST
+        if   (self.NORTH_KEY in self.keys) and Directions.NORTH in legal:   move = Directions.NORTH
+        if   (self.SOUTH_KEY in self.keys) and Directions.SOUTH in legal: move = Directions.SOUTH
+        return move

tracking/layout.py

@@ -0,0 +1,150 @@
+# layout.py
+# ---------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+from util import manhattanDistance
+from game import Grid
+import os
+import random
+from functools import reduce
+
+VISIBILITY_MATRIX_CACHE = {}
+
+class Layout:
+    """
+    A Layout manages the static information about the game board.
+    """
+
+    def __init__(self, layoutText):
+        self.width = len(layoutText[0])
+        self.height= len(layoutText)
+        self.walls = Grid(self.width, self.height, False)
+        self.food = Grid(self.width, self.height, False)
+        self.capsules = []
+        self.agentPositions = []
+        self.numGhosts = 0
+        self.processLayoutText(layoutText)
+        self.layoutText = layoutText
+        self.totalFood = len(self.food.asList())
+        # self.initializeVisibilityMatrix()
+
+    def getNumGhosts(self):
+        return self.numGhosts
+
+    def initializeVisibilityMatrix(self):
+        global VISIBILITY_MATRIX_CACHE
+        if reduce(str.__add__, self.layoutText) not in VISIBILITY_MATRIX_CACHE:
+            from game import Directions
+            vecs = [(-0.5,0), (0.5,0),(0,-0.5),(0,0.5)]
+            dirs = [Directions.NORTH, Directions.SOUTH, Directions.WEST, Directions.EAST]
+            vis = Grid(self.width, self.height, {Directions.NORTH:set(), Directions.SOUTH:set(), Directions.EAST:set(), Directions.WEST:set(), Directions.STOP:set()})
+            for x in range(self.width):
+                for y in range(self.height):
+                    if self.walls[x][y] == False:
+                        for vec, direction in zip(vecs, dirs):
+                            dx, dy = vec
+                            nextx, nexty = x + dx, y + dy
+                            while (nextx + nexty) != int(nextx) + int(nexty) or not self.walls[int(nextx)][int(nexty)] :
+                                vis[x][y][direction].add((nextx, nexty))
+                                nextx, nexty = x + dx, y + dy
+            self.visibility = vis
+            VISIBILITY_MATRIX_CACHE[reduce(str.__add__, self.layoutText)] = vis
+        else:
+            self.visibility = VISIBILITY_MATRIX_CACHE[reduce(str.__add__, self.layoutText)]
+
+    def isWall(self, pos):
+        x, col = pos
+        return self.walls[x][col]
+
+    def getRandomLegalPosition(self):
+        x = random.choice(range(self.width))
+        y = random.choice(range(self.height))
+        while self.isWall( (x, y) ):
+            x = random.choice(range(self.width))
+            y = random.choice(range(self.height))
+        return (x,y)
+
+    def getRandomCorner(self):
+        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
+        return random.choice(poses)
+
+    def getFurthestCorner(self, pacPos):
+        poses = [(1,1), (1, self.height - 2), (self.width - 2, 1), (self.width - 2, self.height - 2)]
+        dist, pos = max([(manhattanDistance(p, pacPos), p) for p in poses])
+        return pos
+
+    def isVisibleFrom(self, ghostPos, pacPos, pacDirection):
+        row, col = [int(x) for x in pacPos]
+        return ghostPos in self.visibility[row][col][pacDirection]
+
+    def __str__(self):
+        return "\n".join(self.layoutText)
+
+    def deepCopy(self):
+        return Layout(self.layoutText[:])
+
+    def processLayoutText(self, layoutText):
+        """
+        Coordinates are flipped from the input format to the (x,y) convention here
+
+        The shape of the maze.  Each character
+        represents a different type of object.
+         % - Wall
+         . - Food
+         o - Capsule
+         G - Ghost
+         P - Pacman
+        Other characters are ignored.
+        """
+        maxY = self.height - 1
+        for y in range(self.height):
+            for x in range(self.width):
+                layoutChar = layoutText[maxY - y][x]
+                self.processLayoutChar(x, y, layoutChar)
+        self.agentPositions.sort()
+        self.agentPositions = [ ( i == 0, pos) for i, pos in self.agentPositions]
+
+    def processLayoutChar(self, x, y, layoutChar):
+        if layoutChar == '%':
+            self.walls[x][y] = True
+        elif layoutChar == '.':
+            self.food[x][y] = True
+        elif layoutChar == 'o':
+            self.capsules.append((x, y))
+        elif layoutChar == 'P':
+            self.agentPositions.append( (0, (x, y) ) )
+        elif layoutChar in ['G']:
+            self.agentPositions.append( (1, (x, y) ) )
+            self.numGhosts += 1
+        elif layoutChar in  ['1', '2', '3', '4']:
+            self.agentPositions.append( (int(layoutChar), (x,y)))
+            self.numGhosts += 1
+def getLayout(name, back = 2):
+    if name.endswith('.lay'):
+        layout = tryToLoad('layouts/' + name)
+        if layout == None: layout = tryToLoad(name)
+    else:
+        layout = tryToLoad('layouts/' + name + '.lay')
+        if layout == None: layout = tryToLoad(name + '.lay')
+    if layout == None and back >= 0:
+        curdir = os.path.abspath('.')
+        os.chdir('..')
+        layout = getLayout(name, back -1)
+        os.chdir(curdir)
+    return layout
+
+def tryToLoad(fullname):
+    if(not os.path.exists(fullname)): return None
+    f = open(fullname)
+    try: return Layout([line.strip() for line in f])
+    finally: f.close()

tracking/layouts/bigHunt.lay

@@ -0,0 +1,20 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%P                        %
+% %%%%%%%%%%%%        %%% %
+%                 %%      %
+%                 %%      %
+%          %         %    %
+% %%%%%%   %%% %%    %  %G%
+%                    %%%%%%
+% %%%%%%    %        %    %
+% %    %    %             %
+% %  G %    %  %%%%%%%%   %
+% %    %    %             %
+% %    %    %  %%%%%%%%   %
+%           %    G        %
+%     %%    %  %%    %%   %
+%     %%    %             %
+%          G%             %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% % % % %%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%

tracking/layouts/oneHunt.lay

@@ -0,0 +1,16 @@
+%%%%%%%%%%%%%%%%%%%%
+%                  %
+%                  %
+%    G       G     %
+%                  %
+%           P      %
+%                  %
+%                  %
+%                  %
+%    G       G     %
+%                  %
+%                  %
+%                  %
+%%%%%%%%%%%%%%%%%%%%
+% % % % %%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%

tracking/layouts/openHunt.lay

@@ -0,0 +1,13 @@
+%%%%%%%%%%%%%%%%%%%%
+%P      G          %
+% %%%  %%%  %% %%% %
+%     G            %
+%          %       %
+%          %       %
+% %%%%%%   %%%G%%% %
+%     G            %
+%           %      %
+%           %      %
+%%%%%%%%%%%%%%%%%%%%
+% % % % %%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%

tracking/layouts/smallHunt.lay

@@ -0,0 +1,8 @@
+%%%%%%%%%%%%%%%%%%%%
+%P      G       G  %
+% %%%%% %%%%%% % % %
+%     G            %
+%          G       %
+%%%%%%%%%%%%%%%%%%%%
+% % % % %%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%

tracking/pacman.py

@@ -0,0 +1,684 @@
+# pacman.py
+# ---------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+"""
+Pacman.py holds the logic for the classic pacman game along with the main
+code to run a game.  This file is divided into three sections:
+
+  (i)  Your interface to the pacman world:
+          Pacman is a complex environment.  You probably don't want to
+          read through all of the code we wrote to make the game runs
+          correctly.  This section contains the parts of the code
+          that you will need to understand in order to complete the
+          project.  There is also some code in game.py that you should
+          understand.
+
+  (ii)  The hidden secrets of pacman:
+          This section contains all of the logic code that the pacman
+          environment uses to decide who can move where, who dies when
+          things collide, etc.  You shouldn't need to read this section
+          of code, but you can if you want.
+
+  (iii) Framework to start a game:
+          The final section contains the code for reading the command
+          you use to set up the game, then starting up a new game, along with
+          linking in all the external parts (agent functions, graphics).
+          Check this section out to see all the options available to you.
+
+To play your first game, type 'python pacman.py' from the command line.
+The keys are 'a', 's', 'd', and 'w' to move (or arrow keys).  Have fun!
+"""
+from game import GameStateData
+from game import Game
+from game import Directions
+from game import Actions
+from util import nearestPoint
+from util import manhattanDistance
+import util, layout
+import sys, types, time, random, os
+
+###################################################
+# YOUR INTERFACE TO THE PACMAN WORLD: A GameState #
+###################################################
+
+class GameState:
+    """
+    A GameState specifies the full game state, including the food, capsules,
+    agent configurations and score changes.
+
+    GameStates are used by the Game object to capture the actual state of the game and
+    can be used by agents to reason about the game.
+
+    Much of the information in a GameState is stored in a GameStateData object.  We
+    strongly suggest that you access that data via the accessor methods below rather
+    than referring to the GameStateData object directly.
+
+    Note that in classic Pacman, Pacman is always agent 0.
+    """
+
+    ####################################################
+    # Accessor methods: use these to access state data #
+    ####################################################
+
+    # static variable keeps track of which states have had getLegalActions called
+    explored = set()
+    def getAndResetExplored():
+        tmp = GameState.explored.copy()
+        GameState.explored = set()
+        return tmp
+    getAndResetExplored = staticmethod(getAndResetExplored)
+
+    def getLegalActions( self, agentIndex=0 ):
+        """
+        Returns the legal actions for the agent specified.
+        """
+#        GameState.explored.add(self)
+        if self.isWin() or self.isLose(): return []
+
+        if agentIndex == 0:  # Pacman is moving
+            return PacmanRules.getLegalActions( self )
+        else:
+            return GhostRules.getLegalActions( self, agentIndex )
+
+    def generateSuccessor( self, agentIndex, action):
+        """
+        Returns the successor state after the specified agent takes the action.
+        """
+        # Check that successors exist
+        if self.isWin() or self.isLose(): raise Exception('Can\'t generate a successor of a terminal state.')
+
+        # Copy current state
+        state = GameState(self)
+
+        # Let agent's logic deal with its action's effects on the board
+        if agentIndex == 0:  # Pacman is moving
+            state.data._eaten = [False for i in range(state.getNumAgents())]
+            PacmanRules.applyAction( state, action )
+        else:                # A ghost is moving
+            GhostRules.applyAction( state, action, agentIndex )
+
+        # Time passes
+        if agentIndex == 0:
+            state.data.scoreChange += -TIME_PENALTY # Penalty for waiting around
+        else:
+            GhostRules.decrementTimer( state.data.agentStates[agentIndex] )
+
+        # Resolve multi-agent effects
+        GhostRules.checkDeath( state, agentIndex )
+
+        # Book keeping
+        state.data._agentMoved = agentIndex
+        state.data.score += state.data.scoreChange
+        GameState.explored.add(self)
+        GameState.explored.add(state)
+        return state
+
+    def getLegalPacmanActions( self ):
+        return self.getLegalActions( 0 )
+
+    def generatePacmanSuccessor( self, action ):
+        """
+        Generates the successor state after the specified pacman move
+        """
+        return self.generateSuccessor( 0, action )
+
+    def getPacmanState( self ):
+        """
+        Returns an AgentState object for pacman (in game.py)
+
+        state.pos gives the current position
+        state.direction gives the travel vector
+        """
+        return self.data.agentStates[0].copy()
+
+    def getPacmanPosition( self ):
+        return self.data.agentStates[0].getPosition()
+
+    def getGhostStates( self ):
+        return self.data.agentStates[1:]
+
+    def getGhostState( self, agentIndex ):
+        if agentIndex == 0 or agentIndex >= self.getNumAgents():
+            raise Exception("Invalid index passed to getGhostState")
+        return self.data.agentStates[agentIndex]
+
+    def getGhostPosition( self, agentIndex ):
+        if agentIndex == 0:
+            raise Exception("Pacman's index passed to getGhostPosition")
+        return self.data.agentStates[agentIndex].getPosition()
+
+    def getGhostPositions(self):
+        return [s.getPosition() for s in self.getGhostStates()]
+
+    def getNumAgents( self ):
+        return len( self.data.agentStates )
+
+    def getScore( self ):
+        return float(self.data.score)
+
+    def getCapsules(self):
+        """
+        Returns a list of positions (x,y) of the remaining capsules.
+        """
+        return self.data.capsules
+
+    def getNumFood( self ):
+        return self.data.food.count()
+
+    def getFood(self):
+        """
+        Returns a Grid of boolean food indicator variables.
+
+        Grids can be accessed via list notation, so to check
+        if there is food at (x,y), just call
+
+        currentFood = state.getFood()
+        if currentFood[x][y] == True: ...
+        """
+        return self.data.food
+
+    def getWalls(self):
+        """
+        Returns a Grid of boolean wall indicator variables.
+
+        Grids can be accessed via list notation, so to check
+        if there is a wall at (x,y), just call
+
+        walls = state.getWalls()
+        if walls[x][y] == True: ...
+        """
+        return self.data.layout.walls
+
+    def hasFood(self, x, y):
+        return self.data.food[x][y]
+
+    def hasWall(self, x, y):
+        return self.data.layout.walls[x][y]
+
+    def isLose( self ):
+        return self.data._lose
+
+    def isWin( self ):
+        return self.data._win
+
+    #############################################
+    #             Helper methods:               #
+    # You shouldn't need to call these directly #
+    #############################################
+
+    def __init__( self, prevState = None ):
+        """
+        Generates a new state by copying information from its predecessor.
+        """
+        if prevState != None: # Initial state
+            self.data = GameStateData(prevState.data)
+        else:
+            self.data = GameStateData()
+
+    def deepCopy( self ):
+        state = GameState( self )
+        state.data = self.data.deepCopy()
+        return state
+
+    def __eq__( self, other ):
+        """
+        Allows two states to be compared.
+        """
+        return hasattr(other, 'data') and self.data == other.data
+
+    def __hash__( self ):
+        """
+        Allows states to be keys of dictionaries.
+        """
+        return hash( self.data )
+
+    def __str__( self ):
+
+        return str(self.data)
+
+    def initialize( self, layout, numGhostAgents=1000 ):
+        """
+        Creates an initial game state from a layout array (see layout.py).
+        """
+        self.data.initialize(layout, numGhostAgents)
+
+############################################################################
+#                     THE HIDDEN SECRETS OF PACMAN                         #
+#                                                                          #
+# You shouldn't need to look through the code in this section of the file. #
+############################################################################
+
+SCARED_TIME = 40    # Moves ghosts are scared
+COLLISION_TOLERANCE = 0.7 # How close ghosts must be to Pacman to kill
+TIME_PENALTY = 1 # Number of points lost each round
+
+class ClassicGameRules:
+    """
+    These game rules manage the control flow of a game, deciding when
+    and how the game starts and ends.
+    """
+    def __init__(self, timeout=30):
+        self.timeout = timeout
+
+    def newGame( self, layout, pacmanAgent, ghostAgents, display, quiet = False, catchExceptions=False):
+        agents = [pacmanAgent] + ghostAgents[:layout.getNumGhosts()]
+        initState = GameState()
+        initState.initialize( layout, len(ghostAgents) )
+        game = Game(agents, display, self, catchExceptions=catchExceptions)
+        game.state = initState
+        self.initialState = initState.deepCopy()
+        self.quiet = quiet
+        return game
+
+    def process(self, state, game):
+        """
+        Checks to see whether it is time to end the game.
+        """
+        if state.isWin(): self.win(state, game)
+        if state.isLose(): self.lose(state, game)
+
+    def win( self, state, game ):
+        if not self.quiet: print("Pacman emerges victorious! Score: %d" % state.data.score)
+        game.gameOver = True
+
+    def lose( self, state, game ):
+        if not self.quiet: print("Pacman died! Score: %d" % state.data.score)
+        game.gameOver = True
+
+    def getProgress(self, game):
+        return float(game.state.getNumFood()) / self.initialState.getNumFood()
+
+    def agentCrash(self, game, agentIndex):
+        if agentIndex == 0:
+            print("Pacman crashed")
+        else:
+            print("A ghost crashed")
+
+    def getMaxTotalTime(self, agentIndex):
+        return self.timeout
+
+    def getMaxStartupTime(self, agentIndex):
+        return self.timeout
+
+    def getMoveWarningTime(self, agentIndex):
+        return self.timeout
+
+    def getMoveTimeout(self, agentIndex):
+        return self.timeout
+
+    def getMaxTimeWarnings(self, agentIndex):
+        return 0
+
+class PacmanRules:
+    """
+    These functions govern how pacman interacts with his environment under
+    the classic game rules.
+    """
+    PACMAN_SPEED=1
+
+    def getLegalActions( state ):
+        """
+        Returns a list of possible actions.
+        """
+        return Actions.getPossibleActions( state.getPacmanState().configuration, state.data.layout.walls )
+    getLegalActions = staticmethod( getLegalActions )
+
+    def applyAction( state, action ):
+        """
+        Edits the state to reflect the results of the action.
+        """
+        legal = PacmanRules.getLegalActions( state )
+        if action not in legal:
+            raise Exception("Illegal action " + str(action))
+
+        pacmanState = state.data.agentStates[0]
+
+        # Update Configuration
+        vector = Actions.directionToVector( action, PacmanRules.PACMAN_SPEED )
+        pacmanState.configuration = pacmanState.configuration.generateSuccessor( vector )
+
+        # Eat
+        next = pacmanState.configuration.getPosition()
+        nearest = nearestPoint( next )
+        if manhattanDistance( nearest, next ) <= 0.5 :
+            # Remove food
+            PacmanRules.consume( nearest, state )
+    applyAction = staticmethod( applyAction )
+
+    def consume( position, state ):
+        x,y = position
+        # Eat food
+        if state.data.food[x][y]:
+            state.data.scoreChange += 10
+            state.data.food = state.data.food.copy()
+            state.data.food[x][y] = False
+            state.data._foodEaten = position
+            # TODO: cache numFood?
+            numFood = state.getNumFood()
+            if numFood == 0 and not state.data._lose:
+                state.data.scoreChange += 500
+                state.data._win = True
+        # Eat capsule
+        if( position in state.getCapsules() ):
+            state.data.capsules.remove( position )
+            state.data._capsuleEaten = position
+            # Reset all ghosts' scared timers
+            for index in range( 1, len( state.data.agentStates ) ):
+                state.data.agentStates[index].scaredTimer = SCARED_TIME
+    consume = staticmethod( consume )
+
+class GhostRules:
+    """
+    These functions dictate how ghosts interact with their environment.
+    """
+    GHOST_SPEED=1.0
+    def getLegalActions( state, ghostIndex ):
+        """
+        Ghosts cannot stop, and cannot turn around unless they
+        reach a dead end, but can turn 90 degrees at intersections.
+        """
+        conf = state.getGhostState( ghostIndex ).configuration
+        possibleActions = Actions.getPossibleActions( conf, state.data.layout.walls )
+        reverse = Actions.reverseDirection( conf.direction )
+        if Directions.STOP in possibleActions:
+            possibleActions.remove( Directions.STOP )
+        if reverse in possibleActions and len( possibleActions ) > 1:
+            possibleActions.remove( reverse )
+        return possibleActions
+    getLegalActions = staticmethod( getLegalActions )
+
+    def applyAction( state, action, ghostIndex):
+
+        legal = GhostRules.getLegalActions( state, ghostIndex )
+        if action not in legal:
+            raise Exception("Illegal ghost action " + str(action))
+
+        ghostState = state.data.agentStates[ghostIndex]
+        speed = GhostRules.GHOST_SPEED
+        if ghostState.scaredTimer > 0: speed /= 2.0
+        vector = Actions.directionToVector( action, speed )
+        ghostState.configuration = ghostState.configuration.generateSuccessor( vector )
+    applyAction = staticmethod( applyAction )
+
+    def decrementTimer( ghostState):
+        timer = ghostState.scaredTimer
+        if timer == 1:
+            ghostState.configuration.pos = nearestPoint( ghostState.configuration.pos )
+        ghostState.scaredTimer = max( 0, timer - 1 )
+    decrementTimer = staticmethod( decrementTimer )
+
+    def checkDeath( state, agentIndex):
+        pacmanPosition = state.getPacmanPosition()
+        if agentIndex == 0: # Pacman just moved; Anyone can kill him
+            for index in range( 1, len( state.data.agentStates ) ):
+                ghostState = state.data.agentStates[index]
+                ghostPosition = ghostState.configuration.getPosition()
+                if GhostRules.canKill( pacmanPosition, ghostPosition ):
+                    GhostRules.collide( state, ghostState, index )
+        else:
+            ghostState = state.data.agentStates[agentIndex]
+            ghostPosition = ghostState.configuration.getPosition()
+            if GhostRules.canKill( pacmanPosition, ghostPosition ):
+                GhostRules.collide( state, ghostState, agentIndex )
+    checkDeath = staticmethod( checkDeath )
+
+    def collide( state, ghostState, agentIndex):
+        if ghostState.scaredTimer > 0:
+            state.data.scoreChange += 200
+            GhostRules.placeGhost(state, ghostState)
+            ghostState.scaredTimer = 0
+            # Added for first-person
+            state.data._eaten[agentIndex] = True
+        else:
+            if not state.data._win:
+                state.data.scoreChange -= 500
+                state.data._lose = True
+    collide = staticmethod( collide )
+
+    def canKill( pacmanPosition, ghostPosition ):
+        return manhattanDistance( ghostPosition, pacmanPosition ) <= COLLISION_TOLERANCE
+    canKill = staticmethod( canKill )
+
+    def placeGhost(state, ghostState):
+        ghostState.configuration = ghostState.start
+    placeGhost = staticmethod( placeGhost )
+
+#############################
+# FRAMEWORK TO START A GAME #
+#############################
+
+def default(str):
+    return str + ' [Default: %default]'
+
+def parseAgentArgs(str):
+    if str == None: return {}
+    pieces = str.split(',')
+    opts = {}
+    for p in pieces:
+        if '=' in p:
+            key, val = p.split('=')
+        else:
+            key,val = p, 1
+        opts[key] = val
+    return opts
+
+def readCommand( argv ):
+    """
+    Processes the command used to run pacman from the command line.
+    """
+    from optparse import OptionParser
+    usageStr = """
+    USAGE:      python pacman.py <options>
+    EXAMPLES:   (1) python pacman.py
+                    - starts an interactive game
+                (2) python pacman.py --layout smallClassic --zoom 2
+                OR  python pacman.py -l smallClassic -z 2
+                    - starts an interactive game on a smaller board, zoomed in
+    """
+    parser = OptionParser(usageStr)
+
+    parser.add_option('-n', '--numGames', dest='numGames', type='int',
+                      help=default('the number of GAMES to play'), metavar='GAMES', default=1)
+    parser.add_option('-l', '--layout', dest='layout',
+                      help=default('the LAYOUT_FILE from which to load the map layout'),
+                      metavar='LAYOUT_FILE', default='mediumClassic')
+    parser.add_option('-p', '--pacman', dest='pacman',
+                      help=default('the agent TYPE in the pacmanAgents module to use'),
+                      metavar='TYPE', default='KeyboardAgent')
+    parser.add_option('-t', '--textGraphics', action='store_true', dest='textGraphics',
+                      help='Display output as text only', default=False)
+    parser.add_option('-q', '--quietTextGraphics', action='store_true', dest='quietGraphics',
+                      help='Generate minimal output and no graphics', default=False)
+    parser.add_option('-g', '--ghosts', dest='ghost',
+                      help=default('the ghost agent TYPE in the ghostAgents module to use'),
+                      metavar = 'TYPE', default='RandomGhost')
+    parser.add_option('-k', '--numghosts', type='int', dest='numGhosts',
+                      help=default('The maximum number of ghosts to use'), default=4)
+    parser.add_option('-z', '--zoom', type='float', dest='zoom',
+                      help=default('Zoom the size of the graphics window'), default=1.0)
+    parser.add_option('-f', '--fixRandomSeed', action='store_true', dest='fixRandomSeed',
+                      help='Fixes the random seed to always play the same game', default=False)
+    parser.add_option('-r', '--recordActions', action='store_true', dest='record',
+                      help='Writes game histories to a file (named by the time they were played)', default=False)
+    parser.add_option('--replay', dest='gameToReplay',
+                      help='A recorded game file (pickle) to replay', default=None)
+    parser.add_option('-a','--agentArgs',dest='agentArgs',
+                      help='Comma separated values sent to agent. e.g. "opt1=val1,opt2,opt3=val3"')
+    parser.add_option('-x', '--numTraining', dest='numTraining', type='int',
+                      help=default('How many episodes are training (suppresses output)'), default=0)
+    parser.add_option('--frameTime', dest='frameTime', type='float',
+                      help=default('Time to delay between frames; <0 means keyboard'), default=0.1)
+    parser.add_option('-c', '--catchExceptions', action='store_true', dest='catchExceptions',
+                      help='Turns on exception handling and timeouts during games', default=False)
+    parser.add_option('--timeout', dest='timeout', type='int',
+                      help=default('Maximum length of time an agent can spend computing in a single game'), default=30)
+
+    options, otherjunk = parser.parse_args(argv)
+    if len(otherjunk) != 0:
+        raise Exception('Command line input not understood: ' + str(otherjunk))
+    args = dict()
+
+    # Fix the random seed
+    if options.fixRandomSeed: random.seed('cs188')
+
+    # Choose a layout
+    args['layout'] = layout.getLayout( options.layout )
+    if args['layout'] == None: raise Exception("The layout " + options.layout + " cannot be found")
+
+    # Choose a Pacman agent
+    noKeyboard = options.gameToReplay == None and (options.textGraphics or options.quietGraphics)
+    pacmanType = loadAgent(options.pacman, noKeyboard)
+    agentOpts = parseAgentArgs(options.agentArgs)
+    if options.numTraining > 0:
+        args['numTraining'] = options.numTraining
+        if 'numTraining' not in agentOpts: agentOpts['numTraining'] = options.numTraining
+    pacman = pacmanType(**agentOpts) # Instantiate Pacman with agentArgs
+    args['pacman'] = pacman
+
+    # Don't display training games
+    if 'numTrain' in agentOpts:
+        options.numQuiet = int(agentOpts['numTrain'])
+        options.numIgnore = int(agentOpts['numTrain'])
+
+    # Choose a ghost agent
+    ghostType = loadAgent(options.ghost, noKeyboard)
+    args['ghosts'] = [ghostType( i+1 ) for i in range( options.numGhosts )]
+
+    # Choose a display format
+    if options.quietGraphics:
+        import textDisplay
+        args['display'] = textDisplay.NullGraphics()
+    elif options.textGraphics:
+        import textDisplay
+        textDisplay.SLEEP_TIME = options.frameTime
+        args['display'] = textDisplay.PacmanGraphics()
+    else:
+        import graphicsDisplay
+        args['display'] = graphicsDisplay.PacmanGraphics(options.zoom, frameTime = options.frameTime)
+    args['numGames'] = options.numGames
+    args['record'] = options.record
+    args['catchExceptions'] = options.catchExceptions
+    args['timeout'] = options.timeout
+
+    # Special case: recorded games don't use the runGames method or args structure
+    if options.gameToReplay != None:
+        print('Replaying recorded game %s.' % options.gameToReplay)
+        import pickle
+        f = open(options.gameToReplay)
+        try: recorded = pickle.load(f)
+        finally: f.close()
+        recorded['display'] = args['display']
+        replayGame(**recorded)
+        sys.exit(0)
+
+    return args
+
+def loadAgent(pacman, nographics):
+    # Looks through all pythonPath Directories for the right module,
+    pythonPathStr = os.path.expandvars("$PYTHONPATH")
+    if pythonPathStr.find(';') == -1:
+        pythonPathDirs = pythonPathStr.split(':')
+    else:
+        pythonPathDirs = pythonPathStr.split(';')
+    pythonPathDirs.append('.')
+
+    for moduleDir in pythonPathDirs:
+        if not os.path.isdir(moduleDir): continue
+        moduleNames = [f for f in os.listdir(moduleDir) if f.endswith('gents.py')]
+        for modulename in moduleNames:
+            try:
+                module = __import__(modulename[:-3])
+            except ImportError:
+                continue
+            if pacman in dir(module):
+                if nographics and modulename == 'keyboardAgents.py':
+                    raise Exception('Using the keyboard requires graphics (not text display)')
+                return getattr(module, pacman)
+    raise Exception('The agent ' + pacman + ' is not specified in any *Agents.py.')
+
+def replayGame( layout, actions, display ):
+    import pacmanAgents, ghostAgents
+    rules = ClassicGameRules()
+    agents = [pacmanAgents.GreedyAgent()] + [ghostAgents.RandomGhost(i+1) for i in range(layout.getNumGhosts())]
+    game = rules.newGame( layout, agents[0], agents[1:], display )
+    state = game.state
+    display.initialize(state.data)
+
+    for action in actions:
+            # Execute the action
+        state = state.generateSuccessor( *action )
+        # Change the display
+        display.update( state.data )
+        # Allow for game specific conditions (winning, losing, etc.)
+        rules.process(state, game)
+
+    display.finish()
+
+def runGames( layout, pacman, ghosts, display, numGames, record, numTraining = 0, catchExceptions=False, timeout=30 ):
+    import __main__
+    __main__.__dict__['_display'] = display
+
+    rules = ClassicGameRules(timeout)
+    games = []
+
+    for i in range( numGames ):
+        beQuiet = i < numTraining
+        if beQuiet:
+                # Suppress output and graphics
+            import textDisplay
+            gameDisplay = textDisplay.NullGraphics()
+            rules.quiet = True
+        else:
+            gameDisplay = display
+            rules.quiet = False
+        game = rules.newGame( layout, pacman, ghosts, gameDisplay, beQuiet, catchExceptions)
+        game.run()
+        if not beQuiet: games.append(game)
+
+        if record:
+            import time, pickle
+            fname = ('recorded-game-%d' % (i + 1)) +  '-'.join([str(t) for t in time.localtime()[1:6]])
+            f = open(fname, 'w')
+            components = {'layout': layout, 'actions': game.moveHistory}
+            pickle.dump(components, f)
+            f.close()
+
+    if (numGames-numTraining) > 0:
+        scores = [game.state.getScore() for game in games]
+        wins = [game.state.isWin() for game in games]
+        winRate = wins.count(True)/ float(len(wins))
+        print('Average Score:', sum(scores) / float(len(scores)))
+        print('Scores:       ', ', '.join([str(score) for score in scores]))
+        print('Win Rate:      %d/%d (%.2f)' % (wins.count(True), len(wins), winRate))
+        print('Record:       ', ', '.join([ ['Loss', 'Win'][int(w)] for w in wins]))
+
+    return games
+
+if __name__ == '__main__':
+    """
+    The main function called when pacman.py is run
+    from the command line:
+
+    > python pacman.py
+
+    See the usage string for more details.
+
+    > python pacman.py --help
+    """
+    args = readCommand( sys.argv[1:] ) # Get game components based on input
+    runGames( **args )
+
+    # import cProfile
+    # cProfile.run("runGames( **args )")
+    pass

tracking/pacmanAgents.py

@@ -0,0 +1,52 @@
+# pacmanAgents.py
+# ---------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+from pacman import Directions
+from game import Agent
+import random
+import game
+import util
+
+class LeftTurnAgent(game.Agent):
+    "An agent that turns left at every opportunity"
+
+    def getAction(self, state):
+        legal = state.getLegalPacmanActions()
+        current = state.getPacmanState().configuration.direction
+        if current == Directions.STOP: current = Directions.NORTH
+        left = Directions.LEFT[current]
+        if left in legal: return left
+        if current in legal: return current
+        if Directions.RIGHT[current] in legal: return Directions.RIGHT[current]
+        if Directions.LEFT[left] in legal: return Directions.LEFT[left]
+        return Directions.STOP
+
+class GreedyAgent(Agent):
+    def __init__(self, evalFn="scoreEvaluation"):
+        self.evaluationFunction = util.lookup(evalFn, globals())
+        assert self.evaluationFunction != None
+
+    def getAction(self, state):
+        # Generate candidate actions
+        legal = state.getLegalPacmanActions()
+        if Directions.STOP in legal: legal.remove(Directions.STOP)
+
+        successors = [(state.generateSuccessor(action), action) for action in legal]
+        scored = [(self.evaluationFunction(state), action) for state, action in successors]
+        bestScore = max(scored)[0]
+        bestActions = [pair[1] for pair in scored if pair[0] == bestScore]
+        return random.choice(bestActions)
+
+def scoreEvaluation(state):
+    return state.getScore()

tracking/projectParams.py

@@ -0,0 +1,18 @@
+# projectParams.py
+# ----------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+STUDENT_CODE_DEFAULT = 'factorOperations.py,inference.py,bustersAgents.py'
+PROJECT_TEST_CLASSES = 'bayesHMMTestClasses.py'
+PROJECT_NAME = 'Project 4: Bayes Nets and HMMs'
+BONUS_PIC = False

tracking/testClasses.py

@@ -0,0 +1,240 @@
+# testClasses.py
+# --------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+# import modules from python standard library
+import inspect
+import re
+import sys
+
+# BEGIN SOLUTION NO PROMPT
+def invertLayout(layout_text):
+    # Keep lower left fix as this is hardcoded in PositionSearchProblem (gah)
+    # as the goal.
+    lines = [l.strip() for l in layout_text.split('\n')]
+    h = len(lines)
+    w = len(lines[0])
+    tiles = {}
+    for y, line in enumerate(lines):
+        for x, tile in enumerate(line):
+            # (x,y)
+            # (0,0) -> (h,w)
+            # (0,h) -> (0,w)
+            tiles[h-1-y, w-1-x] = tile
+
+    new_lines = []
+    for y in range(w):
+        new_lines.append("")
+        for x in range(h):
+            new_lines[-1] += tiles[x,y]
+    #return layout_text
+    return "\n".join(new_lines)
+# END SOLUTION NO PROMPT
+
+# Class which models a question in a project.  Note that questions have a
+# maximum number of points they are worth, and are composed of a series of
+# test cases
+class Question(object):
+
+    def raiseNotDefined(self):
+        print('Method not implemented: %s' % inspect.stack()[1][3])
+        sys.exit(1)
+
+    def __init__(self, questionDict, display):
+        self.maxPoints = int(questionDict['max_points'])
+        self.testCases = []
+        self.display = display
+
+    def getDisplay(self):
+        return self.display
+
+    def getMaxPoints(self):
+        return self.maxPoints
+
+    # Note that 'thunk' must be a function which accepts a single argument,
+    # namely a 'grading' object
+    def addTestCase(self, testCase, thunk):
+        self.testCases.append((testCase, thunk))
+
+    def execute(self, grades):
+        self.raiseNotDefined()
+
+# Question in which all test cases must be passed in order to receive credit
+class PassAllTestsQuestion(Question):
+
+    def execute(self, grades):
+        # TODO: is this the right way to use grades?  The autograder doesn't seem to use it.
+        testsFailed = False
+        grades.assignZeroCredit()
+        for _, f in self.testCases:
+            if not f(grades):
+                testsFailed = True
+        if testsFailed:
+            grades.fail("Tests failed.")
+        else:
+            grades.assignFullCredit()
+
+class ExtraCreditPassAllTestsQuestion(Question):
+    def __init__(self, questionDict, display):
+        Question.__init__(self, questionDict, display)
+        self.extraPoints = int(questionDict['extra_points'])
+
+    def execute(self, grades):
+        # TODO: is this the right way to use grades?  The autograder doesn't seem to use it.
+        testsFailed = False
+        grades.assignZeroCredit()
+        for _, f in self.testCases:
+            if not f(grades):
+                testsFailed = True
+        if testsFailed:
+            grades.fail("Tests failed.")
+        else:
+            grades.assignFullCredit()
+            grades.addPoints(self.extraPoints)
+
+# Question in which predict credit is given for test cases with a ``points'' property.
+# All other tests are mandatory and must be passed.
+class HackedPartialCreditQuestion(Question):
+
+    def execute(self, grades):
+        # TODO: is this the right way to use grades?  The autograder doesn't seem to use it.
+        grades.assignZeroCredit()
+
+        points = 0
+        passed = True
+        for testCase, f in self.testCases:
+            testResult = f(grades)
+            if "points" in testCase.testDict:
+                if testResult: points += float(testCase.testDict["points"])
+            else:
+                passed = passed and testResult
+
+        ## FIXME: Below terrible hack to match q3's logic
+        if int(points) == self.maxPoints and not passed:
+            grades.assignZeroCredit()
+        else:
+            grades.addPoints(int(points))
+
+
+class Q6PartialCreditQuestion(Question):
+    """Fails any test which returns False, otherwise doesn't effect the grades object.
+    Partial credit tests will add the required points."""
+
+    def execute(self, grades):
+        grades.assignZeroCredit()
+
+        results = []
+        for _, f in self.testCases:
+            results.append(f(grades))
+        if False in results:
+            grades.assignZeroCredit()
+
+class PartialCreditQuestion(Question):
+    """Fails any test which returns False, otherwise doesn't effect the grades object.
+    Partial credit tests will add the required points."""
+
+    def execute(self, grades):
+        grades.assignZeroCredit()
+
+        for _, f in self.testCases:
+            if not f(grades):
+                grades.assignZeroCredit()
+                grades.fail("Tests failed.")
+                return False
+
+
+
+class NumberPassedQuestion(Question):
+    """Grade is the number of test cases passed."""
+
+    def execute(self, grades):
+        grades.addPoints([f(grades) for _, f in self.testCases].count(True))
+
+
+
+
+# BEGIN SOLUTION NO PROMPT
+from testParser import emitTestDict
+# END SOLUTION NO PROMPT
+
+# Template modeling a generic test case
+class TestCase(object):
+
+    def raiseNotDefined(self):
+        print('Method not implemented: %s' % inspect.stack()[1][3])
+        sys.exit(1)
+
+    def getPath(self):
+        return self.path
+
+    def __init__(self, question, testDict):
+        self.question = question
+        self.testDict = testDict
+        self.path = testDict['path']
+        self.messages = []
+
+    def __str__(self):
+        self.raiseNotDefined()
+
+    def execute(self, grades, moduleDict, solutionDict):
+        self.raiseNotDefined()
+
+    def writeSolution(self, moduleDict, filePath):
+        self.raiseNotDefined()
+        return True
+
+    # Tests should call the following messages for grading
+    # to ensure a uniform format for test output.
+    #
+    # TODO: this is hairy, but we need to fix grading.py's interface
+    # to get a nice hierarchical project - question - test structure,
+    # then these should be moved into Question proper.
+    def testPass(self, grades):
+        grades.addMessage('PASS: %s' % (self.path,))
+        for line in self.messages:
+            grades.addMessage('    %s' % (line,))
+        return True
+
+    def testFail(self, grades):
+        grades.addMessage('FAIL: %s' % (self.path,))
+        for line in self.messages:
+            grades.addMessage('    %s' % (line,))
+        return False
+
+    # This should really be question level?
+    #
+    def testPartial(self, grades, points, maxPoints):
+        grades.addPoints(points)
+        extraCredit = max(0, points - maxPoints)
+        regularCredit = points - extraCredit
+
+        grades.addMessage('%s: %s (%s of %s points)' % ("PASS" if points >= maxPoints else "FAIL", self.path, regularCredit, maxPoints))
+        if extraCredit > 0:
+            grades.addMessage('EXTRA CREDIT: %s points' % (extraCredit,))
+
+        for line in self.messages:
+            grades.addMessage('    %s' % (line,))
+
+        return True
+
+    def addMessage(self, message):
+        self.messages.extend(message.split('\n'))
+
+    # BEGIN SOLUTION NO PROMPT
+    def createPublicVersion(self):
+        self.raiseNotDefined()
+
+    def emitPublicVersion(self, filePath):
+        with open(filePath, 'w') as handle:
+            emitTestDict(self.testDict, handle)
+    # END SOLUTION NO PROMPT

tracking/testParser.py

@@ -0,0 +1,85 @@
+# testParser.py
+# -------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+import re
+import sys
+
+class TestParser(object):
+
+    def __init__(self, path):
+        # save the path to the test file
+        self.path = path
+
+    def removeComments(self, rawlines):
+        # remove any portion of a line following a '#' symbol
+        fixed_lines = []
+        for l in rawlines:
+            idx = l.find('#')
+            if idx == -1:
+                fixed_lines.append(l)
+            else:
+                fixed_lines.append(l[0:idx])
+        return '\n'.join(fixed_lines)
+
+    def parse(self):
+        # read in the test case and remove comments
+        test = {}
+        with open(self.path) as handle:
+            raw_lines = handle.read().split('\n')
+
+        test_text = self.removeComments(raw_lines)
+        test['__raw_lines__'] = raw_lines
+        test['path'] = self.path
+        test['__emit__'] = []
+        lines = test_text.split('\n')
+        i = 0
+        # read a property in each loop cycle
+        while(i < len(lines)):
+            # skip blank lines
+            if re.match(r'\A\s*\Z', lines[i]):
+                test['__emit__'].append(("raw", raw_lines[i]))
+                i += 1
+                continue
+            m = re.match(r'\A([^"]*?):\s*"([^"]*)"\s*\Z', lines[i])
+            if m:
+                test[m.group(1)] = m.group(2)
+                test['__emit__'].append(("oneline", m.group(1)))
+                i += 1
+                continue
+            m = re.match(r'\A([^"]*?):\s*"""\s*\Z', lines[i])
+            if m:
+                msg = []
+                i += 1
+                while(not re.match(r'\A\s*"""\s*\Z', lines[i])):
+                    msg.append(raw_lines[i])
+                    i += 1
+                test[m.group(1)] = '\n'.join(msg)
+                test['__emit__'].append(("multiline", m.group(1)))
+                i += 1
+                continue
+            print('error parsing test file: %s' % self.path)
+            sys.exit(1)
+        return test
+
+
+def emitTestDict(testDict, handle):
+    for kind, data in testDict['__emit__']:
+        if kind == "raw":
+            handle.write(data + "\n")
+        elif kind == "oneline":
+            handle.write('%s: "%s"\n' % (data, testDict[data]))
+        elif kind == "multiline":
+            handle.write('%s: """\n%s\n"""\n' % (data, testDict[data]))
+        else:
+            raise Exception("Bad __emit__")

tracking/test_cases/CONFIG

@@ -0,0 +1 @@
+order: "q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14"

tracking/test_cases/q1/1-small-board.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases\q1\1-small-board.test.
+
+solutionString: """
+Variables: Ghost1, Observation1, Observation0, Ghost0, Pacman
+Edges: Ghost1 -> Observation1, Pacman -> Observation1, Ghost0 -> Observation0, Pacman -> Observation0
+{'Pacman': [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 10), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9), (4, 10), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), (5, 7), (5, 8), (5, 9), (5, 10), (6, 0), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (6, 8), (6, 9), (6, 10), (7, 0), (7, 1), (7, 2), (7, 3), (7, 4), (7, 5), (7, 6), (7, 7), (7, 8), (7, 9), (7, 10), (8, 0), (8, 1), (8, 2), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (8, 9), (8, 10), (9, 0), (9, 1), (9, 2), (9, 3), (9, 4), (9, 5), (9, 6), (9, 7), (9, 8), (9, 9), (9, 10), (10, 0), (10, 1), (10, 2), (10, 3), (10, 4), (10, 5), (10, 6), (10, 7), (10, 8), (10, 9), (10, 10), (11, 0), (11, 1), (11, 2), (11, 3), (11, 4), (11, 5), (11, 6), (11, 7), (11, 8), (11, 9), (11, 10), (12, 0), (12, 1), (12, 2), (12, 3), (12, 4), (12, 5), (12, 6), (12, 7), (12, 8), (12, 9), (12, 10), (13, 0), (13, 1), (13, 2), (13, 3), (13, 4), (13, 5), (13, 6), (13, 7), (13, 8), (13, 9), (13, 10), (14, 0), (14, 1), (14, 2), (14, 3), (14, 4), (14, 5), (14, 6), (14, 7), (14, 8), (14, 9), (14, 10)], 'Ghost0': [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 10), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9), (4, 10), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), (5, 7), (5, 8), (5, 9), (5, 10), (6, 0), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (6, 8), (6, 9), (6, 10), (7, 0), (7, 1), (7, 2), (7, 3), (7, 4), (7, 5), (7, 6), (7, 7), (7, 8), (7, 9), (7, 10), (8, 0), (8, 1), (8, 2), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (8, 9), (8, 10), (9, 0), (9, 1), (9, 2), (9, 3), (9, 4), (9, 5), (9, 6), (9, 7), (9, 8), (9, 9), (9, 10), (10, 0), (10, 1), (10, 2), (10, 3), (10, 4), (10, 5), (10, 6), (10, 7), (10, 8), (10, 9), (10, 10), (11, 0), (11, 1), (11, 2), (11, 3), (11, 4), (11, 5), (11, 6), (11, 7), (11, 8), (11, 9), (11, 10), (12, 0), (12, 1), (12, 2), (12, 3), (12, 4), (12, 5), (12, 6), (12, 7), (12, 8), (12, 9), (12, 10), (13, 0), (13, 1), (13, 2), (13, 3), (13, 4), (13, 5), (13, 6), (13, 7), (13, 8), (13, 9), (13, 10), (14, 0), (14, 1), (14, 2), (14, 3), (14, 4), (14, 5), (14, 6), (14, 7), (14, 8), (14, 9), (14, 10)], 'Ghost1': [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 10), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9), (4, 10), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), (5, 7), (5, 8), (5, 9), (5, 10), (6, 0), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (6, 8), (6, 9), (6, 10), (7, 0), (7, 1), (7, 2), (7, 3), (7, 4), (7, 5), (7, 6), (7, 7), (7, 8), (7, 9), (7, 10), (8, 0), (8, 1), (8, 2), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (8, 9), (8, 10), (9, 0), (9, 1), (9, 2), (9, 3), (9, 4), (9, 5), (9, 6), (9, 7), (9, 8), (9, 9), (9, 10), (10, 0), (10, 1), (10, 2), (10, 3), (10, 4), (10, 5), (10, 6), (10, 7), (10, 8), (10, 9), (10, 10), (11, 0), (11, 1), (11, 2), (11, 3), (11, 4), (11, 5), (11, 6), (11, 7), (11, 8), (11, 9), (11, 10), (12, 0), (12, 1), (12, 2), (12, 3), (12, 4), (12, 5), (12, 6), (12, 7), (12, 8), (12, 9), (12, 10), (13, 0), (13, 1), (13, 2), (13, 3), (13, 4), (13, 5), (13, 6), (13, 7), (13, 8), (13, 9), (13, 10), (14, 0), (14, 1), (14, 2), (14, 3), (14, 4), (14, 5), (14, 6), (14, 7), (14, 8), (14, 9), (14, 10)], 'Observation0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], 'Observation1': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}
+"""

tracking/test_cases/q1/1-small-board.test

@@ -0,0 +1,17 @@
+class: "GraphEqualityTest"
+
+layoutName: "smallBoard"
+layout: """
+%%%%%%%%%%%%%%%
+%             %
+% RBB         %
+% BGB         %
+% B R         %
+%             %
+%         RBR %
+%         B.R %
+%         B R %
+%P            %
+%%%%%%%%%%%%%%%
+"""
+

tracking/test_cases/q1/2-long-bottom.solution

@@ -0,0 +1,7 @@
+# This is the solution file for test_cases\q1\2-long-bottom.test.
+
+solutionString: """
+Variables: Ghost1, Observation1, Observation0, Ghost0, Pacman
+Edges: Ghost1 -> Observation1, Pacman -> Observation1, Ghost0 -> Observation0, Pacman -> Observation0
+{'Pacman': [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (0, 13), (0, 14), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (1, 13), (1, 14), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 10), (2, 11), (2, 12), (2, 13), (2, 14), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (3, 11), (3, 12), (3, 13), (3, 14), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9), (4, 10), (4, 11), (4, 12), (4, 13), (4, 14), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), (5, 7), (5, 8), (5, 9), (5, 10), (5, 11), (5, 12), (5, 13), (5, 14), (6, 0), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (6, 8), (6, 9), (6, 10), (6, 11), (6, 12), (6, 13), (6, 14), (7, 0), (7, 1), (7, 2), (7, 3), (7, 4), (7, 5), (7, 6), (7, 7), (7, 8), (7, 9), (7, 10), (7, 11), (7, 12), (7, 13), (7, 14), (8, 0), (8, 1), (8, 2), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (8, 9), (8, 10), (8, 11), (8, 12), (8, 13), (8, 14), (9, 0), (9, 1), (9, 2), (9, 3), (9, 4), (9, 5), (9, 6), (9, 7), (9, 8), (9, 9), (9, 10), (9, 11), (9, 12), (9, 13), (9, 14), (10, 0), (10, 1), (10, 2), (10, 3), (10, 4), (10, 5), (10, 6), (10, 7), (10, 8), (10, 9), (10, 10), (10, 11), (10, 12), (10, 13), (10, 14)], 'Ghost0': [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (0, 13), (0, 14), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (1, 13), (1, 14), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 10), (2, 11), (2, 12), (2, 13), (2, 14), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (3, 11), (3, 12), (3, 13), (3, 14), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9), (4, 10), (4, 11), (4, 12), (4, 13), (4, 14), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), (5, 7), (5, 8), (5, 9), (5, 10), (5, 11), (5, 12), (5, 13), (5, 14), (6, 0), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (6, 8), (6, 9), (6, 10), (6, 11), (6, 12), (6, 13), (6, 14), (7, 0), (7, 1), (7, 2), (7, 3), (7, 4), (7, 5), (7, 6), (7, 7), (7, 8), (7, 9), (7, 10), (7, 11), (7, 12), (7, 13), (7, 14), (8, 0), (8, 1), (8, 2), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (8, 9), (8, 10), (8, 11), (8, 12), (8, 13), (8, 14), (9, 0), (9, 1), (9, 2), (9, 3), (9, 4), (9, 5), (9, 6), (9, 7), (9, 8), (9, 9), (9, 10), (9, 11), (9, 12), (9, 13), (9, 14), (10, 0), (10, 1), (10, 2), (10, 3), (10, 4), (10, 5), (10, 6), (10, 7), (10, 8), (10, 9), (10, 10), (10, 11), (10, 12), (10, 13), (10, 14)], 'Ghost1': [(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (0, 13), (0, 14), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (1, 13), (1, 14), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 10), (2, 11), (2, 12), (2, 13), (2, 14), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (3, 11), (3, 12), (3, 13), (3, 14), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9), (4, 10), (4, 11), (4, 12), (4, 13), (4, 14), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), (5, 7), (5, 8), (5, 9), (5, 10), (5, 11), (5, 12), (5, 13), (5, 14), (6, 0), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6), (6, 7), (6, 8), (6, 9), (6, 10), (6, 11), (6, 12), (6, 13), (6, 14), (7, 0), (7, 1), (7, 2), (7, 3), (7, 4), (7, 5), (7, 6), (7, 7), (7, 8), (7, 9), (7, 10), (7, 11), (7, 12), (7, 13), (7, 14), (8, 0), (8, 1), (8, 2), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (8, 9), (8, 10), (8, 11), (8, 12), (8, 13), (8, 14), (9, 0), (9, 1), (9, 2), (9, 3), (9, 4), (9, 5), (9, 6), (9, 7), (9, 8), (9, 9), (9, 10), (9, 11), (9, 12), (9, 13), (9, 14), (10, 0), (10, 1), (10, 2), (10, 3), (10, 4), (10, 5), (10, 6), (10, 7), (10, 8), (10, 9), (10, 10), (10, 11), (10, 12), (10, 13), (10, 14)], 'Observation0': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], 'Observation1': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]}
+"""

tracking/test_cases/q1/2-long-bottom.test

@@ -0,0 +1,21 @@
+class: "GraphEqualityTest"
+
+layoutName: "longBottomBoard"
+layout: """
+%%%%%%%%%%%
+%         %
+%         %
+%         %
+%         %
+%         %
+%         %
+%         %
+%         %
+%         %
+% RBR BBR %
+% R.B BGB %
+% B B R R %
+%P        %
+%%%%%%%%%%%
+"""
+

tracking/test_cases/q1/3-wide-inverted.test

@@ -0,0 +1,21 @@
+class: "GraphEqualityTest"
+
+layoutName: "wideInvertedBoard"
+layout: """
+%%%%%%%%%%%%%%%%%%%
+%                 %
+%             BRR %
+%             RGB %
+%             R B %
+%                 %
+%                 %
+%                 %
+%                 %
+%                 %
+% BBB             %
+% R.B             %
+% B B             %
+%P                %
+%%%%%%%%%%%%%%%%%%%
+"""
+

tracking/test_cases/q1/CONFIG

@@ -0,0 +1,2 @@
+max_points: "2"
+class: "PassAllTestsQuestion"

tracking/test_cases/q10/1-ParticleUpdate.solution

@@ -0,0 +1,55 @@
+# move_number action likelihood_dictionary
+correctActions: """
+0 None [{(5, 4): 0.125, (3, 4): 0.125, (1, 4): 0.125, (7, 4): 0.125, (9, 4): 0.125, (5, 2): 0.125, (2, 4): 0.125, (8, 4): 0.125, }, ]
+1 Stop [{(5, 4): 0.222222222222, (5, 2): 0.0, (1, 4): 0.0555555555556, (2, 4): 0.111111111111, (7, 4): 0.222222222222, (9, 4): 0.0555555555556, (3, 4): 0.222222222222, (1, 1): 0.0, (8, 4): 0.111111111111, }, ]
+2 Stop [{(5, 4): 0.603773584906, (5, 2): 0.0, (1, 4): 0.00943396226415, (2, 4): 0.0377358490566, (7, 4): 0.150943396226, (9, 4): 0.00943396226415, (3, 4): 0.150943396226, (1, 1): 0.0, (8, 4): 0.0377358490566, }, ]
+3 Stop [{(5, 4): 0.304761904762, (5, 2): 0.0, (1, 4): 0.0047619047619, (2, 4): 0.0380952380952, (7, 4): 0.304761904762, (9, 4): 0.0047619047619, (3, 4): 0.304761904762, (1, 1): 0.0, (8, 4): 0.0380952380952, }, ]
+4 Stop [{(5, 4): 0.104918032787, (5, 2): 0.0, (1, 4): 0.0016393442623, (2, 4): 0.0262295081967, (7, 4): 0.419672131148, (9, 4): 0.0016393442623, (3, 4): 0.419672131148, (1, 1): 0.0, (8, 4): 0.0262295081967, }, ]
+5 Stop [{(5, 4): 0.326322498407, (5, 2): 0.0, (1, 4): 0.00031867431485, (2, 4): 0.0101975780752, (7, 4): 0.326322498407, (9, 4): 0.00031867431485, (3, 4): 0.326322498407, (1, 1): 0.0, (8, 4): 0.0101975780752, }, ]
+6 Stop [{(5, 4): 0.329843775165, (5, 2): 0.0, (1, 4): 8.05282654212e-05, (2, 4): 0.00515380898695, (7, 4): 0.329843775165, (9, 4): 8.05282654212e-05, (3, 4): 0.329843775165, (1, 1): 0.0, (8, 4): 0.00515380898695, }, ]
+7 Stop [{(5, 4): 0.664921572208, (5, 2): 0.0, (1, 4): 1.01458980134e-05, (2, 4): 0.00129867494572, (7, 4): 0.166230393052, (9, 4): 1.01458980134e-05, (3, 4): 0.166230393052, (1, 1): 0.0, (8, 4): 0.00129867494572, }, ]
+8 Stop [{(5, 4): 0.888501748065, (5, 2): 0.0, (1, 4): 8.47341297212e-07, (2, 4): 0.000216919372086, (7, 4): 0.0555313592541, (9, 4): 8.47341297212e-07, (3, 4): 0.0555313592541, (1, 1): 0.0, (8, 4): 0.000216919372086, }, ]
+9 Stop [{(5, 4): 0.888695652911, (5, 2): 0.0, (1, 4): 2.11881554821e-07, (2, 4): 0.000108483356068, (7, 4): 0.0555434783069, (9, 4): 2.11881554821e-07, (3, 4): 0.0555434783069, (1, 1): 0.0, (8, 4): 0.000108483356068, }, ]
+10 Stop [{(5, 4): 0.969668246473, (5, 2): 0.0, (1, 4): 1.44491828452e-08, (2, 4): 1.47959632335e-05, (7, 4): 0.0151510663511, (9, 4): 1.44491828452e-08, (3, 4): 0.0151510663511, (1, 1): 0.0, (8, 4): 1.47959632335e-05, }, ]
+11 Stop [{(5, 4): 0.992244304412, (5, 2): 0.0, (1, 4): 9.24099520232e-10, (2, 4): 1.89255581744e-06, (7, 4): 0.00387595431411, (9, 4): 9.24099520232e-10, (3, 4): 0.00387595431411, (1, 1): 0.0, (8, 4): 1.89255581744e-06, }, ]
+12 Stop [{(5, 4): 0.998050207166, (5, 2): 0.0, (1, 4): 5.80941680333e-11, (2, 4): 2.37953712264e-07, (7, 4): 0.000974658405435, (9, 4): 5.80941680333e-11, (3, 4): 0.000974658405435, (1, 1): 0.0, (8, 4): 2.37953712264e-07, }, ]
+13 Stop [{(5, 4): 0.998050444742, (5, 2): 0.0, (1, 4): 1.45235454655e-11, (2, 4): 1.18976884454e-07, (7, 4): 0.000974658637444, (9, 4): 1.45235454655e-11, (3, 4): 0.000974658637444, (1, 1): 0.0, (8, 4): 1.18976884454e-07, }, ]
+14 Stop [{(5, 4): 0.999511927277, (5, 2): 0.0, (1, 4): 9.09050802217e-13, (2, 4): 1.48938883435e-08, (7, 4): 0.00024402146662, (9, 4): 9.09050802217e-13, (3, 4): 0.00024402146662, (1, 1): 0.0, (8, 4): 1.48938883435e-08, }, ]
+15 Stop [{(5, 4): 0.999877940862, (5, 2): 0.0, (1, 4): 5.68364806021e-14, (2, 4): 1.86241779637e-09, (7, 4): 6.10277063515e-05, (9, 4): 5.68364806021e-14, (3, 4): 6.10277063515e-05, (1, 1): 0.0, (8, 4): 1.86241779637e-09, }, ]
+16 Stop [{(5, 4): 0.999969482888, (5, 2): 0.0, (1, 4): 3.55260526024e-15, (2, 4): 2.32823538335e-10, (7, 4): 1.52583234083e-05, (9, 4): 3.55260526024e-15, (3, 4): 1.52583234083e-05, (1, 1): 0.0, (8, 4): 2.32823538335e-10, }, ]
+17 Stop [{(5, 4): 0.999992370605, (5, 2): 0.0, (1, 4): 2.22042910859e-16, (2, 4): 2.91036084121e-11, (7, 4): 3.81466816179e-06, (9, 4): 2.22042910859e-16, (3, 4): 3.81466816179e-06, (1, 1): 0.0, (8, 4): 2.91036084121e-11, }, ]
+18 Stop [{(5, 4): 0.999998092648, (5, 2): 0.0, (1, 4): 1.3877761338e-17, (2, 4): 3.63797186818e-12, (7, 4): 9.53672497413e-07, (9, 4): 1.3877761338e-17, (3, 4): 9.53672497413e-07, (1, 1): 0.0, (8, 4): 3.63797186818e-12, }, ]
+19 Stop [{(5, 4): 0.999998092651, (5, 2): 0.0, (1, 4): 3.46944033451e-18, (2, 4): 1.8189859341e-12, (7, 4): 9.53672497417e-07, (9, 4): 3.46944033451e-18, (3, 4): 9.53672497417e-07, (1, 1): 0.0, (8, 4): 1.8189859341e-12, }, ]
+20 Stop [{(5, 4): 0.999992370656, (5, 2): 0.0, (1, 4): 3.46942048235e-18, (2, 4): 3.6379510517e-12, (7, 4): 3.81466816199e-06, (9, 4): 3.46942048235e-18, (3, 4): 3.81466816199e-06, (1, 1): 0.0, (8, 4): 3.6379510517e-12, }, ]
+21 Stop [{(5, 4): 0.999998092654, (5, 2): 0.0, (1, 4): 2.16840020907e-19, (2, 4): 4.54746483526e-13, (7, 4): 9.53672497419e-07, (9, 4): 2.16840020907e-19, (3, 4): 9.53672497419e-07, (1, 1): 0.0, (8, 4): 4.54746483526e-13, }, ]
+22 Stop [{(5, 4): 0.999992370656, (5, 2): 0.0, (1, 4): 3.46942048235e-18, (2, 4): 3.6379510517e-12, (7, 4): 3.81466816199e-06, (9, 4): 3.46942048235e-18, (3, 4): 3.81466816199e-06, (1, 1): 0.0, (8, 4): 3.6379510517e-12, }, ]
+23 Stop [{(5, 4): 0.99999237066, (5, 2): 0.0, (1, 4): 8.67355120591e-19, (2, 4): 1.81897552586e-12, (7, 4): 3.814668162e-06, (9, 4): 8.67355120591e-19, (3, 4): 3.814668162e-06, (1, 1): 0.0, (8, 4): 1.81897552586e-12, }, ]
+24 Stop [{(5, 4): 0.999998092655, (5, 2): 0.0, (1, 4): 5.42100052269e-20, (2, 4): 2.27373241763e-13, (7, 4): 9.5367249742e-07, (9, 4): 5.42100052269e-20, (3, 4): 9.5367249742e-07, (1, 1): 0.0, (8, 4): 2.27373241763e-13, }, ]
+25 Stop [{(5, 4): 0.999998092655, (5, 2): 0.0, (1, 4): 1.35525013067e-20, (2, 4): 1.13686620882e-13, (7, 4): 9.5367249742e-07, (9, 4): 1.35525013067e-20, (3, 4): 9.5367249742e-07, (1, 1): 0.0, (8, 4): 1.13686620882e-13, }, ]
+26 Stop [{(5, 4): 0.999999523163, (5, 2): 0.0, (1, 4): 8.47032543358e-22, (2, 4): 1.42108479389e-14, (7, 4): 2.38418465415e-07, (9, 4): 8.47032543358e-22, (3, 4): 2.38418465415e-07, (1, 1): 0.0, (8, 4): 1.42108479389e-14, }, ]
+27 Stop [{(5, 4): 0.999999880791, (5, 2): 0.0, (1, 4): 5.29395528925e-23, (2, 4): 1.77635662764e-15, (7, 4): 5.960463767e-08, (9, 4): 5.29395528925e-23, (3, 4): 5.960463767e-08, (1, 1): 0.0, (8, 4): 1.77635662764e-15, }, ]
+28 Stop [{(5, 4): 0.999999970198, (5, 2): 0.0, (1, 4): 3.3087223516e-24, (2, 4): 2.22044598308e-16, (7, 4): 1.49011607498e-08, (9, 4): 3.3087223516e-24, (3, 4): 1.49011607498e-08, (1, 1): 0.0, (8, 4): 2.22044598308e-16, }, ]
+29 Stop [{(5, 4): 0.999999970198, (5, 2): 0.0, (1, 4): 8.27180587901e-25, (2, 4): 1.11022299154e-16, (7, 4): 1.49011607498e-08, (9, 4): 8.27180587901e-25, (3, 4): 1.49011607498e-08, (1, 1): 0.0, (8, 4): 1.11022299154e-16, }, ]
+30 Stop [{(5, 4): 0.999999992549, (5, 2): 0.0, (1, 4): 5.16987878994e-26, (2, 4): 1.38777877044e-17, (7, 4): 3.72529027071e-09, (9, 4): 5.16987878994e-26, (3, 4): 3.72529027071e-09, (1, 1): 0.0, (8, 4): 1.38777877044e-17, }, ]
+31 Stop [{(5, 4): 0.999999970198, (5, 2): 0.0, (1, 4): 2.06795146975e-25, (2, 4): 1.11022299154e-16, (7, 4): 1.49011607498e-08, (9, 4): 2.06795146975e-25, (3, 4): 1.49011607498e-08, (1, 1): 0.0, (8, 4): 1.11022299154e-16, }, ]
+32 Stop [{(5, 4): 0.999999992549, (5, 2): 0.0, (1, 4): 1.29246969748e-26, (2, 4): 1.38777877044e-17, (7, 4): 3.72529027071e-09, (9, 4): 1.29246969748e-26, (3, 4): 3.72529027071e-09, (1, 1): 0.0, (8, 4): 1.38777877044e-17, }, ]
+33 Stop [{(5, 4): 0.999999998137, (5, 2): 0.0, (1, 4): 8.07793565442e-28, (2, 4): 1.73472347275e-18, (7, 4): 9.31322572881e-10, (9, 4): 8.07793565442e-28, (3, 4): 9.31322572881e-10, (1, 1): 0.0, (8, 4): 1.73472347275e-18, }, ]
+34 Stop [{(5, 4): 0.999999999534, (5, 2): 0.0, (1, 4): 5.04870979106e-29, (2, 4): 2.16840434396e-19, (7, 4): 2.32830643545e-10, (9, 4): 5.04870979106e-29, (3, 4): 2.32830643545e-10, (1, 1): 0.0, (8, 4): 2.16840434396e-19, }, ]
+35 Stop [{(5, 4): 0.999999999884, (5, 2): 0.0, (1, 4): 3.15544362052e-30, (2, 4): 2.7105054309e-20, (7, 4): 5.82076609067e-11, (9, 4): 3.15544362052e-30, (3, 4): 5.82076609067e-11, (1, 1): 0.0, (8, 4): 2.7105054309e-20, }, ]
+36 Stop [{(5, 4): 0.999999999971, (5, 2): 0.0, (1, 4): 1.972152263e-31, (2, 4): 3.38813178892e-21, (7, 4): 1.45519152279e-11, (9, 4): 1.972152263e-31, (3, 4): 1.45519152279e-11, (1, 1): 0.0, (8, 4): 3.38813178892e-21, }, ]
+37 Stop [{(5, 4): 0.999999999993, (5, 2): 0.0, (1, 4): 1.2325951644e-32, (2, 4): 4.23516473624e-22, (7, 4): 3.63797880707e-12, (9, 4): 1.2325951644e-32, (3, 4): 3.63797880707e-12, (1, 1): 0.0, (8, 4): 4.23516473624e-22, }, ]
+38 Stop [{(5, 4): 0.999999999993, (5, 2): 0.0, (1, 4): 3.081487911e-33, (2, 4): 2.11758236812e-22, (7, 4): 3.63797880707e-12, (9, 4): 3.081487911e-33, (3, 4): 3.63797880707e-12, (1, 1): 0.0, (8, 4): 2.11758236812e-22, }, ]
+39 Stop [{(5, 4): 0.999999999998, (5, 2): 0.0, (1, 4): 1.92592994438e-34, (2, 4): 2.64697796016e-23, (7, 4): 9.09494701771e-13, (9, 4): 1.92592994438e-34, (3, 4): 9.09494701771e-13, (1, 1): 0.0, (8, 4): 2.64697796016e-23, }, ]
+40 Stop [{(5, 4): 0.999999999998, (5, 2): 0.0, (1, 4): 4.81482486096e-35, (2, 4): 1.32348898008e-23, (7, 4): 9.09494701771e-13, (9, 4): 4.81482486096e-35, (3, 4): 9.09494701771e-13, (1, 1): 0.0, (8, 4): 1.32348898008e-23, }, ]
+41 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 3.0092655381e-36, (2, 4): 1.65436122511e-24, (7, 4): 2.27373675443e-13, (9, 4): 3.0092655381e-36, (3, 4): 2.27373675443e-13, (1, 1): 0.0, (8, 4): 1.65436122511e-24, }, ]
+42 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 1.88079096132e-37, (2, 4): 2.06795153138e-25, (7, 4): 5.68434188608e-14, (9, 4): 1.88079096132e-37, (3, 4): 5.68434188608e-14, (1, 1): 0.0, (8, 4): 2.06795153138e-25, }, ]
+43 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 7.52316384526e-37, (2, 4): 1.65436122511e-24, (7, 4): 2.27373675443e-13, (9, 4): 7.52316384526e-37, (3, 4): 2.27373675443e-13, (1, 1): 0.0, (8, 4): 1.65436122511e-24, }, ]
+44 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 1.88079096131e-37, (2, 4): 8.27180612553e-25, (7, 4): 2.27373675443e-13, (9, 4): 1.88079096131e-37, (3, 4): 2.27373675443e-13, (1, 1): 0.0, (8, 4): 8.27180612553e-25, }, ]
+45 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 1.17549435082e-38, (2, 4): 1.03397576569e-25, (7, 4): 5.68434188608e-14, (9, 4): 1.17549435082e-38, (3, 4): 5.68434188608e-14, (1, 1): 0.0, (8, 4): 1.03397576569e-25, }, ]
+46 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 7.34683969264e-40, (2, 4): 1.29246970711e-26, (7, 4): 1.42108547152e-14, (9, 4): 7.34683969264e-40, (3, 4): 1.42108547152e-14, (1, 1): 0.0, (8, 4): 1.29246970711e-26, }, ]
+47 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 7.34683969264e-40, (2, 4): 2.58493941423e-26, (7, 4): 5.68434188608e-14, (9, 4): 7.34683969264e-40, (3, 4): 5.68434188608e-14, (1, 1): 0.0, (8, 4): 2.58493941423e-26, }, ]
+48 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 4.5917748079e-41, (2, 4): 3.23117426779e-27, (7, 4): 1.42108547152e-14, (9, 4): 4.5917748079e-41, (3, 4): 1.42108547152e-14, (1, 1): 0.0, (8, 4): 3.23117426779e-27, }, ]
+49 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 1.14794370197e-41, (2, 4): 1.61558713389e-27, (7, 4): 1.42108547152e-14, (9, 4): 1.14794370197e-41, (3, 4): 1.42108547152e-14, (1, 1): 0.0, (8, 4): 1.61558713389e-27, }, ]
+50 Stop [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 1.83670992316e-40, (2, 4): 1.29246970711e-26, (7, 4): 5.68434188608e-14, (9, 4): 1.83670992316e-40, (3, 4): 5.68434188608e-14, (1, 1): 0.0, (8, 4): 1.29246970711e-26, }, ]
+51 None [{(5, 4): 1.0, (5, 2): 0.0, (1, 4): 4.5917748079e-41, (2, 4): 6.46234853557e-27, (7, 4): 5.68434188608e-14, (9, 4): 4.5917748079e-41, (3, 4): 5.68434188608e-14, (1, 1): 0.0, (8, 4): 6.46234853557e-27, }, ]
+"""

tracking/test_cases/q10/1-ParticleUpdate.test

@@ -0,0 +1,26 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%%%%%%
+%   %G%   %
+%%%%%%%%%%%
+%%%%%P%%%%%
+% %%%%%%%%%
+%%%%%%%%%%%
+"""
+
+observe: "True"
+
+elapse: "False"
+
+checkUniform: "False"
+maxMoves: "50"
+numParticles: "5000"
+numGhosts: "1"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+L2Tolerance: "0.05"
+errorMsg: "Particle filter observe test: %d inference errors."
+

tracking/test_cases/q10/2-ParticleUpdate.solution

@@ -0,0 +1,105 @@
+# move_number action likelihood_dictionary
+correctActions: """
+0 None [{(1, 3): 0.0714285714286, (2, 9): 0.0714285714286, (5, 6): 0.0714285714286, (2, 8): 0.0714285714286, (9, 8): 0.0714285714286, (9, 3): 0.0714285714286, (9, 9): 0.0714285714286, (1, 4): 0.0714285714286, (8, 9): 0.0714285714286, (1, 8): 0.0714285714286, (8, 8): 0.0714285714286, (2, 3): 0.0714285714286, (1, 9): 0.0714285714286, (2, 4): 0.0714285714286, }, ]
+1 Stop [{(1, 3): 0.129032258065, (2, 9): 0.0645161290323, (5, 6): 0.0, (2, 8): 0.0322580645161, (9, 8): 0.0645161290323, (9, 3): 0.129032258065, (9, 9): 0.129032258065, (1, 4): 0.0645161290323, (2, 4): 0.0322580645161, (8, 9): 0.0645161290323, (1, 8): 0.0645161290323, (8, 8): 0.0322580645161, (2, 3): 0.0645161290323, (1, 9): 0.129032258065, (1, 1): 0.0, }, ]
+2 Stop [{(1, 3): 0.0769230769231, (2, 9): 0.0769230769231, (5, 6): 0.0, (2, 8): 0.0769230769231, (9, 8): 0.0769230769231, (9, 3): 0.0769230769231, (9, 9): 0.0769230769231, (1, 4): 0.0769230769231, (2, 4): 0.0769230769231, (8, 9): 0.0769230769231, (1, 8): 0.0769230769231, (8, 8): 0.0769230769231, (2, 3): 0.0769230769231, (1, 9): 0.0769230769231, (1, 1): 0.0, }, ]
+3 Stop [{(1, 3): 0.129032258065, (2, 9): 0.0645161290323, (5, 6): 0.0, (2, 8): 0.0322580645161, (9, 8): 0.0645161290323, (9, 3): 0.129032258065, (9, 9): 0.129032258065, (1, 4): 0.0645161290323, (2, 4): 0.0322580645161, (8, 9): 0.0645161290323, (1, 8): 0.0645161290323, (8, 8): 0.0322580645161, (2, 3): 0.0645161290323, (1, 9): 0.129032258065, (1, 1): 0.0, }, ]
+4 Stop [{(1, 3): 0.175824175824, (2, 9): 0.043956043956, (5, 6): 0.0, (2, 8): 0.010989010989, (9, 8): 0.043956043956, (9, 3): 0.175824175824, (9, 9): 0.175824175824, (1, 4): 0.043956043956, (2, 4): 0.010989010989, (8, 9): 0.043956043956, (1, 8): 0.043956043956, (8, 8): 0.010989010989, (2, 3): 0.043956043956, (1, 9): 0.175824175824, (1, 1): 0.0, }, ]
+5 Stop [{(1, 3): 0.208469055375, (2, 9): 0.0260586319218, (5, 6): 0.0, (2, 8): 0.00325732899023, (9, 8): 0.0260586319218, (9, 3): 0.208469055375, (9, 9): 0.208469055375, (1, 4): 0.0260586319218, (2, 4): 0.00325732899023, (8, 9): 0.0260586319218, (1, 8): 0.0260586319218, (8, 8): 0.00325732899023, (2, 3): 0.0260586319218, (1, 9): 0.208469055375, (1, 1): 0.0, }, ]
+6 Stop [{(1, 3): 0.227960819234, (2, 9): 0.0142475512021, (5, 6): 0.0, (2, 8): 0.000890471950134, (9, 8): 0.0142475512021, (9, 3): 0.227960819234, (9, 9): 0.227960819234, (1, 4): 0.0142475512021, (2, 4): 0.000890471950134, (8, 9): 0.0142475512021, (1, 8): 0.0142475512021, (8, 8): 0.000890471950134, (2, 3): 0.0142475512021, (1, 9): 0.227960819234, (1, 1): 0.0, }, ]
+7 Stop [{(1, 3): 0.208469055375, (2, 9): 0.0260586319218, (5, 6): 0.0, (2, 8): 0.00325732899023, (9, 8): 0.0260586319218, (9, 3): 0.208469055375, (9, 9): 0.208469055375, (1, 4): 0.0260586319218, (2, 4): 0.00325732899023, (8, 9): 0.0260586319218, (1, 8): 0.0260586319218, (8, 8): 0.00325732899023, (2, 3): 0.0260586319218, (1, 9): 0.208469055375, (1, 1): 0.0, }, ]
+8 Stop [{(1, 3): 0.180281690141, (2, 9): 0.0450704225352, (5, 6): 0.0, (2, 8): 0.00281690140845, (9, 8): 0.0450704225352, (9, 3): 0.180281690141, (9, 9): 0.180281690141, (1, 4): 0.0450704225352, (2, 4): 0.00281690140845, (8, 9): 0.0450704225352, (1, 8): 0.0450704225352, (8, 8): 0.00281690140845, (2, 3): 0.0450704225352, (1, 9): 0.180281690141, (1, 1): 0.0, }, ]
+9 Stop [{(1, 3): 0.210008203445, (2, 9): 0.0262510254307, (5, 6): 0.0, (2, 8): 0.000820344544709, (9, 8): 0.0262510254307, (9, 3): 0.210008203445, (9, 9): 0.210008203445, (1, 4): 0.0262510254307, (2, 4): 0.000820344544709, (8, 9): 0.0262510254307, (1, 8): 0.0262510254307, (8, 8): 0.000820344544709, (2, 3): 0.0262510254307, (1, 9): 0.210008203445, (1, 1): 0.0, }, ]
+10 Stop [{(1, 3): 0.180281690141, (2, 9): 0.0450704225352, (5, 6): 0.0, (2, 8): 0.00281690140845, (9, 8): 0.0450704225352, (9, 3): 0.180281690141, (9, 9): 0.180281690141, (1, 4): 0.0450704225352, (2, 4): 0.00281690140845, (8, 9): 0.0450704225352, (1, 8): 0.0450704225352, (8, 8): 0.00281690140845, (2, 3): 0.0450704225352, (1, 9): 0.180281690141, (1, 1): 0.0, }, ]
+11 Stop [{(1, 3): 0.139130434783, (2, 9): 0.0695652173913, (5, 6): 0.0, (2, 8): 0.00869565217391, (9, 8): 0.0695652173913, (9, 3): 0.139130434783, (9, 9): 0.139130434783, (1, 4): 0.0695652173913, (2, 4): 0.00869565217391, (8, 9): 0.0695652173913, (1, 8): 0.0695652173913, (8, 8): 0.00869565217391, (2, 3): 0.0695652173913, (1, 9): 0.139130434783, (1, 1): 0.0, }, ]
+12 Stop [{(1, 3): 0.0981595092025, (2, 9): 0.0981595092025, (5, 6): 0.0, (2, 8): 0.00613496932515, (9, 8): 0.0981595092025, (9, 3): 0.0981595092025, (9, 9): 0.0981595092025, (1, 4): 0.0981595092025, (2, 4): 0.00613496932515, (8, 9): 0.0981595092025, (1, 8): 0.0981595092025, (8, 8): 0.00613496932515, (2, 3): 0.0981595092025, (1, 9): 0.0981595092025, (1, 1): 0.0, }, ]
+13 Stop [{(1, 3): 0.141906873614, (2, 9): 0.0709534368071, (5, 6): 0.0, (2, 8): 0.00221729490022, (9, 8): 0.0709534368071, (9, 3): 0.141906873614, (9, 9): 0.141906873614, (1, 4): 0.0709534368071, (2, 4): 0.00221729490022, (8, 9): 0.0709534368071, (1, 8): 0.0709534368071, (8, 8): 0.00221729490022, (2, 3): 0.0709534368071, (1, 9): 0.141906873614, (1, 1): 0.0, }, ]
+14 Stop [{(1, 3): 0.0981595092025, (2, 9): 0.0981595092025, (5, 6): 0.0, (2, 8): 0.00613496932515, (9, 8): 0.0981595092025, (9, 3): 0.0981595092025, (9, 9): 0.0981595092025, (1, 4): 0.0981595092025, (2, 4): 0.00613496932515, (8, 9): 0.0981595092025, (1, 8): 0.0981595092025, (8, 8): 0.00613496932515, (2, 3): 0.0981595092025, (1, 9): 0.0981595092025, (1, 1): 0.0, }, ]
+15 Stop [{(1, 3): 0.141906873614, (2, 9): 0.0709534368071, (5, 6): 0.0, (2, 8): 0.00221729490022, (9, 8): 0.0709534368071, (9, 3): 0.141906873614, (9, 9): 0.141906873614, (1, 4): 0.0709534368071, (2, 4): 0.00221729490022, (8, 9): 0.0709534368071, (1, 8): 0.0709534368071, (8, 8): 0.00221729490022, (2, 3): 0.0709534368071, (1, 9): 0.141906873614, (1, 1): 0.0, }, ]
+16 Stop [{(1, 3): 0.181431608788, (2, 9): 0.045357902197, (5, 6): 0.0, (2, 8): 0.000708717221828, (9, 8): 0.045357902197, (9, 3): 0.181431608788, (9, 9): 0.181431608788, (1, 4): 0.045357902197, (2, 4): 0.000708717221828, (8, 9): 0.045357902197, (1, 8): 0.045357902197, (8, 8): 0.000708717221828, (2, 3): 0.045357902197, (1, 9): 0.181431608788, (1, 1): 0.0, }, ]
+17 Stop [{(1, 3): 0.210396548182, (2, 9): 0.0262995685227, (5, 6): 0.0, (2, 8): 0.000205465379084, (9, 8): 0.0262995685227, (9, 3): 0.210396548182, (9, 9): 0.210396548182, (1, 4): 0.0262995685227, (2, 4): 0.000205465379084, (8, 9): 0.0262995685227, (1, 8): 0.0262995685227, (8, 8): 0.000205465379084, (2, 3): 0.0262995685227, (1, 9): 0.210396548182, (1, 1): 0.0, }, ]
+18 Stop [{(1, 3): 0.22853316967, (2, 9): 0.0142833231044, (5, 6): 0.0, (2, 8): 5.57942308765e-05, (9, 8): 0.0142833231044, (9, 3): 0.22853316967, (9, 9): 0.22853316967, (1, 4): 0.0142833231044, (2, 4): 5.57942308765e-05, (8, 9): 0.0142833231044, (1, 8): 0.0142833231044, (8, 8): 5.57942308765e-05, (2, 3): 0.0142833231044, (1, 9): 0.22853316967, (1, 1): 0.0, }, ]
+19 Stop [{(1, 3): 0.238795528414, (2, 9): 0.00746236026293, (5, 6): 0.0, (2, 8): 1.45749223885e-05, (9, 8): 0.00746236026293, (9, 3): 0.238795528414, (9, 9): 0.238795528414, (1, 4): 0.00746236026293, (2, 4): 1.45749223885e-05, (8, 9): 0.00746236026293, (1, 8): 0.00746236026293, (8, 8): 1.45749223885e-05, (2, 3): 0.00746236026293, (1, 9): 0.238795528414, (1, 1): 0.0, }, ]
+20 Stop [{(1, 3): 0.244272077707, (2, 9): 0.00381675121417, (5, 6): 0.0, (2, 8): 3.72729610758e-06, (9, 8): 0.00381675121417, (9, 3): 0.244272077707, (9, 9): 0.244272077707, (1, 4): 0.00381675121417, (2, 4): 3.72729610758e-06, (8, 9): 0.00381675121417, (1, 8): 0.00381675121417, (8, 8): 3.72729610758e-06, (2, 3): 0.00381675121417, (1, 9): 0.244272077707, (1, 1): 0.0, }, ]
+21 Stop [{(1, 3): 0.238795528414, (2, 9): 0.00746236026293, (5, 6): 0.0, (2, 8): 1.45749223885e-05, (9, 8): 0.00746236026293, (9, 3): 0.238795528414, (9, 9): 0.238795528414, (1, 4): 0.00746236026293, (2, 4): 1.45749223885e-05, (8, 9): 0.00746236026293, (1, 8): 0.00746236026293, (8, 8): 1.45749223885e-05, (2, 3): 0.00746236026293, (1, 9): 0.238795528414, (1, 1): 0.0, }, ]
+22 Stop [{(1, 3): 0.244272077707, (2, 9): 0.00381675121417, (5, 6): 0.0, (2, 8): 3.72729610758e-06, (9, 8): 0.00381675121417, (9, 3): 0.244272077707, (9, 9): 0.244272077707, (1, 4): 0.00381675121417, (2, 4): 3.72729610758e-06, (8, 9): 0.00381675121417, (1, 8): 0.00381675121417, (8, 8): 3.72729610758e-06, (2, 3): 0.00381675121417, (1, 9): 0.244272077707, (1, 1): 0.0, }, ]
+23 Stop [{(1, 3): 0.247103548324, (2, 9): 0.00193049647128, (5, 6): 0.0, (2, 8): 9.42625230118e-07, (9, 8): 0.00193049647128, (9, 3): 0.247103548324, (9, 9): 0.247103548324, (1, 4): 0.00193049647128, (2, 4): 9.42625230118e-07, (8, 9): 0.00193049647128, (1, 8): 0.00193049647128, (8, 8): 9.42625230118e-07, (2, 3): 0.00193049647128, (1, 9): 0.247103548324, (1, 1): 0.0, }, ]
+24 Stop [{(1, 3): 0.244272077707, (2, 9): 0.00381675121417, (5, 6): 0.0, (2, 8): 3.72729610758e-06, (9, 8): 0.00381675121417, (9, 3): 0.244272077707, (9, 9): 0.244272077707, (1, 4): 0.00381675121417, (2, 4): 3.72729610758e-06, (8, 9): 0.00381675121417, (1, 8): 0.00381675121417, (8, 8): 3.72729610758e-06, (2, 3): 0.00381675121417, (1, 9): 0.244272077707, (1, 1): 0.0, }, ]
+25 Stop [{(1, 3): 0.247103548324, (2, 9): 0.00193049647128, (5, 6): 0.0, (2, 8): 9.42625230118e-07, (9, 8): 0.00193049647128, (9, 3): 0.247103548324, (9, 9): 0.247103548324, (1, 4): 0.00193049647128, (2, 4): 9.42625230118e-07, (8, 9): 0.00193049647128, (1, 8): 0.00193049647128, (8, 8): 9.42625230118e-07, (2, 3): 0.00193049647128, (1, 9): 0.247103548324, (1, 1): 0.0, }, ]
+26 Stop [{(1, 3): 0.244272077707, (2, 9): 0.00381675121417, (5, 6): 0.0, (2, 8): 3.72729610758e-06, (9, 8): 0.00381675121417, (9, 3): 0.244272077707, (9, 9): 0.244272077707, (1, 4): 0.00381675121417, (2, 4): 3.72729610758e-06, (8, 9): 0.00381675121417, (1, 8): 0.00381675121417, (8, 8): 3.72729610758e-06, (2, 3): 0.00381675121417, (1, 9): 0.244272077707, (1, 1): 0.0, }, ]
+27 Stop [{(1, 3): 0.23880335963, (2, 9): 0.00746260498843, (5, 6): 0.0, (2, 8): 3.64385009201e-06, (9, 8): 0.00746260498843, (9, 3): 0.23880335963, (9, 9): 0.23880335963, (1, 4): 0.00746260498843, (2, 4): 3.64385009201e-06, (8, 9): 0.00746260498843, (1, 8): 0.00746260498843, (8, 8): 3.64385009201e-06, (2, 3): 0.00746260498843, (1, 9): 0.23880335963, (1, 1): 0.0, }, ]
+28 Stop [{(1, 3): 0.228561862645, (2, 9): 0.0142851164153, (5, 6): 0.0, (2, 8): 1.39503089993e-05, (9, 8): 0.0142851164153, (9, 3): 0.228561862645, (9, 9): 0.228561862645, (1, 4): 0.0142851164153, (2, 4): 1.39503089993e-05, (8, 9): 0.0142851164153, (1, 8): 0.0142851164153, (8, 8): 1.39503089993e-05, (2, 3): 0.0142851164153, (1, 9): 0.228561862645, (1, 1): 0.0, }, ]
+29 Stop [{(1, 3): 0.23880335963, (2, 9): 0.00746260498843, (5, 6): 0.0, (2, 8): 3.64385009201e-06, (9, 8): 0.00746260498843, (9, 3): 0.23880335963, (9, 9): 0.23880335963, (1, 4): 0.00746260498843, (2, 4): 3.64385009201e-06, (8, 9): 0.00746260498843, (1, 8): 0.00746260498843, (8, 8): 3.64385009201e-06, (2, 3): 0.00746260498843, (1, 9): 0.23880335963, (1, 1): 0.0, }, ]
+30 Stop [{(1, 3): 0.228569037015, (2, 9): 0.0142855648134, (5, 6): 0.0, (2, 8): 3.48768672203e-06, (9, 8): 0.0142855648134, (9, 3): 0.228569037015, (9, 9): 0.228569037015, (1, 4): 0.0142855648134, (2, 4): 3.48768672203e-06, (8, 9): 0.0142855648134, (1, 8): 0.0142855648134, (8, 8): 3.48768672203e-06, (2, 3): 0.0142855648134, (1, 9): 0.228569037015, (1, 1): 0.0, }, ]
+31 Stop [{(1, 3): 0.238805317514, (2, 9): 0.00746266617231, (5, 6): 0.0, (2, 8): 9.10969991738e-07, (9, 8): 0.00746266617231, (9, 3): 0.238805317514, (9, 9): 0.238805317514, (1, 4): 0.00746266617231, (2, 4): 9.10969991738e-07, (8, 9): 0.00746266617231, (1, 8): 0.00746266617231, (8, 8): 9.10969991738e-07, (2, 3): 0.00746266617231, (1, 9): 0.238805317514, (1, 1): 0.0, }, ]
+32 Stop [{(1, 3): 0.228569037015, (2, 9): 0.0142855648134, (5, 6): 0.0, (2, 8): 3.48768672203e-06, (9, 8): 0.0142855648134, (9, 3): 0.228569037015, (9, 9): 0.228569037015, (1, 4): 0.0142855648134, (2, 4): 3.48768672203e-06, (8, 9): 0.0142855648134, (1, 8): 0.0142855648134, (8, 8): 3.48768672203e-06, (2, 3): 0.0142855648134, (1, 9): 0.228569037015, (1, 1): 0.0, }, ]
+33 Stop [{(1, 3): 0.21052428694, (2, 9): 0.0263155358674, (5, 6): 0.0, (2, 8): 3.21234568694e-06, (9, 8): 0.0263155358674, (9, 3): 0.21052428694, (9, 9): 0.21052428694, (1, 4): 0.0263155358674, (2, 4): 3.21234568694e-06, (8, 9): 0.0263155358674, (1, 8): 0.0263155358674, (8, 8): 3.21234568694e-06, (2, 3): 0.0263155358674, (1, 9): 0.21052428694, (1, 1): 0.0, }, ]
+34 Stop [{(1, 3): 0.181816668562, (2, 9): 0.0454541671406, (5, 6): 0.0, (2, 8): 2.77430219364e-06, (9, 8): 0.0454541671406, (9, 3): 0.181816668562, (9, 9): 0.181816668562, (1, 4): 0.0454541671406, (2, 4): 2.77430219364e-06, (8, 9): 0.0454541671406, (1, 8): 0.0454541671406, (8, 8): 2.77430219364e-06, (2, 3): 0.0454541671406, (1, 9): 0.181816668562, (1, 1): 0.0, }, ]
+35 Stop [{(1, 3): 0.142856208652, (2, 9): 0.0714281043258, (5, 6): 0.0, (2, 8): 2.17981275408e-06, (9, 8): 0.0714281043258, (9, 3): 0.142856208652, (9, 9): 0.142856208652, (1, 4): 0.0714281043258, (2, 4): 2.17981275408e-06, (8, 9): 0.0714281043258, (1, 8): 0.0714281043258, (8, 8): 2.17981275408e-06, (2, 3): 0.0714281043258, (1, 9): 0.142856208652, (1, 1): 0.0, }, ]
+36 Stop [{(1, 3): 0.181817803502, (2, 9): 0.0454544508755, (5, 6): 0.0, (2, 8): 6.93579877861e-07, (9, 8): 0.0454544508755, (9, 3): 0.181817803502, (9, 9): 0.181817803502, (1, 4): 0.0454544508755, (2, 4): 6.93579877861e-07, (8, 9): 0.0454544508755, (1, 8): 0.0454544508755, (8, 8): 6.93579877861e-07, (2, 3): 0.0454544508755, (1, 9): 0.181817803502, (1, 1): 0.0, }, ]
+37 Stop [{(1, 3): 0.142856208652, (2, 9): 0.0714281043258, (5, 6): 0.0, (2, 8): 2.17981275408e-06, (9, 8): 0.0714281043258, (9, 3): 0.142856208652, (9, 9): 0.142856208652, (1, 4): 0.0714281043258, (2, 4): 2.17981275408e-06, (8, 9): 0.0714281043258, (1, 8): 0.0714281043258, (8, 8): 2.17981275408e-06, (2, 3): 0.0714281043258, (1, 9): 0.142856208652, (1, 1): 0.0, }, ]
+38 Stop [{(1, 3): 0.181817803502, (2, 9): 0.0454544508755, (5, 6): 0.0, (2, 8): 6.93579877861e-07, (9, 8): 0.0454544508755, (9, 3): 0.181817803502, (9, 9): 0.181817803502, (1, 4): 0.0454544508755, (2, 4): 6.93579877861e-07, (8, 9): 0.0454544508755, (1, 8): 0.0454544508755, (8, 8): 6.93579877861e-07, (2, 3): 0.0454544508755, (1, 9): 0.181817803502, (1, 1): 0.0, }, ]
+39 Stop [{(1, 3): 0.210526188985, (2, 9): 0.0263157736232, (5, 6): 0.0, (2, 8): 2.00773419366e-07, (9, 8): 0.0263157736232, (9, 3): 0.210526188985, (9, 9): 0.210526188985, (1, 4): 0.0263157736232, (2, 4): 2.00773419366e-07, (8, 9): 0.0263157736232, (1, 8): 0.0263157736232, (8, 8): 2.00773419366e-07, (2, 3): 0.0263157736232, (1, 9): 0.210526188985, (1, 1): 0.0, }, ]
+40 Stop [{(1, 3): 0.228571391203, (2, 9): 0.0142857119502, (5, 6): 0.0, (2, 8): 5.44956663139e-08, (9, 8): 0.0142857119502, (9, 3): 0.228571391203, (9, 9): 0.228571391203, (1, 4): 0.0142857119502, (2, 4): 5.44956663139e-08, (8, 9): 0.0142857119502, (1, 8): 0.0142857119502, (8, 8): 5.44956663139e-08, (2, 3): 0.0142857119502, (1, 9): 0.228571391203, (1, 1): 0.0, }, ]
+41 Stop [{(1, 3): 0.210526284088, (2, 9): 0.026315785511, (5, 6): 0.0, (2, 8): 5.01933775159e-08, (9, 8): 0.026315785511, (9, 3): 0.210526284088, (9, 9): 0.210526284088, (1, 4): 0.026315785511, (2, 4): 5.01933775159e-08, (8, 9): 0.026315785511, (1, 8): 0.026315785511, (8, 8): 5.01933775159e-08, (2, 3): 0.026315785511, (1, 9): 0.210526284088, (1, 1): 0.0, }, ]
+42 Stop [{(1, 3): 0.228571419229, (2, 9): 0.0142857137018, (5, 6): 0.0, (2, 8): 1.3623918249e-08, (9, 8): 0.0142857137018, (9, 3): 0.228571419229, (9, 9): 0.228571419229, (1, 4): 0.0142857137018, (2, 4): 1.3623918249e-08, (8, 9): 0.0142857137018, (1, 8): 0.0142857137018, (8, 8): 1.3623918249e-08, (2, 3): 0.0142857137018, (1, 9): 0.228571419229, (1, 1): 0.0, }, ]
+43 Stop [{(1, 3): 0.2388059676, (2, 9): 0.0074626864875, (5, 6): 0.0, (2, 8): 3.55848621726e-09, (9, 8): 0.0074626864875, (9, 3): 0.2388059676, (9, 9): 0.2388059676, (1, 4): 0.0074626864875, (2, 4): 3.55848621726e-09, (8, 9): 0.0074626864875, (1, 8): 0.0074626864875, (8, 8): 3.55848621726e-09, (2, 3): 0.0074626864875, (1, 9): 0.2388059676, (1, 1): 0.0, }, ]
+44 Stop [{(1, 3): 0.244274808493, (2, 9): 0.00381679388271, (5, 6): 0.0, (2, 8): 9.09994574239e-10, (9, 8): 0.00381679388271, (9, 3): 0.244274808493, (9, 9): 0.244274808493, (1, 4): 0.00381679388271, (2, 4): 9.09994574239e-10, (8, 9): 0.00381679388271, (1, 8): 0.00381679388271, (8, 8): 9.09994574239e-10, (2, 3): 0.00381679388271, (1, 9): 0.244274808493, (1, 1): 0.0, }, ]
+45 Stop [{(1, 3): 0.2388059676, (2, 9): 0.0074626864875, (5, 6): 0.0, (2, 8): 3.55848621726e-09, (9, 8): 0.0074626864875, (9, 3): 0.2388059676, (9, 9): 0.2388059676, (1, 4): 0.0074626864875, (2, 4): 3.55848621726e-09, (8, 9): 0.0074626864875, (1, 8): 0.0074626864875, (8, 8): 3.55848621726e-09, (2, 3): 0.0074626864875, (1, 9): 0.2388059676, (1, 1): 0.0, }, ]
+46 Stop [{(1, 3): 0.244274808493, (2, 9): 0.00381679388271, (5, 6): 0.0, (2, 8): 9.09994574239e-10, (9, 8): 0.00381679388271, (9, 3): 0.244274808493, (9, 9): 0.244274808493, (1, 4): 0.00381679388271, (2, 4): 9.09994574239e-10, (8, 9): 0.00381679388271, (1, 8): 0.00381679388271, (8, 8): 9.09994574239e-10, (2, 3): 0.00381679388271, (1, 9): 0.244274808493, (1, 1): 0.0, }, ]
+47 Stop [{(1, 3): 0.247104246934, (2, 9): 0.00193050192917, (5, 6): 0.0, (2, 8): 2.30133763453e-10, (9, 8): 0.00193050192917, (9, 3): 0.247104246934, (9, 9): 0.247104246934, (1, 4): 0.00193050192917, (2, 4): 2.30133763453e-10, (8, 9): 0.00193050192917, (1, 8): 0.00193050192917, (8, 8): 2.30133763453e-10, (2, 3): 0.00193050192917, (1, 9): 0.247104246934, (1, 1): 0.0, }, ]
+48 Stop [{(1, 3): 0.244274808493, (2, 9): 0.00381679388271, (5, 6): 0.0, (2, 8): 9.09994574239e-10, (9, 8): 0.00381679388271, (9, 3): 0.244274808493, (9, 9): 0.244274808493, (1, 4): 0.00381679388271, (2, 4): 9.09994574239e-10, (8, 9): 0.00381679388271, (1, 8): 0.00381679388271, (8, 8): 9.09994574239e-10, (2, 3): 0.00381679388271, (1, 9): 0.244274808493, (1, 1): 0.0, }, ]
+49 Stop [{(1, 3): 0.247104246934, (2, 9): 0.00193050192917, (5, 6): 0.0, (2, 8): 2.30133763453e-10, (9, 8): 0.00193050192917, (9, 3): 0.247104246934, (9, 9): 0.247104246934, (1, 4): 0.00193050192917, (2, 4): 2.30133763453e-10, (8, 9): 0.00193050192917, (1, 8): 0.00193050192917, (8, 8): 2.30133763453e-10, (2, 3): 0.00193050192917, (1, 9): 0.247104246934, (1, 1): 0.0, }, ]
+50 Stop [{(1, 3): 0.248543689277, (2, 9): 0.000970873786239, (5, 6): 0.0, (2, 8): 5.78685871505e-11, (9, 8): 0.000970873786239, (9, 3): 0.248543689277, (9, 9): 0.248543689277, (1, 4): 0.000970873786239, (2, 4): 5.78685871505e-11, (8, 9): 0.000970873786239, (1, 8): 0.000970873786239, (8, 8): 5.78685871505e-11, (2, 3): 0.000970873786239, (1, 9): 0.248543689277, (1, 1): 0.0, }, ]
+51 Stop [{(1, 3): 0.249269717613, (2, 9): 0.000486854917213, (5, 6): 0.0, (2, 8): 1.45094071988e-11, (9, 8): 0.000486854917213, (9, 3): 0.249269717613, (9, 9): 0.249269717613, (1, 4): 0.000486854917213, (2, 4): 1.45094071988e-11, (8, 9): 0.000486854917213, (1, 8): 0.000486854917213, (8, 8): 1.45094071988e-11, (2, 3): 0.000486854917213, (1, 9): 0.249269717613, (1, 1): 0.0, }, ]
+52 Stop [{(1, 3): 0.248543689277, (2, 9): 0.000970873786239, (5, 6): 0.0, (2, 8): 5.78685871505e-11, (9, 8): 0.000970873786239, (9, 3): 0.248543689277, (9, 9): 0.248543689277, (1, 4): 0.000970873786239, (2, 4): 5.78685871505e-11, (8, 9): 0.000970873786239, (1, 8): 0.000970873786239, (8, 8): 5.78685871505e-11, (2, 3): 0.000970873786239, (1, 9): 0.248543689277, (1, 1): 0.0, }, ]
+53 Stop [{(1, 3): 0.249269717613, (2, 9): 0.000486854917213, (5, 6): 0.0, (2, 8): 1.45094071988e-11, (9, 8): 0.000486854917213, (9, 3): 0.249269717613, (9, 9): 0.249269717613, (1, 4): 0.000486854917213, (2, 4): 1.45094071988e-11, (8, 9): 0.000486854917213, (1, 8): 0.000486854917213, (8, 8): 1.45094071988e-11, (2, 3): 0.000486854917213, (1, 9): 0.249269717613, (1, 1): 0.0, }, ]
+54 Stop [{(1, 3): 0.249634324717, (2, 9): 0.000243783520231, (5, 6): 0.0, (2, 8): 3.63265753137e-12, (9, 8): 0.000243783520231, (9, 3): 0.249634324717, (9, 9): 0.249634324717, (1, 4): 0.000243783520231, (2, 4): 3.63265753137e-12, (8, 9): 0.000243783520231, (1, 8): 0.000243783520231, (8, 8): 3.63265753137e-12, (2, 3): 0.000243783520231, (1, 9): 0.249634324717, (1, 1): 0.0, }, ]
+55 Stop [{(1, 3): 0.249817028543, (2, 9): 0.000121980970968, (5, 6): 0.0, (2, 8): 9.0882905549e-13, (9, 8): 0.000121980970968, (9, 3): 0.249817028543, (9, 9): 0.249817028543, (1, 4): 0.000121980970968, (2, 4): 9.0882905549e-13, (8, 9): 0.000121980970968, (1, 8): 0.000121980970968, (8, 8): 9.0882905549e-13, (2, 3): 0.000121980970968, (1, 9): 0.249817028543, (1, 1): 0.0, }, ]
+56 Stop [{(1, 3): 0.249908480781, (2, 9): 6.10128126906e-05, (5, 6): 0.0, (2, 8): 2.27290439198e-13, (9, 8): 6.10128126906e-05, (9, 3): 0.249908480781, (9, 9): 0.249908480781, (1, 4): 6.10128126906e-05, (2, 4): 2.27290439198e-13, (8, 9): 6.10128126906e-05, (1, 8): 6.10128126906e-05, (8, 8): 2.27290439198e-13, (2, 3): 6.10128126906e-05, (1, 9): 0.249908480781, (1, 1): 0.0, }, ]
+57 Stop [{(1, 3): 0.249954232013, (2, 9): 3.05119912125e-05, (5, 6): 0.0, (2, 8): 5.68330124254e-14, (9, 8): 3.05119912125e-05, (9, 3): 0.249954232013, (9, 9): 0.249954232013, (1, 4): 3.05119912125e-05, (2, 4): 5.68330124254e-14, (8, 9): 3.05119912125e-05, (1, 8): 3.05119912125e-05, (8, 8): 5.68330124254e-14, (2, 3): 3.05119912125e-05, (1, 9): 0.249954232013, (1, 1): 0.0, }, ]
+58 Stop [{(1, 3): 0.249977113912, (2, 9): 1.52573922065e-05, (5, 6): 0.0, (2, 8): 1.42095537917e-14, (9, 8): 1.52573922065e-05, (9, 3): 0.249977113912, (9, 9): 0.249977113912, (1, 4): 1.52573922065e-05, (2, 4): 1.42095537917e-14, (8, 9): 1.52573922065e-05, (1, 8): 1.52573922065e-05, (8, 8): 1.42095537917e-14, (2, 3): 1.52573922065e-05, (1, 9): 0.249977113912, (1, 1): 0.0, }, ]
+59 Stop [{(1, 3): 0.249988556432, (2, 9): 7.62904530127e-06, (5, 6): 0.0, (2, 8): 3.55255105592e-15, (9, 8): 7.62904530127e-06, (9, 3): 0.249988556432, (9, 9): 0.249988556432, (1, 4): 7.62904530127e-06, (2, 4): 3.55255105592e-15, (8, 9): 7.62904530127e-06, (1, 8): 7.62904530127e-06, (8, 8): 3.55255105592e-15, (2, 3): 7.62904530127e-06, (1, 9): 0.249988556432, (1, 1): 0.0, }, ]
+60 Stop [{(1, 3): 0.249994278085, (2, 9): 3.81460995613e-06, (5, 6): 0.0, (2, 8): 8.88158091375e-16, (9, 8): 3.81460995613e-06, (9, 3): 0.249994278085, (9, 9): 0.249994278085, (1, 4): 3.81460995613e-06, (2, 4): 8.88158091375e-16, (8, 9): 3.81460995613e-06, (1, 8): 3.81460995613e-06, (8, 8): 8.88158091375e-16, (2, 3): 3.81460995613e-06, (1, 9): 0.249994278085, (1, 1): 0.0, }, ]
+61 Stop [{(1, 3): 0.24999713901, (2, 9): 1.90732680519e-06, (5, 6): 0.0, (2, 8): 2.22042063855e-16, (9, 8): 1.90732680519e-06, (9, 3): 0.24999713901, (9, 9): 0.24999713901, (1, 4): 1.90732680519e-06, (2, 4): 2.22042063855e-16, (8, 9): 1.90732680519e-06, (1, 8): 1.90732680519e-06, (8, 8): 2.22042063855e-16, (2, 3): 1.90732680519e-06, (1, 9): 0.24999713901, (1, 1): 0.0, }, ]
+62 Stop [{(1, 3): 0.249994278085, (2, 9): 3.81460995613e-06, (5, 6): 0.0, (2, 8): 2.22039522844e-16, (9, 8): 3.81460995613e-06, (9, 3): 0.249994278085, (9, 9): 0.249994278085, (1, 4): 3.81460995613e-06, (2, 4): 2.22039522844e-16, (8, 9): 3.81460995613e-06, (1, 8): 3.81460995613e-06, (8, 8): 2.22039522844e-16, (2, 3): 3.81460995613e-06, (1, 9): 0.249994278085, (1, 1): 0.0, }, ]
+63 Stop [{(1, 3): 0.24999713901, (2, 9): 1.90732680519e-06, (5, 6): 0.0, (2, 8): 5.55105159638e-17, (9, 8): 1.90732680519e-06, (9, 3): 0.24999713901, (9, 9): 0.24999713901, (1, 4): 1.90732680519e-06, (2, 4): 5.55105159638e-17, (8, 9): 1.90732680519e-06, (1, 8): 1.90732680519e-06, (8, 8): 5.55105159638e-17, (2, 3): 1.90732680519e-06, (1, 9): 0.24999713901, (1, 1): 0.0, }, ]
+64 Stop [{(1, 3): 0.249998569497, (2, 9): 9.53668859469e-07, (5, 6): 0.0, (2, 8): 1.38777083989e-17, (9, 8): 9.53668859469e-07, (9, 3): 0.249998569497, (9, 9): 0.249998569497, (1, 4): 9.53668859469e-07, (2, 4): 1.38777083989e-17, (8, 9): 9.53668859469e-07, (1, 8): 9.53668859469e-07, (8, 8): 1.38777083989e-17, (2, 3): 9.53668859469e-07, (1, 9): 0.249998569497, (1, 1): 0.0, }, ]
+65 Stop [{(1, 3): 0.249999284746, (2, 9): 4.76835793965e-07, (5, 6): 0.0, (2, 8): 3.46943702581e-18, (9, 8): 4.76835793965e-07, (9, 3): 0.249999284746, (9, 9): 0.249999284746, (1, 4): 4.76835793965e-07, (2, 4): 3.46943702581e-18, (8, 9): 4.76835793965e-07, (1, 8): 4.76835793965e-07, (8, 8): 3.46943702581e-18, (2, 3): 4.76835793965e-07, (1, 9): 0.249999284746, (1, 1): 0.0, }, ]
+66 Stop [{(1, 3): 0.249999642373, (2, 9): 2.38418238042e-07, (5, 6): 0.0, (2, 8): 8.67360497219e-19, (9, 8): 2.38418238042e-07, (9, 3): 0.249999642373, (9, 9): 0.249999642373, (1, 4): 2.38418238042e-07, (2, 4): 8.67360497219e-19, (8, 9): 2.38418238042e-07, (1, 8): 2.38418238042e-07, (8, 8): 8.67360497219e-19, (2, 3): 2.38418238042e-07, (1, 9): 0.249999642373, (1, 1): 0.0, }, ]
+67 Stop [{(1, 3): 0.249999821186, (2, 9): 1.19209204286e-07, (5, 6): 0.0, (2, 8): 2.16840279401e-19, (9, 8): 1.19209204286e-07, (9, 3): 0.249999821186, (9, 9): 0.249999821186, (1, 4): 1.19209204286e-07, (2, 4): 2.16840279401e-19, (8, 9): 1.19209204286e-07, (1, 8): 1.19209204286e-07, (8, 8): 2.16840279401e-19, (2, 3): 1.19209204286e-07, (1, 9): 0.249999821186, (1, 1): 0.0, }, ]
+68 Stop [{(1, 3): 0.249999910593, (2, 9): 5.96046234591e-08, (5, 6): 0.0, (2, 8): 5.42100892372e-20, (9, 8): 5.96046234591e-08, (9, 3): 0.249999910593, (9, 9): 0.249999910593, (1, 4): 5.96046234591e-08, (2, 4): 5.42100892372e-20, (8, 9): 5.96046234591e-08, (1, 8): 5.96046234591e-08, (8, 8): 5.42100892372e-20, (2, 3): 5.96046234591e-08, (1, 9): 0.249999910593, (1, 1): 0.0, }, ]
+69 Stop [{(1, 3): 0.249999955297, (2, 9): 2.98023170586e-08, (5, 6): 0.0, (2, 8): 1.35525247327e-20, (9, 8): 2.98023170586e-08, (9, 3): 0.249999955297, (9, 9): 0.249999955297, (1, 4): 2.98023170586e-08, (2, 4): 1.35525247327e-20, (8, 9): 2.98023170586e-08, (1, 8): 2.98023170586e-08, (8, 8): 1.35525247327e-20, (2, 3): 2.98023170586e-08, (1, 9): 0.249999955297, (1, 1): 0.0, }, ]
+70 Stop [{(1, 3): 0.249999977648, (2, 9): 1.49011598616e-08, (5, 6): 0.0, (2, 8): 3.38813148609e-21, (9, 8): 1.49011598616e-08, (9, 3): 0.249999977648, (9, 9): 0.249999977648, (1, 4): 1.49011598616e-08, (2, 4): 3.38813148609e-21, (8, 9): 1.49011598616e-08, (1, 8): 1.49011598616e-08, (8, 8): 3.38813148609e-21, (2, 3): 1.49011598616e-08, (1, 9): 0.249999977648, (1, 1): 0.0, }, ]
+71 Stop [{(1, 3): 0.249999988824, (2, 9): 7.45058026386e-09, (5, 6): 0.0, (2, 8): 8.47032909389e-22, (9, 8): 7.45058026386e-09, (9, 3): 0.249999988824, (9, 9): 0.249999988824, (1, 4): 7.45058026386e-09, (2, 4): 8.47032909389e-22, (8, 9): 7.45058026386e-09, (1, 8): 7.45058026386e-09, (8, 8): 8.47032909389e-22, (2, 3): 7.45058026386e-09, (1, 9): 0.249999988824, (1, 1): 0.0, }, ]
+72 Stop [{(1, 3): 0.249999994412, (2, 9): 3.7252902152e-09, (5, 6): 0.0, (2, 8): 2.1175823208e-22, (9, 8): 3.7252902152e-09, (9, 3): 0.249999994412, (9, 9): 0.249999994412, (1, 4): 3.7252902152e-09, (2, 4): 2.1175823208e-22, (8, 9): 3.7252902152e-09, (1, 8): 3.7252902152e-09, (8, 8): 2.1175823208e-22, (2, 3): 3.7252902152e-09, (1, 9): 0.249999994412, (1, 1): 0.0, }, ]
+73 Stop [{(1, 3): 0.249999997206, (2, 9): 1.86264512841e-09, (5, 6): 0.0, (2, 8): 5.29395586117e-23, (9, 8): 1.86264512841e-09, (9, 3): 0.249999997206, (9, 9): 0.249999997206, (1, 4): 1.86264512841e-09, (2, 4): 5.29395586117e-23, (8, 9): 1.86264512841e-09, (1, 8): 1.86264512841e-09, (8, 8): 5.29395586117e-23, (2, 3): 1.86264512841e-09, (1, 9): 0.249999997206, (1, 1): 0.0, }, ]
+74 Stop [{(1, 3): 0.249999994412, (2, 9): 3.7252902152e-09, (5, 6): 0.0, (2, 8): 5.29395580201e-23, (9, 8): 3.7252902152e-09, (9, 3): 0.249999994412, (9, 9): 0.249999994412, (1, 4): 3.7252902152e-09, (2, 4): 5.29395580201e-23, (8, 9): 3.7252902152e-09, (1, 8): 3.7252902152e-09, (8, 8): 5.29395580201e-23, (2, 3): 3.7252902152e-09, (1, 9): 0.249999994412, (1, 1): 0.0, }, ]
+75 Stop [{(1, 3): 0.249999997206, (2, 9): 1.86264512841e-09, (5, 6): 0.0, (2, 8): 1.32348896529e-23, (9, 8): 1.86264512841e-09, (9, 3): 0.249999997206, (9, 9): 0.249999997206, (1, 4): 1.86264512841e-09, (2, 4): 1.32348896529e-23, (8, 9): 1.86264512841e-09, (1, 8): 1.86264512841e-09, (8, 8): 1.32348896529e-23, (2, 3): 1.86264512841e-09, (1, 9): 0.249999997206, (1, 1): 0.0, }, ]
+76 Stop [{(1, 3): 0.249999998603, (2, 9): 9.31322569411e-10, (5, 6): 0.0, (2, 8): 3.30872243172e-24, (9, 8): 9.31322569411e-10, (9, 3): 0.249999998603, (9, 9): 0.249999998603, (1, 4): 9.31322569411e-10, (2, 4): 3.30872243172e-24, (8, 9): 9.31322569411e-10, (1, 8): 9.31322569411e-10, (8, 8): 3.30872243172e-24, (2, 3): 9.31322569411e-10, (1, 9): 0.249999998603, (1, 1): 0.0, }, ]
+77 Stop [{(1, 3): 0.249999999302, (2, 9): 4.65661286007e-10, (5, 6): 0.0, (2, 8): 8.27180610242e-25, (9, 8): 4.65661286007e-10, (9, 3): 0.249999999302, (9, 9): 0.249999999302, (1, 4): 4.65661286007e-10, (2, 4): 8.27180610242e-25, (8, 9): 4.65661286007e-10, (1, 8): 4.65661286007e-10, (8, 8): 8.27180610242e-25, (2, 3): 4.65661286007e-10, (1, 9): 0.249999999302, (1, 1): 0.0, }, ]
+78 Stop [{(1, 3): 0.249999998603, (2, 9): 9.31322569411e-10, (5, 6): 0.0, (2, 8): 8.27180607931e-25, (9, 8): 9.31322569411e-10, (9, 3): 0.249999998603, (9, 9): 0.249999998603, (1, 4): 9.31322569411e-10, (2, 4): 8.27180607931e-25, (8, 9): 9.31322569411e-10, (1, 8): 9.31322569411e-10, (8, 8): 8.27180607931e-25, (2, 3): 9.31322569411e-10, (1, 9): 0.249999998603, (1, 1): 0.0, }, ]
+79 Stop [{(1, 3): 0.249999999302, (2, 9): 4.65661286007e-10, (5, 6): 0.0, (2, 8): 2.0679515256e-25, (9, 8): 4.65661286007e-10, (9, 3): 0.249999999302, (9, 9): 0.249999999302, (1, 4): 4.65661286007e-10, (2, 4): 2.0679515256e-25, (8, 9): 4.65661286007e-10, (1, 8): 4.65661286007e-10, (8, 8): 2.0679515256e-25, (2, 3): 4.65661286007e-10, (1, 9): 0.249999999302, (1, 1): 0.0, }, ]
+80 Stop [{(1, 3): 0.249999999651, (2, 9): 2.32830643329e-10, (5, 6): 0.0, (2, 8): 5.16987882123e-26, (9, 8): 2.32830643329e-10, (9, 3): 0.249999999651, (9, 9): 0.249999999651, (1, 4): 2.32830643329e-10, (2, 4): 5.16987882123e-26, (8, 9): 2.32830643329e-10, (1, 8): 2.32830643329e-10, (8, 8): 5.16987882123e-26, (2, 3): 2.32830643329e-10, (1, 9): 0.249999999651, (1, 1): 0.0, }, ]
+81 Stop [{(1, 3): 0.249999999302, (2, 9): 4.65661286007e-10, (5, 6): 0.0, (2, 8): 5.16987881401e-26, (9, 8): 4.65661286007e-10, (9, 3): 0.249999999302, (9, 9): 0.249999999302, (1, 4): 4.65661286007e-10, (2, 4): 5.16987881401e-26, (8, 9): 4.65661286007e-10, (1, 8): 4.65661286007e-10, (8, 8): 5.16987881401e-26, (2, 3): 4.65661286007e-10, (1, 9): 0.249999999302, (1, 1): 0.0, }, ]
+82 Stop [{(1, 3): 0.249999999651, (2, 9): 2.32830643329e-10, (5, 6): 0.0, (2, 8): 1.29246970531e-26, (9, 8): 2.32830643329e-10, (9, 3): 0.249999999651, (9, 9): 0.249999999651, (1, 4): 2.32830643329e-10, (2, 4): 1.29246970531e-26, (8, 9): 2.32830643329e-10, (1, 8): 2.32830643329e-10, (8, 8): 1.29246970531e-26, (2, 3): 2.32830643329e-10, (1, 9): 0.249999999651, (1, 1): 0.0, }, ]
+83 Stop [{(1, 3): 0.249999999302, (2, 9): 4.65661286007e-10, (5, 6): 0.0, (2, 8): 5.16987881401e-26, (9, 8): 4.65661286007e-10, (9, 3): 0.249999999302, (9, 9): 0.249999999302, (1, 4): 4.65661286007e-10, (2, 4): 5.16987881401e-26, (8, 9): 4.65661286007e-10, (1, 8): 4.65661286007e-10, (8, 8): 5.16987881401e-26, (2, 3): 4.65661286007e-10, (1, 9): 0.249999999302, (1, 1): 0.0, }, ]
+84 Stop [{(1, 3): 0.249999999651, (2, 9): 2.32830643329e-10, (5, 6): 0.0, (2, 8): 1.29246970531e-26, (9, 8): 2.32830643329e-10, (9, 3): 0.249999999651, (9, 9): 0.249999999651, (1, 4): 2.32830643329e-10, (2, 4): 1.29246970531e-26, (8, 9): 2.32830643329e-10, (1, 8): 2.32830643329e-10, (8, 8): 1.29246970531e-26, (2, 3): 2.32830643329e-10, (1, 9): 0.249999999651, (1, 1): 0.0, }, ]
+85 Stop [{(1, 3): 0.249999999825, (2, 9): 1.16415321746e-10, (5, 6): 0.0, (2, 8): 3.23117426553e-27, (9, 8): 1.16415321746e-10, (9, 3): 0.249999999825, (9, 9): 0.249999999825, (1, 4): 1.16415321746e-10, (2, 4): 3.23117426553e-27, (8, 9): 1.16415321746e-10, (1, 8): 1.16415321746e-10, (8, 8): 3.23117426553e-27, (2, 3): 1.16415321746e-10, (1, 9): 0.249999999825, (1, 1): 0.0, }, ]
+86 Stop [{(1, 3): 0.249999999651, (2, 9): 2.32830643329e-10, (5, 6): 0.0, (2, 8): 3.23117426327e-27, (9, 8): 2.32830643329e-10, (9, 3): 0.249999999651, (9, 9): 0.249999999651, (1, 4): 2.32830643329e-10, (2, 4): 3.23117426327e-27, (8, 9): 2.32830643329e-10, (1, 8): 2.32830643329e-10, (8, 8): 3.23117426327e-27, (2, 3): 2.32830643329e-10, (1, 9): 0.249999999651, (1, 1): 0.0, }, ]
+87 Stop [{(1, 3): 0.249999999302, (2, 9): 4.65661286007e-10, (5, 6): 0.0, (2, 8): 1.2924697035e-26, (9, 8): 4.65661286007e-10, (9, 3): 0.249999999302, (9, 9): 0.249999999302, (1, 4): 4.65661286007e-10, (2, 4): 1.2924697035e-26, (8, 9): 4.65661286007e-10, (1, 8): 4.65661286007e-10, (8, 8): 1.2924697035e-26, (2, 3): 4.65661286007e-10, (1, 9): 0.249999999302, (1, 1): 0.0, }, ]
+88 Stop [{(1, 3): 0.249999999651, (2, 9): 2.32830643329e-10, (5, 6): 0.0, (2, 8): 3.23117426327e-27, (9, 8): 2.32830643329e-10, (9, 3): 0.249999999651, (9, 9): 0.249999999651, (1, 4): 2.32830643329e-10, (2, 4): 3.23117426327e-27, (8, 9): 2.32830643329e-10, (1, 8): 2.32830643329e-10, (8, 8): 3.23117426327e-27, (2, 3): 2.32830643329e-10, (1, 9): 0.249999999651, (1, 1): 0.0, }, ]
+89 Stop [{(1, 3): 0.249999999825, (2, 9): 1.16415321746e-10, (5, 6): 0.0, (2, 8): 8.07793566382e-28, (9, 8): 1.16415321746e-10, (9, 3): 0.249999999825, (9, 9): 0.249999999825, (1, 4): 1.16415321746e-10, (2, 4): 8.07793566382e-28, (8, 9): 1.16415321746e-10, (1, 8): 1.16415321746e-10, (8, 8): 8.07793566382e-28, (2, 3): 1.16415321746e-10, (1, 9): 0.249999999825, (1, 1): 0.0, }, ]
+90 Stop [{(1, 3): 0.249999999651, (2, 9): 2.32830643329e-10, (5, 6): 0.0, (2, 8): 8.07793565818e-28, (9, 8): 2.32830643329e-10, (9, 3): 0.249999999651, (9, 9): 0.249999999651, (1, 4): 2.32830643329e-10, (2, 4): 8.07793565818e-28, (8, 9): 2.32830643329e-10, (1, 8): 2.32830643329e-10, (8, 8): 8.07793565818e-28, (2, 3): 2.32830643329e-10, (1, 9): 0.249999999651, (1, 1): 0.0, }, ]
+91 Stop [{(1, 3): 0.249999999302, (2, 9): 4.65661286007e-10, (5, 6): 0.0, (2, 8): 3.23117425876e-27, (9, 8): 4.65661286007e-10, (9, 3): 0.249999999302, (9, 9): 0.249999999302, (1, 4): 4.65661286007e-10, (2, 4): 3.23117425876e-27, (8, 9): 4.65661286007e-10, (1, 8): 4.65661286007e-10, (8, 8): 3.23117425876e-27, (2, 3): 4.65661286007e-10, (1, 9): 0.249999999302, (1, 1): 0.0, }, ]
+92 Stop [{(1, 3): 0.249999998603, (2, 9): 9.31322569411e-10, (5, 6): 0.0, (2, 8): 1.29246969989e-26, (9, 8): 9.31322569411e-10, (9, 3): 0.249999998603, (9, 9): 0.249999998603, (1, 4): 9.31322569411e-10, (2, 4): 1.29246969989e-26, (8, 9): 9.31322569411e-10, (1, 8): 9.31322569411e-10, (8, 8): 1.29246969989e-26, (2, 3): 9.31322569411e-10, (1, 9): 0.249999998603, (1, 1): 0.0, }, ]
+93 Stop [{(1, 3): 0.249999999302, (2, 9): 4.65661286007e-10, (5, 6): 0.0, (2, 8): 3.23117425876e-27, (9, 8): 4.65661286007e-10, (9, 3): 0.249999999302, (9, 9): 0.249999999302, (1, 4): 4.65661286007e-10, (2, 4): 3.23117425876e-27, (8, 9): 4.65661286007e-10, (1, 8): 4.65661286007e-10, (8, 8): 3.23117425876e-27, (2, 3): 4.65661286007e-10, (1, 9): 0.249999999302, (1, 1): 0.0, }, ]
+94 Stop [{(1, 3): 0.249999999651, (2, 9): 2.32830643329e-10, (5, 6): 0.0, (2, 8): 8.07793565818e-28, (9, 8): 2.32830643329e-10, (9, 3): 0.249999999651, (9, 9): 0.249999999651, (1, 4): 2.32830643329e-10, (2, 4): 8.07793565818e-28, (8, 9): 2.32830643329e-10, (1, 8): 2.32830643329e-10, (8, 8): 8.07793565818e-28, (2, 3): 2.32830643329e-10, (1, 9): 0.249999999651, (1, 1): 0.0, }, ]
+95 Stop [{(1, 3): 0.249999999825, (2, 9): 1.16415321746e-10, (5, 6): 0.0, (2, 8): 2.01948391596e-28, (9, 8): 1.16415321746e-10, (9, 3): 0.249999999825, (9, 9): 0.249999999825, (1, 4): 1.16415321746e-10, (2, 4): 2.01948391596e-28, (8, 9): 1.16415321746e-10, (1, 8): 1.16415321746e-10, (8, 8): 2.01948391596e-28, (2, 3): 1.16415321746e-10, (1, 9): 0.249999999825, (1, 1): 0.0, }, ]
+96 Stop [{(1, 3): 0.249999999913, (2, 9): 5.82076608931e-11, (5, 6): 0.0, (2, 8): 5.04870979165e-29, (9, 8): 5.82076608931e-11, (9, 3): 0.249999999913, (9, 9): 0.249999999913, (1, 4): 5.82076608931e-11, (2, 4): 5.04870979165e-29, (8, 9): 5.82076608931e-11, (1, 8): 5.82076608931e-11, (8, 8): 5.04870979165e-29, (2, 3): 5.82076608931e-11, (1, 9): 0.249999999913, (1, 1): 0.0, }, ]
+97 Stop [{(1, 3): 0.249999999825, (2, 9): 1.16415321746e-10, (5, 6): 0.0, (2, 8): 2.01948391596e-28, (9, 8): 1.16415321746e-10, (9, 3): 0.249999999825, (9, 9): 0.249999999825, (1, 4): 1.16415321746e-10, (2, 4): 2.01948391596e-28, (8, 9): 1.16415321746e-10, (1, 8): 1.16415321746e-10, (8, 8): 2.01948391596e-28, (2, 3): 1.16415321746e-10, (1, 9): 0.249999999825, (1, 1): 0.0, }, ]
+98 Stop [{(1, 3): 0.249999999913, (2, 9): 5.82076608931e-11, (5, 6): 0.0, (2, 8): 5.04870979165e-29, (9, 8): 5.82076608931e-11, (9, 3): 0.249999999913, (9, 9): 0.249999999913, (1, 4): 5.82076608931e-11, (2, 4): 5.04870979165e-29, (8, 9): 5.82076608931e-11, (1, 8): 5.82076608931e-11, (8, 8): 5.04870979165e-29, (2, 3): 5.82076608931e-11, (1, 9): 0.249999999913, (1, 1): 0.0, }, ]
+99 Stop [{(1, 3): 0.249999999825, (2, 9): 1.16415321746e-10, (5, 6): 0.0, (2, 8): 2.01948391596e-28, (9, 8): 1.16415321746e-10, (9, 3): 0.249999999825, (9, 9): 0.249999999825, (1, 4): 1.16415321746e-10, (2, 4): 2.01948391596e-28, (8, 9): 1.16415321746e-10, (1, 8): 1.16415321746e-10, (8, 8): 2.01948391596e-28, (2, 3): 1.16415321746e-10, (1, 9): 0.249999999825, (1, 1): 0.0, }, ]
+100 Stop [{(1, 3): 0.249999999651, (2, 9): 2.32830643329e-10, (5, 6): 0.0, (2, 8): 8.07793565818e-28, (9, 8): 2.32830643329e-10, (9, 3): 0.249999999651, (9, 9): 0.249999999651, (1, 4): 2.32830643329e-10, (2, 4): 8.07793565818e-28, (8, 9): 2.32830643329e-10, (1, 8): 2.32830643329e-10, (8, 8): 8.07793565818e-28, (2, 3): 2.32830643329e-10, (1, 9): 0.249999999651, (1, 1): 0.0, }, ]
+101 None [{(1, 3): 0.249999999825, (2, 9): 1.16415321746e-10, (5, 6): 0.0, (2, 8): 2.01948391596e-28, (9, 8): 1.16415321746e-10, (9, 3): 0.249999999825, (9, 9): 0.249999999825, (1, 4): 1.16415321746e-10, (2, 4): 2.01948391596e-28, (8, 9): 1.16415321746e-10, (1, 8): 1.16415321746e-10, (8, 8): 2.01948391596e-28, (2, 3): 1.16415321746e-10, (1, 9): 0.249999999825, (1, 1): 0.0, }, ]
+"""

tracking/test_cases/q10/2-ParticleUpdate.test

@@ -0,0 +1,31 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%%%%%%
+%  %%%%%  %
+%  %%%%%  %
+%%%%%%%%%%%
+%%%%%P%%%%%
+%%%%%%%%%%%
+%  %%%%%%%%
+%  %%%%%%G%
+%%%%%%%%%%%
+% %%%%%%%%%
+%%%%%%%%%%%
+"""
+
+observe: "True"
+
+elapse: "False"
+
+checkUniform: "False"
+maxMoves: "100"
+numParticles: "5000"
+numGhosts: "1"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+L2Tolerance: "0.1"
+errorMsg: "Particle filter observe test: %d inference errors."
+

tracking/test_cases/q10/3-ParticleUpdate.solution

@@ -0,0 +1,105 @@
+# move_number action likelihood_dictionary
+correctActions: """
+0 None [{(4, 7): 0.0454545454545, (1, 3): 0.0454545454545, (6, 6): 0.0454545454545, (5, 6): 0.0454545454545, (2, 8): 0.0454545454545, (9, 8): 0.0454545454545, (8, 9): 0.0454545454545, (6, 7): 0.0454545454545, (5, 5): 0.0454545454545, (2, 9): 0.0454545454545, (4, 5): 0.0454545454545, (9, 3): 0.0454545454545, (1, 4): 0.0454545454545, (2, 3): 0.0454545454545, (1, 9): 0.0454545454545, (6, 5): 0.0454545454545, (4, 6): 0.0454545454545, (5, 7): 0.0454545454545, (9, 9): 0.0454545454545, (1, 8): 0.0454545454545, (8, 8): 0.0454545454545, (2, 4): 0.0454545454545, }, ]
+1 South [{(4, 7): 0.00393700787402, (1, 3): 0.125984251969, (6, 6): 0.00196850393701, (5, 6): 0.0, (2, 8): 0.0314960629921, (9, 8): 0.0629921259843, (8, 9): 0.0629921259843, (6, 7): 0.00393700787402, (5, 5): 0.00196850393701, (2, 9): 0.0629921259843, (1, 1): 0.0, (4, 5): 0.00393700787402, (9, 3): 0.125984251969, (1, 4): 0.0629921259843, (2, 3): 0.0629921259843, (1, 9): 0.125984251969, (6, 5): 0.00393700787402, (4, 6): 0.00196850393701, (5, 7): 0.00196850393701, (9, 9): 0.125984251969, (1, 8): 0.0629921259843, (8, 8): 0.0314960629921, (2, 4): 0.0314960629921, }, ]
+2 East [{(4, 7): 0.00864164191196, (1, 3): 0.138266270591, (6, 6): 0.00216041047799, (5, 6): 0.0, (2, 8): 0.0345665676479, (9, 8): 0.0345665676479, (8, 9): 0.0345665676479, (6, 7): 0.00864164191196, (5, 5): 0.0, (2, 9): 0.0345665676479, (1, 1): 0.0, (4, 5): 0.00418579530111, (9, 3): 0.138266270591, (1, 4): 0.138266270591, (2, 3): 0.138266270591, (1, 9): 0.0345665676479, (6, 5): 0.00418579530111, (4, 6): 0.00216041047799, (5, 7): 0.00216041047799, (9, 9): 0.0345665676479, (1, 8): 0.0345665676479, (8, 8): 0.0345665676479, (2, 4): 0.138266270591, }, ]
+3 West [{(4, 7): 0.00214745283573, (1, 3): 0.274873962973, (6, 6): 0.000100661851675, (5, 6): 0.0, (2, 8): 0.0687184907433, (9, 8): 0.0343592453717, (8, 9): 0.0343592453717, (6, 7): 0.000536863208932, (5, 5): 0.0, (2, 9): 0.0343592453717, (1, 1): 0.0, (4, 5): 0.000260043116826, (9, 3): 0.0687184907433, (1, 4): 0.137436981487, (2, 3): 0.137436981487, (1, 9): 0.0171796226858, (6, 5): 0.0, (4, 6): 0.000268431604466, (5, 7): 0.000268431604466, (9, 9): 0.0687184907433, (1, 8): 0.0343592453717, (8, 8): 0.0171796226858, (2, 4): 0.0687184907433, }, ]
+4 East [{(4, 7): 0.000223143123433, (1, 3): 0.228498558395, (6, 6): 5.22991695546e-06, (5, 6): 0.0, (2, 8): 0.0571246395988, (9, 8): 0.0571246395988, (8, 9): 0.0571246395988, (6, 7): 5.57857808582e-05, (5, 5): 0.0, (2, 9): 0.0571246395988, (1, 1): 0.0, (4, 5): 6.7553094008e-06, (9, 3): 0.0571246395988, (1, 4): 0.0571246395988, (2, 3): 0.0571246395988, (1, 9): 0.0571246395988, (6, 5): 0.0, (4, 6): 1.39464452145e-05, (5, 7): 1.39464452145e-05, (9, 9): 0.228498558395, (1, 8): 0.0571246395988, (8, 8): 0.0142811598997, (2, 4): 0.0142811598997, }, ]
+5 North [{(4, 7): 0.000322046872051, (1, 3): 0.16488799849, (6, 6): 1.76905630399e-06, (5, 6): 0.0, (2, 8): 0.0412219996225, (9, 8): 0.082443999245, (8, 9): 0.082443999245, (6, 7): 2.01279295032e-05, (5, 5): 0.0, (2, 9): 0.0206109998113, (1, 1): 0.0, (4, 5): 2.43736646328e-06, (9, 3): 0.16488799849, (1, 4): 0.082443999245, (2, 3): 0.082443999245, (1, 9): 0.0103054999056, (6, 5): 0.0, (4, 6): 1.00639647516e-05, (5, 7): 1.00639647516e-05, (9, 9): 0.16488799849, (1, 8): 0.0206109998113, (8, 8): 0.0412219996225, (2, 4): 0.0412219996225, }, ]
+6 South [{(4, 7): 3.68499725789e-05, (1, 3): 0.150937487683, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0377343719208, (9, 8): 0.0377343719208, (8, 9): 0.0377343719208, (6, 7): 8.63671232318e-07, (5, 5): 0.0, (2, 9): 0.0377343719208, (1, 1): 0.0, (4, 5): 2.78893835436e-07, (9, 3): 0.150937487683, (1, 4): 0.150937487683, (2, 3): 0.150937487683, (1, 9): 0.0094335929802, (6, 5): 0.0, (4, 6): 5.75780821545e-07, (5, 7): 5.75780821545e-07, (9, 9): 0.150937487683, (1, 8): 0.0377343719208, (8, 8): 0.0094335929802, (2, 4): 0.0377343719208, }, ]
+7 Stop [{(4, 7): 0.000152973182823, (1, 3): 0.0783222696052, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0195805674013, (9, 8): 0.0391611348026, (8, 9): 0.0391611348026, (6, 7): 3.5853089724e-06, (5, 5): 0.0, (2, 9): 0.00979028370064, (1, 1): 0.0, (4, 5): 1.15775602234e-06, (9, 3): 0.313289078421, (1, 4): 0.15664453921, (2, 3): 0.15664453921, (1, 9): 0.00122378546258, (6, 5): 0.0, (4, 6): 4.78041196321e-06, (5, 7): 4.78041196321e-06, (9, 9): 0.0783222696052, (1, 8): 0.00979028370064, (8, 8): 0.0195805674013, (2, 4): 0.0783222696052, }, ]
+8 North [{(4, 7): 0.00046788244328, (1, 3): 0.0299444763699, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.00748611909247, (9, 8): 0.0299444763699, (8, 9): 0.0299444763699, (6, 7): 2.74149869109e-06, (5, 5): 0.0, (2, 9): 0.00187152977312, (1, 1): 0.0, (4, 5): 8.85275618998e-07, (9, 3): 0.479111621918, (1, 4): 0.11977790548, (2, 3): 0.11977790548, (1, 9): 0.00011697061082, (6, 5): 0.0, (4, 6): 7.31066317624e-06, (5, 7): 7.31066317624e-06, (9, 9): 0.0299444763699, (1, 8): 0.00187152977312, (8, 8): 0.0299444763699, (2, 4): 0.11977790548, }, ]
+9 South [{(4, 7): 4.9127930802e-05, (1, 3): 0.0251535005706, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.00628837514265, (9, 8): 0.0125767502853, (8, 9): 0.0125767502853, (6, 7): 1.07947113579e-07, (5, 5): 0.0, (2, 9): 0.00314418757133, (1, 1): 0.0, (4, 5): 9.29544589149e-08, (9, 3): 0.40245600913, (1, 4): 0.201228004565, (2, 3): 0.201228004565, (1, 9): 9.8255861604e-05, (6, 5): 0.0, (4, 6): 3.83811959391e-07, (5, 7): 3.83811959391e-07, (9, 9): 0.0251535005706, (1, 8): 0.00314418757133, (8, 8): 0.00628837514265, (2, 4): 0.100614002282, }, ]
+10 West [{(4, 7): 0.000133101778699, (1, 3): 0.00851851383675, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.00212962845919, (9, 8): 0.00851851383675, (8, 9): 0.00851851383675, (6, 7): 1.16983985185e-06, (5, 5): 0.0, (2, 9): 0.000532407114797, (1, 1): 0.0, (4, 5): 1.00736209465e-06, (9, 3): 0.545184885552, (1, 4): 0.136296221388, (2, 3): 0.136296221388, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 2.07971529218e-06, (5, 7): 2.07971529218e-06, (9, 9): 0.00851851383675, (1, 8): 0.000532407114797, (8, 8): 0.00851851383675, (2, 4): 0.136296221388, }, ]
+11 East [{(4, 7): 0.00015976921452, (1, 3): 0.00511261486465, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.00127815371616, (9, 8): 0.00255630743232, (8, 9): 0.00255630743232, (6, 7): 1.40422161199e-06, (5, 5): 0.0, (2, 9): 0.00015976921452, (1, 1): 0.0, (4, 5): 5.85701809517e-07, (9, 3): 0.327207351338, (1, 4): 0.163603675669, (2, 3): 0.163603675669, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 1.24819698844e-06, (5, 7): 1.24819698844e-06, (9, 9): 0.00127815371616, (1, 8): 0.00015976921452, (8, 8): 0.00511261486465, (2, 4): 0.327207351338, }, ]
+12 North [{(4, 7): 0.000385931123825, (1, 3): 0.0015437244953, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.000385931123825, (9, 8): 0.0015437244953, (8, 9): 0.0015437244953, (6, 7): 8.47993191999e-07, (5, 5): 0.0, (2, 9): 2.41206952391e-05, (1, 1): 0.0, (4, 5): 3.53698549267e-07, (9, 3): 0.395193470797, (1, 4): 0.0987983676992, (2, 3): 0.0987983676992, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 1.50754345244e-06, (5, 7): 1.50754345244e-06, (9, 9): 0.000385931123825, (1, 8): 2.41206952391e-05, (8, 8): 0.0061748979812, (2, 4): 0.395193470797, }, ]
+13 West [{(4, 7): 4.05005779962e-05, (1, 3): 0.00129601849588, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.00032400462397, (9, 8): 0.00064800924794, (8, 9): 0.00064800924794, (6, 7): 3.33714479339e-08, (5, 5): 0.0, (2, 9): 4.05005779962e-05, (1, 1): 0.0, (4, 5): 3.71180109543e-08, (9, 3): 0.331780734945, (1, 4): 0.165890367473, (2, 3): 0.165890367473, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 7.91026913989e-08, (5, 7): 7.91026913989e-08, (9, 9): 0.00032400462397, (1, 8): 4.05005779962e-05, (8, 8): 0.00129601849588, (2, 4): 0.331780734945, }, ]
+14 Stop [{(4, 7): 0.000555126164104, (1, 3): 0.000555126164104, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.000555126164104, (9, 8): 0.000555126164104, (8, 9): 0.000555126164104, (6, 7): 4.57409864222e-07, (5, 5): 0.0, (2, 9): 3.46953852565e-05, (1, 1): 0.0, (4, 5): 5.08762592034e-07, (9, 3): 0.142112298011, (1, 4): 0.142112298011, (2, 3): 0.142112298011, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 4.31998205098e-06, (5, 7): 4.31998205098e-06, (9, 9): 0.000138781541026, (1, 8): 3.46953852565e-05, (8, 8): 0.00222050465642, (2, 4): 0.568449192043, }, ]
+15 West [{(4, 7): 4.05005652565e-05, (1, 3): 0.00129601808821, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.000324004522052, (9, 8): 0.000648009044104, (8, 9): 0.000648009044104, (6, 7): 3.33714374367e-08, (5, 5): 0.0, (2, 9): 4.05005652565e-05, (1, 1): 0.0, (4, 5): 3.71179992786e-08, (9, 3): 0.331780630581, (1, 4): 0.165890315291, (2, 3): 0.165890315291, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 2.36381015177e-07, (5, 7): 2.36381015177e-07, (9, 9): 0.000324004522052, (1, 8): 4.05005652565e-05, (8, 8): 0.00129601808821, (2, 4): 0.331780630581, }, ]
+16 Stop [{(4, 7): 7.99743432144e-07, (1, 3): 0.000818937274515, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 5.11835796572e-05, (9, 8): 0.000818937274515, (8, 9): 0.000818937274515, (6, 7): 2.6358731284e-09, (5, 5): 0.0, (2, 9): 1.27958949143e-05, (1, 1): 0.0, (4, 5): 7.32949674883e-10, (9, 3): 0.838591769103, (1, 4): 0.052411985569, (2, 3): 0.052411985569, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 9.33538399643e-09, (9, 9): 0.000818937274515, (1, 8): 1.27958949143e-05, (8, 8): 0.000818937274515, (2, 4): 0.052411985569, }, ]
+17 Stop [{(4, 7): 7.29033685408e-09, (1, 3): 0.000238889758034, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 3.73265246929e-06, (9, 8): 0.000477779516069, (8, 9): 0.000477779516069, (6, 7): 9.61128393848e-11, (5, 5): 0.0, (2, 9): 1.86632623464e-06, (1, 1): 0.0, (4, 5): 6.68145534207e-12, (9, 3): 0.978492448909, (1, 4): 0.0076444722571, (2, 3): 0.0076444722571, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 1.70199819744e-10, (9, 9): 0.000955559032137, (1, 8): 1.86632623464e-06, (8, 8): 0.000238889758034, (2, 4): 0.00382223612855, }, ]
+18 East [{(4, 7): 5.79881817339e-11, (1, 3): 6.08050156498e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 2.37519592382e-07, (9, 8): 0.000243220062599, (8, 9): 0.000243220062599, (6, 7): 3.05797052113e-12, (5, 5): 0.0, (2, 9): 2.37519592382e-07, (1, 1): 0.0, (4, 5): 5.31450678313e-14, (9, 3): 0.996229376407, (1, 4): 0.000972880250398, (2, 3): 0.000972880250398, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 2.70757806558e-12, (9, 9): 0.000972880250398, (1, 8): 2.37519592382e-07, (8, 8): 6.08050156498e-05, (2, 4): 0.000243220062599, }, ]
+19 South [{(4, 7): 1.8147521519e-12, (1, 3): 6.08929776778e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 5.94657985135e-08, (9, 8): 0.000121785955356, (8, 9): 0.000121785955356, (6, 7): 9.56998205104e-14, (5, 5): 0.0, (2, 9): 1.18931597027e-07, (1, 1): 0.0, (4, 5): 1.66318589971e-15, (9, 3): 0.997670546273, (1, 4): 0.000487143821422, (2, 3): 0.000487143821422, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 4.23671080384e-14, (9, 9): 0.000974287642845, (1, 8): 1.18931597027e-07, (8, 8): 1.52232444194e-05, (2, 4): 6.08929776778e-05, }, ]
+20 West [{(4, 7): 2.26248113325e-13, (1, 3): 6.07330154695e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 5.93095854195e-08, (9, 8): 0.000242932061878, (8, 9): 0.000242932061878, (6, 7): 1.19310528511e-14, (5, 5): 0.0, (2, 9): 2.37238341678e-07, (1, 1): 0.0, (4, 5): 5.18380253089e-17, (9, 3): 0.995049725453, (1, 4): 0.000242932061878, (2, 3): 0.000242932061878, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 2.64098826132e-15, (9, 9): 0.00388691299005, (1, 8): 2.37238341678e-07, (8, 8): 1.51832538674e-05, (2, 4): 1.51832538674e-05, }, ]
+21 North [{(4, 7): 4.52283050598e-13, (1, 3): 0.000242817613857, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 2.37126576032e-07, (9, 8): 0.000121408806928, (8, 9): 0.000121408806928, (6, 7): 9.54034559856e-14, (5, 5): 0.0, (2, 9): 4.74253152064e-07, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.994580946357, (1, 4): 0.00194254091085, (2, 3): 0.00194254091085, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 1.05589762484e-14, (9, 9): 0.000971270455427, (1, 8): 4.74253152064e-07, (8, 8): 1.5176100866e-05, (2, 4): 6.07044034642e-05, }, ]
+22 East [{(4, 7): 0.0, (1, 3): 6.09344995784e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 6.09344995784e-05, (8, 9): 6.09344995784e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 5.95063472445e-08, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.998350841092, (1, 4): 0.000243737998314, (2, 3): 0.000243737998314, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 0.000974951993254, (1, 8): 5.95063472445e-08, (8, 8): 3.80840622365e-06, (2, 4): 0.0, }, ]
+23 East [{(4, 7): 0.0, (1, 3): 6.09532480849e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.04766240424e-05, (8, 9): 3.04766240424e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.97623281665e-08, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.998658016623, (1, 4): 0.00012190649617, (2, 3): 0.00012190649617, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 0.000975251969358, (1, 8): 2.97623281665e-08, (8, 8): 9.52394501327e-07, (2, 4): 0.0, }, ]
+24 South [{(4, 7): 0.0, (1, 3): 1.52398945435e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 6.0959578174e-05, (8, 9): 6.0959578174e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.48827095151e-08, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.998761728803, (1, 4): 6.0959578174e-05, (2, 3): 6.0959578174e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 0.000975353250784, (1, 8): 1.48827095151e-08, (8, 8): 3.80997363588e-06, (2, 4): 0.0, }, ]
+25 West [{(4, 7): 0.0, (1, 3): 1.52361796059e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 0.000121889436847, (8, 9): 0.000121889436847, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.43954082321e-09, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.998518266654, (1, 4): 0.000121889436847, (2, 3): 0.000121889436847, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 0.00097511549478, (1, 8): 7.43954082321e-09, (8, 8): 3.80904490148e-06, (2, 4): 0.0, }, ]
+26 Stop [{(4, 7): 0.0, (1, 3): 1.52454710287e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 6.0981884115e-05, (8, 9): 6.0981884115e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.72203882538e-09, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.99912718934, (1, 4): 0.00024392753646, (2, 3): 0.00024392753646, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 0.00024392753646, (1, 8): 3.72203882538e-09, (8, 8): 3.81136775719e-06, (2, 4): 0.0, }, ]
+27 East [{(4, 7): 0.0, (1, 3): 1.52417532022e-05, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.04835064043e-05, (8, 9): 3.04835064043e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.86056557644e-09, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.998883537857, (1, 4): 0.000487736102469, (2, 3): 0.000487736102469, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 6.09670128086e-05, (1, 8): 1.86056557644e-09, (8, 8): 3.81043830054e-06, (2, 4): 0.0, }, ]
+28 West [{(4, 7): 0.0, (1, 3): 3.81263201061e-06, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.52505280424e-05, (8, 9): 1.52505280424e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.32704590491e-10, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999458605789, (1, 4): 0.000244008448679, (2, 3): 0.000244008448679, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.52505280424e-05, (1, 8): 2.32704590491e-10, (8, 8): 3.81263201061e-06, (2, 4): 0.0, }, ]
+29 Stop [{(4, 7): 0.0, (1, 3): 3.81344620779e-06, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.05075696623e-05, (8, 9): 3.05075696623e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 4.65508570287e-10, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999672042695, (1, 4): 0.000122030278649, (2, 3): 0.000122030278649, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.52537848312e-05, (1, 8): 4.65508570287e-10, (8, 8): 3.81344620779e-06, (2, 4): 0.0, }, ]
+30 West [{(4, 7): 0.0, (1, 3): 3.8126756196e-06, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.52507024784e-05, (8, 9): 1.52507024784e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.32707252173e-10, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999470037623, (1, 4): 0.000244011239654, (2, 3): 0.000244011239654, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.8126756196e-06, (1, 8): 2.32707252173e-10, (8, 8): 3.8126756196e-06, (2, 4): 0.0, }, ]
+31 East [{(4, 7): 0.0, (1, 3): 9.53538820727e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.05132422633e-05, (8, 9): 3.05132422633e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.16398781827e-10, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999857922483, (1, 4): 3.05132422633e-05, (2, 3): 3.05132422633e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.52566211316e-05, (1, 8): 1.16398781827e-10, (8, 8): 3.81415528291e-06, (2, 4): 0.0, }, ]
+32 East [{(4, 7): 0.0, (1, 3): 9.53520636459e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.52563301834e-05, (8, 9): 1.52563301834e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 5.81982810339e-11, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999838854896, (1, 4): 6.10253207334e-05, (2, 3): 6.10253207334e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.81408254584e-06, (1, 8): 5.81982810339e-11, (8, 8): 3.81408254584e-06, (2, 4): 0.0, }, ]
+33 West [{(4, 7): 0.0, (1, 3): 2.38399197068e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 7.62877430618e-06, (8, 9): 7.62877430618e-06, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.27536612146e-12, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999918705859, (1, 4): 3.05150972247e-05, (2, 3): 3.05150972247e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 9.53596788272e-07, (1, 8): 7.27536612146e-12, (8, 8): 3.81438715309e-06, (2, 4): 0.0, }, ]
+34 North [{(4, 7): 0.0, (1, 3): 2.38386637378e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.81418619805e-06, (8, 9): 3.81418619805e-06, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.63749141507e-12, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999866026703, (1, 4): 6.10269791689e-05, (2, 3): 6.10269791689e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.38386637378e-07, (1, 8): 3.63749141507e-12, (8, 8): 3.81418619805e-06, (2, 4): 0.0, }, ]
+35 West [{(4, 7): 0.0, (1, 3): 2.3835572685e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 7.62738325921e-06, (8, 9): 7.62738325921e-06, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.2740395157e-12, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999736378552, (1, 4): 0.000122038132147, (2, 3): 0.000122038132147, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.3835572685e-07, (1, 8): 7.2740395157e-12, (8, 8): 3.81369162961e-06, (2, 4): 0.0, }, ]
+36 North [{(4, 7): 0.0, (1, 3): 5.96022965241e-08, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.81454697754e-06, (8, 9): 3.81454697754e-06, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 9.09458870301e-13, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.99996060288, (1, 4): 1.52581879102e-05, (2, 3): 1.52581879102e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.38409186096e-07, (1, 8): 9.09458870301e-13, (8, 8): 9.53636744385e-07, (2, 4): 0.0, }, ]
+37 East [{(4, 7): 0.0, (1, 3): 2.38344648745e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.05081150393e-05, (8, 9): 3.05081150393e-05, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.81842535969e-12, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999689913608, (1, 4): 0.000122032460157, (2, 3): 0.000122032460157, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 9.53378594979e-07, (1, 8): 1.81842535969e-12, (8, 8): 3.81351437991e-06, (2, 4): 0.0, }, ]
+38 South [{(4, 7): 0.0, (1, 3): 2.38387489806e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.81419983689e-06, (8, 9): 3.81419983689e-06, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.2734402638e-13, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999869602043, (1, 4): 6.10271973903e-05, (2, 3): 6.10271973903e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.38387489806e-07, (1, 8): 2.2734402638e-13, (8, 8): 2.38387489806e-07, (2, 4): 0.0, }, ]
+39 East [{(4, 7): 0.0, (1, 3): 2.38402990813e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.90722392651e-06, (8, 9): 1.90722392651e-06, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.13679404646e-13, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.99993461798, (1, 4): 3.05155828241e-05, (2, 3): 3.05155828241e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.38402990813e-07, (1, 8): 1.13679404646e-13, (8, 8): 5.96007477033e-08, (2, 4): 0.0, }, ]
+40 West [{(4, 7): 0.0, (1, 3): 2.38388906977e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 9.53555627909e-07, (8, 9): 9.53555627909e-07, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.273453779e-13, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999875546091, (1, 4): 6.10275601862e-05, (2, 3): 6.10275601862e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.38388906977e-07, (1, 8): 2.273453779e-13, (8, 8): 1.48993066861e-08, (2, 4): 0.0, }, ]
+41 Stop [{(4, 7): 0.0, (1, 3): 2.38359359419e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.90687487535e-06, (8, 9): 1.90687487535e-06, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 4.54634398306e-13, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999751614649, (1, 4): 0.000122039992023, (2, 3): 0.000122039992023, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.38359359419e-07, (1, 8): 4.54634398306e-13, (8, 8): 1.48974599637e-08, (2, 4): 0.0, }, ]
+42 North [{(4, 7): 0.0, (1, 3): 2.38388909641e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 9.53555638565e-07, (8, 9): 9.53555638565e-07, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.27345380441e-13, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999875557264, (1, 4): 6.10275608682e-05, (2, 3): 6.10275608682e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.38388909641e-07, (1, 8): 2.27345380441e-13, (8, 8): 3.72482671314e-09, (2, 4): 0.0, }, ]
+43 South [{(4, 7): 0.0, (1, 3): 2.38403900137e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.19201950068e-07, (8, 9): 1.19201950068e-07, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.84199595615e-14, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.99993843196, (1, 4): 3.05156992175e-05, (2, 3): 3.05156992175e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.96009750342e-08, (1, 8): 2.84199595615e-14, (8, 8): 2.32816308727e-10, (2, 4): 0.0, }, ]
+44 South [{(4, 7): 0.0, (1, 3): 2.38411203882e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 5.96028009706e-08, (8, 9): 5.96028009706e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.42104151179e-14, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999969066088, (1, 4): 1.52583170485e-05, (2, 3): 1.52583170485e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.96028009706e-08, (1, 8): 1.42104151179e-14, (8, 8): 5.82058603228e-11, (2, 4): 0.0, }, ]
+45 North [{(4, 7): 0.0, (1, 3): 2.38403953463e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.98004941828e-08, (8, 9): 2.98004941828e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.1049914796e-15, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999938655625, (1, 4): 3.05157060432e-05, (2, 3): 3.05157060432e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.49002470914e-08, (1, 8): 7.1049914796e-15, (8, 8): 5.82040902009e-11, (2, 4): 0.0, }, ]
+46 South [{(4, 7): 0.0, (1, 3): 2.38411235865e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.49007022416e-08, (8, 9): 1.49007022416e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.55260425605e-15, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999969200234, (1, 4): 1.52583190954e-05, (2, 3): 1.52583190954e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.49007022416e-08, (1, 8): 3.55260425605e-15, (8, 8): 1.45514670328e-11, (2, 4): 0.0, }, ]
+47 North [{(4, 7): 0.0, (1, 3): 2.38414855912e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.9801856989e-08, (8, 9): 2.9801856989e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.10531639791e-15, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999984383812, (1, 4): 7.62927538919e-06, (2, 3): 7.62927538919e-06, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.96037139781e-08, (1, 8): 7.10531639791e-15, (8, 8): 1.45516879829e-11, (2, 4): 0.0, }, ]
+48 West [{(4, 7): 0.0, (1, 3): 2.38411203882e-07, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 5.96028009706e-08, (8, 9): 5.96028009706e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.42104151179e-14, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999969066088, (1, 4): 1.52583170485e-05, (2, 3): 1.52583170485e-05, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.96028009706e-08, (1, 8): 1.42104151179e-14, (8, 8): 5.82058603228e-11, (2, 4): 0.0, }, ]
+49 Stop [{(4, 7): 0.0, (1, 3): 5.96044067437e-08, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.98022033718e-08, (8, 9): 2.98022033718e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.7763497455e-15, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.99999600649, (1, 4): 1.9073410158e-06, (2, 3): 1.9073410158e-06, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.96044067437e-08, (1, 8): 1.7763497455e-15, (8, 8): 1.45518571152e-11, (2, 4): 0.0, }, ]
+50 South [{(4, 7): 0.0, (1, 3): 1.49011525341e-08, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.49011525341e-08, (8, 9): 1.49011525341e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.22044475884e-16, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999418851, (1, 4): 2.38418440545e-07, (2, 3): 2.38418440545e-07, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.96046101362e-08, (1, 8): 2.22044475884e-16, (8, 8): 3.63797669289e-12, (2, 4): 0.0, }, ]
+51 Stop [{(4, 7): 0.0, (1, 3): 3.72528961844e-09, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.98023169475e-08, (8, 9): 2.98023169475e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.11022282196e-16, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999817457, (1, 4): 2.98023169475e-08, (2, 3): 2.98023169475e-08, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.9604633895e-08, (1, 8): 1.11022282196e-16, (8, 8): 3.637978143e-12, (2, 4): 0.0, }, ]
+52 Stop [{(4, 7): 0.0, (1, 3): 1.49011585293e-08, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.49011585293e-08, (8, 9): 1.49011585293e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.2204456522e-16, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999821182, (1, 4): 5.9604634117e-08, (2, 3): 5.9604634117e-08, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.49011585293e-08, (1, 8): 2.2204456522e-16, (8, 8): 3.63797815656e-12, (2, 4): 0.0, }, ]
+53 Stop [{(4, 7): 0.0, (1, 3): 3.72528978497e-09, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.98023182798e-08, (8, 9): 2.98023182798e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.11022287159e-16, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999862161, (1, 4): 7.45057956994e-09, (2, 3): 7.45057956994e-09, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.96046365595e-08, (1, 8): 1.11022287159e-16, (8, 8): 3.63797830564e-12, (2, 4): 0.0, }, ]
+54 North [{(4, 7): 0.0, (1, 3): 9.31322530377e-10, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.4901160486e-08, (8, 9): 1.4901160486e-08, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 5.55111485944e-17, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999952499, (1, 4): 9.31322530377e-10, (2, 3): 9.31322530377e-10, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.4901160486e-08, (1, 8): 5.55111485944e-17, (8, 8): 3.63797863428e-12, (2, 4): 0.0, }, ]
+55 North [{(4, 7): 0.0, (1, 3): 2.32830636606e-10, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 7.4505803714e-09, (8, 9): 7.4505803714e-09, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 6.93889369387e-18, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999969731, (1, 4): 1.16415318303e-10, (2, 3): 1.16415318303e-10, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.49011607428e-08, (1, 8): 6.93889369387e-18, (8, 8): 9.09494674244e-13, (2, 4): 0.0, }, ]
+56 East [{(4, 7): 0.0, (1, 3): 5.82076605831e-11, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 9.3132256933e-10, (8, 9): 9.3132256933e-10, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.16840433266e-19, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999994325, (1, 4): 1.45519151458e-11, (2, 3): 1.45519151458e-11, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.72529027732e-09, (1, 8): 2.16840433266e-19, (8, 8): 5.68434185382e-14, (2, 4): 0.0, }, ]
+57 Stop [{(4, 7): 0.0, (1, 3): 5.82076608415e-11, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.16415321683e-10, (8, 9): 1.16415321683e-10, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.71050542786e-20, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999998763, (1, 4): 7.27595760518e-12, (2, 3): 7.27595760518e-12, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 9.31322573464e-10, (1, 8): 2.71050542786e-20, (8, 8): 3.55271367441e-15, (2, 4): 0.0, }, ]
+58 South [{(4, 7): 0.0, (1, 3): 5.82076608944e-11, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.45519152236e-11, (8, 9): 1.45519152236e-11, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.38813178791e-21, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999673, (1, 4): 3.6379788059e-12, (2, 3): 3.6379788059e-12, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.32830643578e-10, (1, 8): 3.38813178791e-21, (8, 8): 2.22044604852e-16, (2, 4): 0.0, }, ]
+59 West [{(4, 7): 0.0, (1, 3): 5.82076608955e-11, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 7.27595761193e-12, (8, 9): 7.27595761193e-12, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.69406589398e-21, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999691, (1, 4): 1.81898940298e-12, (2, 3): 1.81898940298e-12, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.32830643582e-10, (1, 8): 1.69406589398e-21, (8, 8): 5.55111512141e-17, (2, 4): 0.0, }, ]
+60 South [{(4, 7): 0.0, (1, 3): 1.45519152247e-11, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.63797880616e-12, (8, 9): 3.63797880616e-12, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.1175823676e-22, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999745, (1, 4): 2.27373675385e-13, (2, 3): 2.27373675385e-13, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.32830643594e-10, (1, 8): 2.1175823676e-22, (8, 8): 1.38777878043e-17, (2, 4): 0.0, }, ]
+61 North [{(4, 7): 0.0, (1, 3): 3.63797880618e-12, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 7.27595761236e-12, (8, 9): 7.27595761236e-12, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.0587911838e-22, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999749, (1, 4): 2.84217094233e-14, (2, 3): 2.84217094233e-14, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.32830643595e-10, (1, 8): 1.0587911838e-22, (8, 8): 1.38777878043e-17, (2, 4): 0.0, }, ]
+62 North [{(4, 7): 0.0, (1, 3): 3.63797880613e-12, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.45519152245e-11, (8, 9): 1.45519152245e-11, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 5.29395591893e-23, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999734, (1, 4): 1.42108547114e-14, (2, 3): 1.42108547114e-14, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.32830643592e-10, (1, 8): 5.29395591893e-23, (8, 8): 1.38777878041e-17, (2, 4): 0.0, }, ]
+63 Stop [{(4, 7): 0.0, (1, 3): 9.09494701716e-13, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.81898940343e-12, (8, 9): 1.81898940343e-12, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.654361225e-24, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999937, (1, 4): 1.77635683929e-15, (2, 3): 1.77635683929e-15, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.82076609098e-11, (1, 8): 1.654361225e-24, (8, 8): 8.67361737934e-19, (2, 4): 0.0, }, ]
+64 East [{(4, 7): 0.0, (1, 3): 2.2737367544e-13, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.2737367544e-13, (8, 9): 2.2737367544e-13, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 5.16987882838e-26, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999985, (1, 4): 2.22044604922e-16, (2, 3): 2.22044604922e-16, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.45519152281e-11, (1, 8): 5.16987882838e-26, (8, 8): 5.42101086234e-20, (2, 4): 0.0, }, ]
+65 East [{(4, 7): 0.0, (1, 3): 2.27373675442e-13, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.84217094303e-14, (8, 9): 2.84217094303e-14, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 6.46234853555e-27, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999996, (1, 4): 1.11022302462e-16, (2, 3): 1.11022302462e-16, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.63797880708e-12, (1, 8): 6.46234853555e-27, (8, 8): 3.388131789e-21, (2, 4): 0.0, }, ]
+66 Stop [{(4, 7): 0.0, (1, 3): 9.09494701771e-13, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.55271367879e-15, (8, 9): 3.55271367879e-15, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.23117426778e-27, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 0.999999999998, (1, 4): 2.22044604925e-16, (2, 3): 2.22044604925e-16, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 9.09494701771e-13, (1, 8): 3.23117426778e-27, (8, 8): 2.11758236813e-22, (2, 4): 0.0, }, ]
+67 West [{(4, 7): 0.0, (1, 3): 2.27373675443e-13, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 4.4408920985e-16, (8, 9): 4.4408920985e-16, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.61558713389e-27, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 1.11022302462e-16, (2, 3): 1.11022302462e-16, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.27373675443e-13, (1, 8): 1.61558713389e-27, (8, 8): 1.32348898008e-23, (2, 4): 0.0, }, ]
+68 West [{(4, 7): 0.0, (1, 3): 2.27373675443e-13, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 5.55111512312e-17, (8, 9): 5.55111512312e-17, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.01948391737e-28, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 5.55111512312e-17, (2, 3): 5.55111512312e-17, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.68434188608e-14, (1, 8): 2.01948391737e-28, (8, 8): 8.27180612553e-25, (2, 4): 0.0, }, ]
+69 Stop [{(4, 7): 0.0, (1, 3): 5.68434188608e-14, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 6.93889390391e-18, (8, 9): 6.93889390391e-18, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 6.31088724177e-30, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 6.93889390391e-18, (2, 3): 6.93889390391e-18, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.42108547152e-14, (1, 8): 6.31088724177e-30, (8, 8): 5.16987882846e-26, (2, 4): 0.0, }, ]
+70 East [{(4, 7): 0.0, (1, 3): 1.42108547152e-14, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 8.67361737988e-19, (8, 9): 8.67361737988e-19, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.97215226305e-31, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 8.67361737988e-19, (2, 3): 8.67361737988e-19, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.5527136788e-15, (1, 8): 1.97215226305e-31, (8, 8): 3.23117426779e-27, (2, 4): 0.0, }, ]
+71 Stop [{(4, 7): 0.0, (1, 3): 1.42108547152e-14, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.08420217249e-19, (8, 9): 1.08420217249e-19, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.46519032882e-32, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 4.33680868994e-19, (2, 3): 4.33680868994e-19, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 8.881784197e-16, (1, 8): 2.46519032882e-32, (8, 8): 2.01948391737e-28, (2, 4): 0.0, }, ]
+72 South [{(4, 7): 0.0, (1, 3): 1.42108547152e-14, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.35525271561e-20, (8, 9): 1.35525271561e-20, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.08148791102e-33, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 2.16840434497e-19, (2, 3): 2.16840434497e-19, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.22044604925e-16, (1, 8): 3.08148791102e-33, (8, 8): 1.26217744835e-29, (2, 4): 0.0, }, ]
+73 East [{(4, 7): 0.0, (1, 3): 1.42108547152e-14, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 6.77626357803e-21, (8, 9): 6.77626357803e-21, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.54074395551e-33, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 1.08420217249e-19, (2, 3): 1.08420217249e-19, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.22044604925e-16, (1, 8): 1.54074395551e-33, (8, 8): 3.15544362088e-30, (2, 4): 0.0, }, ]
+74 West [{(4, 7): 0.0, (1, 3): 3.5527136788e-15, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.35525271561e-20, (8, 9): 1.35525271561e-20, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.70371977755e-34, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 5.42101086243e-20, (2, 3): 5.42101086243e-20, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.22044604925e-16, (1, 8): 7.70371977755e-34, (8, 8): 3.15544362088e-30, (2, 4): 0.0, }, ]
+75 Stop [{(4, 7): 0.0, (1, 3): 3.5527136788e-15, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 6.77626357803e-21, (8, 9): 6.77626357803e-21, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.85185988877e-34, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 2.71050543121e-20, (2, 3): 2.71050543121e-20, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.22044604925e-16, (1, 8): 3.85185988877e-34, (8, 8): 7.88860905221e-31, (2, 4): 0.0, }, ]
+76 South [{(4, 7): 0.0, (1, 3): 3.5527136788e-15, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.38813178902e-21, (8, 9): 3.38813178902e-21, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.92592994439e-34, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 1.35525271561e-20, (2, 3): 1.35525271561e-20, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.22044604925e-16, (1, 8): 1.92592994439e-34, (8, 8): 1.97215226305e-31, (2, 4): 0.0, }, ]
+77 East [{(4, 7): 0.0, (1, 3): 3.5527136788e-15, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.69406589451e-21, (8, 9): 1.69406589451e-21, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 9.62964972194e-35, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 6.77626357803e-21, (2, 3): 6.77626357803e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.55111512313e-17, (1, 8): 9.62964972194e-35, (8, 8): 1.97215226305e-31, (2, 4): 0.0, }, ]
+78 Stop [{(4, 7): 0.0, (1, 3): 8.881784197e-16, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 8.47032947254e-22, (8, 9): 8.47032947254e-22, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.20370621524e-35, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 3.38813178902e-21, (2, 3): 3.38813178902e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.38777878078e-17, (1, 8): 1.20370621524e-35, (8, 8): 1.97215226305e-31, (2, 4): 0.0, }, ]
+79 Stop [{(4, 7): 0.0, (1, 3): 8.881784197e-16, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.69406589451e-21, (8, 9): 1.69406589451e-21, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 6.01853107621e-36, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 6.77626357803e-21, (2, 3): 6.77626357803e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.38777878078e-17, (1, 8): 6.01853107621e-36, (8, 8): 1.97215226305e-31, (2, 4): 0.0, }, ]
+80 West [{(4, 7): 0.0, (1, 3): 2.22044604925e-16, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 8.47032947254e-22, (8, 9): 8.47032947254e-22, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.52316384526e-37, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 3.38813178902e-21, (2, 3): 3.38813178902e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.46944695195e-18, (1, 8): 7.52316384526e-37, (8, 8): 1.97215226305e-31, (2, 4): 0.0, }, ]
+81 North [{(4, 7): 0.0, (1, 3): 2.22044604925e-16, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 4.23516473627e-22, (8, 9): 4.23516473627e-22, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.76158192263e-37, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 6.77626357803e-21, (2, 3): 6.77626357803e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 8.67361737988e-19, (1, 8): 3.76158192263e-37, (8, 8): 1.97215226305e-31, (2, 4): 0.0, }, ]
+82 North [{(4, 7): 0.0, (1, 3): 2.22044604925e-16, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.11758236814e-22, (8, 9): 2.11758236814e-22, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.88079096132e-37, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 3.38813178902e-21, (2, 3): 3.38813178902e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 8.67361737988e-19, (1, 8): 1.88079096132e-37, (8, 8): 4.93038065763e-32, (2, 4): 0.0, }, ]
+83 East [{(4, 7): 0.0, (1, 3): 2.22044604925e-16, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.69406589451e-21, (8, 9): 1.69406589451e-21, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.50463276905e-36, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 6.77626357803e-21, (2, 3): 6.77626357803e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.46944695195e-18, (1, 8): 1.50463276905e-36, (8, 8): 1.97215226305e-31, (2, 4): 0.0, }, ]
+84 Stop [{(4, 7): 0.0, (1, 3): 2.22044604925e-16, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.11758236814e-22, (8, 9): 2.11758236814e-22, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.52316384526e-37, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 1.35525271561e-20, (2, 3): 1.35525271561e-20, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 8.67361737988e-19, (1, 8): 7.52316384526e-37, (8, 8): 1.23259516441e-32, (2, 4): 0.0, }, ]
+85 Stop [{(4, 7): 0.0, (1, 3): 5.55111512313e-17, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.64697796017e-23, (8, 9): 2.64697796017e-23, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.76158192263e-37, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 6.77626357803e-21, (2, 3): 6.77626357803e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.16840434497e-19, (1, 8): 3.76158192263e-37, (8, 8): 7.70371977755e-34, (2, 4): 0.0, }, ]
+86 South [{(4, 7): 0.0, (1, 3): 1.38777878078e-17, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.32348898008e-23, (8, 9): 1.32348898008e-23, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.52316384526e-37, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 3.38813178902e-21, (2, 3): 3.38813178902e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.16840434497e-19, (1, 8): 7.52316384526e-37, (8, 8): 1.92592994439e-34, (2, 4): 0.0, }, ]
+87 West [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.64697796017e-23, (8, 9): 2.64697796017e-23, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.76158192263e-37, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 1.69406589451e-21, (2, 3): 1.69406589451e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.16840434497e-19, (1, 8): 3.76158192263e-37, (8, 8): 7.70371977755e-34, (2, 4): 0.0, }, ]
+88 Stop [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.32348898008e-23, (8, 9): 1.32348898008e-23, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.88079096132e-37, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 8.47032947254e-22, (2, 3): 8.47032947254e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.16840434497e-19, (1, 8): 1.88079096132e-37, (8, 8): 1.92592994439e-34, (2, 4): 0.0, }, ]
+89 South [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 6.61744490042e-24, (8, 9): 6.61744490042e-24, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 9.40395480658e-38, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 4.23516473627e-22, (2, 3): 4.23516473627e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.16840434497e-19, (1, 8): 9.40395480658e-38, (8, 8): 4.81482486097e-35, (2, 4): 0.0, }, ]
+90 West [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 3.30872245021e-24, (8, 9): 3.30872245021e-24, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 4.70197740329e-38, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 8.47032947254e-22, (2, 3): 8.47032947254e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.42101086243e-20, (1, 8): 4.70197740329e-38, (8, 8): 4.81482486097e-35, (2, 4): 0.0, }, ]
+91 East [{(4, 7): 0.0, (1, 3): 1.38777878078e-17, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.65436122511e-24, (8, 9): 1.65436122511e-24, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 9.40395480658e-38, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 6.77626357803e-21, (2, 3): 6.77626357803e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.35525271561e-20, (1, 8): 9.40395480658e-38, (8, 8): 4.81482486097e-35, (2, 4): 0.0, }, ]
+92 West [{(4, 7): 0.0, (1, 3): 1.38777878078e-17, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 8.27180612553e-25, (8, 9): 8.27180612553e-25, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 4.70197740329e-38, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 1.35525271561e-20, (2, 3): 1.35525271561e-20, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.38813178902e-21, (1, 8): 4.70197740329e-38, (8, 8): 4.81482486097e-35, (2, 4): 0.0, }, ]
+93 East [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.65436122511e-24, (8, 9): 1.65436122511e-24, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.35098870164e-38, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 1.69406589451e-21, (2, 3): 1.69406589451e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.35525271561e-20, (1, 8): 2.35098870164e-38, (8, 8): 4.81482486097e-35, (2, 4): 0.0, }, ]
+94 North [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 8.27180612553e-25, (8, 9): 8.27180612553e-25, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.17549435082e-38, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 8.47032947254e-22, (2, 3): 8.47032947254e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.38813178902e-21, (1, 8): 1.17549435082e-38, (8, 8): 4.81482486097e-35, (2, 4): 0.0, }, ]
+95 South [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 4.13590306277e-25, (8, 9): 4.13590306277e-25, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 5.87747175411e-39, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 4.23516473627e-22, (2, 3): 4.23516473627e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 3.38813178902e-21, (1, 8): 5.87747175411e-39, (8, 8): 1.20370621524e-35, (2, 4): 0.0, }, ]
+96 Stop [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.06795153138e-25, (8, 9): 2.06795153138e-25, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.93873587706e-39, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 2.11758236814e-22, (2, 3): 2.11758236814e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 8.47032947254e-22, (1, 8): 2.93873587706e-39, (8, 8): 1.20370621524e-35, (2, 4): 0.0, }, ]
+97 Stop [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 1.03397576569e-25, (8, 9): 1.03397576569e-25, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.46936793853e-39, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 4.23516473627e-22, (2, 3): 4.23516473627e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 2.11758236814e-22, (1, 8): 1.46936793853e-39, (8, 8): 1.20370621524e-35, (2, 4): 0.0, }, ]
+98 West [{(4, 7): 0.0, (1, 3): 3.46944695195e-18, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 5.16987882846e-26, (8, 9): 5.16987882846e-26, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 7.34683969264e-40, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 2.11758236814e-22, (2, 3): 2.11758236814e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 5.29395592034e-23, (1, 8): 7.34683969264e-40, (8, 8): 1.20370621524e-35, (2, 4): 0.0, }, ]
+99 North [{(4, 7): 0.0, (1, 3): 1.38777878078e-17, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.58493941423e-26, (8, 9): 2.58493941423e-26, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 1.46936793853e-39, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 1.69406589451e-21, (2, 3): 1.69406589451e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.32348898008e-23, (1, 8): 1.46936793853e-39, (8, 8): 1.20370621524e-35, (2, 4): 0.0, }, ]
+100 Stop [{(4, 7): 0.0, (1, 3): 5.55111512313e-17, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 5.16987882846e-26, (8, 9): 5.16987882846e-26, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 2.93873587706e-39, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 3.38813178902e-21, (2, 3): 3.38813178902e-21, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.32348898008e-23, (1, 8): 2.93873587706e-39, (8, 8): 4.81482486097e-35, (2, 4): 0.0, }, ]
+101 None [{(4, 7): 0.0, (1, 3): 1.38777878078e-17, (6, 6): 0.0, (5, 6): 0.0, (2, 8): 0.0, (9, 8): 2.58493941423e-26, (8, 9): 2.58493941423e-26, (6, 7): 0.0, (5, 5): 0.0, (2, 9): 3.67341984632e-40, (1, 1): 0.0, (4, 5): 0.0, (9, 3): 1.0, (1, 4): 4.23516473627e-22, (2, 3): 4.23516473627e-22, (1, 9): 0.0, (6, 5): 0.0, (4, 6): 0.0, (5, 7): 0.0, (9, 9): 1.32348898008e-23, (1, 8): 3.67341984632e-40, (8, 8): 1.20370621524e-35, (2, 4): 0.0, }, ]
+"""

tracking/test_cases/q10/3-ParticleUpdate.test

@@ -0,0 +1,31 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%%%%%%
+%  %%%%%  %
+%  %%%%%  %
+%%%%   %%%%
+%%%% P %%%%
+%%%%   %%%%
+%  %%%%%%%%
+%  %%%%%%G%
+%%%%%%%%%%%
+% %%%%%%%%%
+%%%%%%%%%%%
+"""
+
+observe: "True"
+
+elapse: "False"
+
+checkUniform: "False"
+maxMoves: "100"
+numParticles: "5000"
+numGhosts: "1"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+L2Tolerance: "0.1"
+errorMsg: "Particle filter observe test: %d inference errors."
+

tracking/test_cases/q10/4-ParticleUpdate.solution

@@ -0,0 +1,10 @@
+# move_number action likelihood_dictionary
+correctActions: """
+0 None [{(1, 3): 0.25, (2, 3): 0.25, (3, 3): 0.25, (4, 3): 0.25, }, ]
+1 East [{(1, 3): 0.0, (2, 3): 0.244094488189, (3, 3): 0.251968503937, (4, 3): 0.503937007874, (1, 1): 0.0, }, ]
+2 Stop [{(1, 3): 0.0, (2, 3): 0.0, (3, 3): 0.832898172324, (4, 3): 0.167101827676, (1, 1): 0.0, }, ]
+3 West [{(1, 3): 0.0, (2, 3): 0.0, (3, 3): 0.828432604507, (4, 3): 0.171567395493, (1, 1): 0.0, }, ]
+4 East [{(1, 3): 0.0, (2, 3): 0.0, (3, 3): 0.707114765821, (4, 3): 0.292885234179, (1, 1): 0.0, }, ]
+5 East [{(1, 3): 0.0, (2, 3): 0.0, (3, 3): 0.905833634776, (4, 3): 0.0941663652244, (1, 1): 0.0, }, ]
+6 None [{(1, 3): 0.0, (2, 3): 0.0, (3, 3): 0.0, (4, 3): 0.0, (1, 1): 0.0, }, ]
+"""

tracking/test_cases/q10/4-ParticleUpdate.test

@@ -0,0 +1,25 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%
+%P  G%
+%%%%%%
+% %%%%
+%%%%%%
+"""
+
+observe: "True"
+
+elapse: "False"
+
+checkUniform: "False"
+maxMoves: "100"
+numParticles: "5000"
+numGhosts: "1"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+L2Tolerance: "0.05"
+errorMsg: "Particle filter observe test: %d inference errors."
+

tracking/test_cases/q10/5-ParticleUpdate.solution

@@ -0,0 +1 @@
+# This test checks that you successfully handle the case when all particle weights are set to 0

tracking/test_cases/q10/5-ParticleUpdate.test

@@ -0,0 +1,43 @@
+class: "ZeroWeightTest"
+
+seed: "188"
+
+layout: """
+%%%%%
+%P%G%
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%% %
+%%%%%
+% %%%
+%%%%%
+"""
+
+observe: "True"
+
+elapse: "False"
+
+maxMoves: "100"
+numParticles: "5000"
+numGhosts: "1"
+
+errorMsg: "Particle filter observe test: %d inference errors."
+ghost: "GoSouthAgent"
+inference: "ParticleFilter"
+
+

tracking/test_cases/q10/6-ParticleUpdate.solution

@@ -0,0 +1 @@
+# You must win at least 8/10 games with at least 100 points

tracking/test_cases/q10/6-ParticleUpdate.test

@@ -0,0 +1,30 @@
+class: "GameScoreTest"
+
+numRuns: "10"
+numWinsForCredit: "8"
+layout_name: "oneHunt"
+min_score: "100"
+
+layout_str: """
+%%%%%%%%%%
+%    G   %
+%  P     %
+%        %
+%        %
+%        %
+%%%%%%%%%%
+% %%%%%%%%
+%%%%%%%%%%
+"""
+
+observe: "True"
+
+elapse: "False"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+maxMoves: "100"
+numParticles: "5000"
+numGhosts: "1"
+
+
+

tracking/test_cases/q10/CONFIG

@@ -0,0 +1,3 @@
+max_points: "2"
+class: "PassAllTestsQuestion"
+

tracking/test_cases/q11/1-ParticlePredict.solution

@@ -0,0 +1,45 @@
+# move_number action likelihood_dictionary
+correctActions: """
+0 None [{(2, 7): 0.0588235294118, (4, 7): 0.0588235294118, (1, 3): 0.0588235294118, (3, 3): 0.0588235294118, (1, 6): 0.0588235294118, (5, 6): 0.0588235294118, (5, 7): 0.0588235294118, (5, 4): 0.0588235294118, (1, 4): 0.0588235294118, (1, 5): 0.0588235294118, (3, 5): 0.0588235294118, (2, 3): 0.0588235294118, (4, 3): 0.0588235294118, (1, 7): 0.0588235294118, (3, 7): 0.0588235294118, (5, 3): 0.0588235294118, (5, 5): 0.0588235294118, }, ]
+1 Stop [{(2, 7): 0.0588235294118, (4, 7): 0.0588235294118, (1, 3): 0.0588235294118, (3, 3): 0.0588235294118, (1, 6): 0.0588235294118, (5, 6): 0.0588235294118, (5, 7): 0.0588235294118, (5, 4): 0.0588235294118, (1, 4): 0.0588235294118, (1, 5): 0.0588235294118, (3, 5): 0.0588235294118, (2, 3): 0.0588235294118, (4, 3): 0.0588235294118, (1, 7): 0.0588235294118, (3, 7): 0.0588235294118, (5, 3): 0.0588235294118, (5, 5): 0.0588235294118, }, ]
+2 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+3 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+4 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+5 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+6 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+7 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+8 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+9 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+10 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+11 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+12 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+13 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+14 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+15 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+16 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+17 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+18 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+19 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+20 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+21 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+22 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+23 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+24 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+25 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+26 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+27 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+28 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+29 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+30 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+31 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+32 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+33 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+34 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+35 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+36 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+37 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+38 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+39 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+40 Stop [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+41 None [{(2.0, 7.0): 0.0588235294118, (4.0, 7.0): 0.0588235294118, (1.0, 3.0): 0.0588235294118, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0588235294118, (5.0, 4.0): 0.0588235294118, (1.0, 4.0): 0.0588235294118, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0588235294118, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0588235294118, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+"""

tracking/test_cases/q11/1-ParticlePredict.test

@@ -0,0 +1,29 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%%
+%   G %
+% %%% %
+% %P% %
+% %%% %
+%     %
+%%%%%%%
+% %%%%%
+%%%%%%%
+"""
+
+observe: "False"
+
+elapse: "True"
+
+checkUniform: "False"
+maxMoves: "40"
+numParticles: "5000"
+numGhosts: "1"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+L2Tolerance: "0.05"
+errorMsg: "Particle filter full test: %d inference errors."
+

tracking/test_cases/q11/2-ParticlePredict.solution

@@ -0,0 +1,45 @@
+# move_number action likelihood_dictionary
+correctActions: """
+0 None [{(2, 7): 0.0588235294118, (4, 7): 0.0588235294118, (1, 3): 0.0588235294118, (3, 3): 0.0588235294118, (1, 6): 0.0588235294118, (5, 6): 0.0588235294118, (5, 7): 0.0588235294118, (5, 4): 0.0588235294118, (1, 4): 0.0588235294118, (1, 5): 0.0588235294118, (3, 5): 0.0588235294118, (2, 3): 0.0588235294118, (4, 3): 0.0588235294118, (1, 7): 0.0588235294118, (3, 7): 0.0588235294118, (5, 3): 0.0588235294118, (5, 5): 0.0588235294118, }, ]
+1 Stop [{(2, 7): 0.0588235294118, (4, 7): 0.0588235294118, (1, 3): 0.0588235294118, (3, 3): 0.0588235294118, (1, 6): 0.0588235294118, (5, 6): 0.0588235294118, (5, 7): 0.0588235294118, (5, 4): 0.0588235294118, (1, 4): 0.0588235294118, (1, 5): 0.0588235294118, (3, 5): 0.0588235294118, (2, 3): 0.0588235294118, (4, 3): 0.0588235294118, (1, 7): 0.0588235294118, (3, 7): 0.0588235294118, (5, 3): 0.0588235294118, (5, 5): 0.0588235294118, }, ]
+2 Stop [{(2.0, 7.0): 0.0539215686275, (4.0, 7.0): 0.0539215686275, (1.0, 3.0): 0.0686274509804, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0588235294118, (5.0, 6.0): 0.0588235294118, (5.0, 7.0): 0.0490196078431, (5.0, 4.0): 0.0637254901961, (1.0, 4.0): 0.0637254901961, (1.0, 5.0): 0.0588235294118, (2.0, 3.0): 0.0588235294118, (4.0, 3.0): 0.0588235294118, (1.0, 7.0): 0.0490196078431, (3.0, 7.0): 0.0588235294118, (5.0, 3.0): 0.0686274509804, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0588235294118, }, ]
+3 Stop [{(2.0, 7.0): 0.0498366013072, (4.0, 7.0): 0.0498366013072, (1.0, 3.0): 0.0743464052288, (3.0, 3.0): 0.0588235294118, (1.0, 6.0): 0.0539215686275, (5.0, 6.0): 0.0539215686275, (5.0, 7.0): 0.0449346405229, (5.0, 4.0): 0.0682189542484, (1.0, 4.0): 0.0682189542484, (1.0, 5.0): 0.0600490196078, (2.0, 3.0): 0.062091503268, (4.0, 3.0): 0.062091503268, (1.0, 7.0): 0.0449346405229, (3.0, 7.0): 0.0555555555556, (5.0, 3.0): 0.0743464052288, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0600490196078, }, ]
+4 Stop [{(2.0, 7.0): 0.046364379085, (4.0, 7.0): 0.046364379085, (1.0, 3.0): 0.0795887799564, (3.0, 3.0): 0.0610021786492, (1.0, 6.0): 0.0509599673203, (5.0, 6.0): 0.0509599673203, (5.0, 7.0): 0.0413262527233, (5.0, 4.0): 0.0718613834423, (1.0, 4.0): 0.0718613834423, (1.0, 5.0): 0.0590277777778, (2.0, 3.0): 0.0650871459695, (4.0, 3.0): 0.0650871459695, (1.0, 7.0): 0.0413262527233, (3.0, 7.0): 0.05174291939, (5.0, 3.0): 0.0795887799564, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0590277777778, }, ]
+5 Stop [{(2.0, 7.0): 0.0430339960058, (4.0, 7.0): 0.0430339960058, (1.0, 3.0): 0.0841560003631, (3.0, 3.0): 0.0637254901961, (1.0, 6.0): 0.0481600626362, (5.0, 6.0): 0.0481600626362, (5.0, 7.0): 0.0385263480392, (5.0, 4.0): 0.0740088280683, (1.0, 4.0): 0.0740088280683, (1.0, 5.0): 0.0582022739651, (2.0, 3.0): 0.0685593681917, (4.0, 3.0): 0.0685593681917, (1.0, 7.0): 0.0385263480392, (3.0, 7.0): 0.0481572258533, (5.0, 3.0): 0.0841560003631, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0582022739651, }, ]
+6 Stop [{(2.0, 7.0): 0.0400286609628, (4.0, 7.0): 0.0400286609628, (1.0, 3.0): 0.0879095368857, (3.0, 3.0): 0.0669480755265, (1.0, 6.0): 0.0458537581699, (5.0, 6.0): 0.0458537581699, (5.0, 7.0): 0.0360162680041, (5.0, 4.0): 0.0756553441207, (1.0, 4.0): 0.0756553441207, (1.0, 5.0): 0.0571328068264, (2.0, 3.0): 0.072146952917, (4.0, 3.0): 0.072146952917, (1.0, 7.0): 0.0360162680041, (3.0, 7.0): 0.0447417392883, (5.0, 3.0): 0.0879095368857, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0571328068264, }, ]
+7 Stop [{(2.0, 7.0): 0.0372608670847, (4.0, 7.0): 0.0372608670847, (1.0, 3.0): 0.0911798353279, (3.0, 3.0): 0.0704139937868, (1.0, 6.0): 0.0437547752511, (5.0, 6.0): 0.0437547752511, (5.0, 7.0): 0.0338103935311, (5.0, 4.0): 0.0767834184053, (1.0, 4.0): 0.0767834184053, (1.0, 5.0): 0.0561239168217, (2.0, 3.0): 0.0756681884431, (4.0, 3.0): 0.0756681884431, (1.0, 7.0): 0.0338103935311, (3.0, 7.0): 0.0415996870713, (5.0, 3.0): 0.0911798353279, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0561239168217, }, ]
+8 Stop [{(2.0, 7.0): 0.0347394497681, (4.0, 7.0): 0.0347394497681, (1.0, 3.0): 0.0940077171263, (3.0, 3.0): 0.0739167902243, (1.0, 6.0): 0.0418748697838, (5.0, 6.0): 0.0418748697838, (5.0, 7.0): 0.0318115812238, (5.0, 4.0): 0.0776510914548, (1.0, 4.0): 0.0776510914548, (1.0, 5.0): 0.0551042214323, (2.0, 3.0): 0.0790873391859, (4.0, 3.0): 0.0790873391859, (1.0, 7.0): 0.0318115812238, (3.0, 7.0): 0.0387071404136, (5.0, 3.0): 0.0940077171263, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0551042214323, }, ]
+9 Stop [{(2.0, 7.0): 0.0324350920332, (4.0, 7.0): 0.0324350920332, (1.0, 3.0): 0.0965238978315, (3.0, 3.0): 0.0773638228654, (1.0, 6.0): 0.0401505634159, (5.0, 6.0): 0.0401505634159, (5.0, 7.0): 0.0300014293413, (5.0, 4.0): 0.0783007892886, (1.0, 4.0): 0.0783007892886, (1.0, 5.0): 0.0541262631137, (2.0, 3.0): 0.0823372821789, (4.0, 3.0): 0.0823372821789, (1.0, 7.0): 0.0300014293413, (3.0, 7.0): 0.0360620133166, (5.0, 3.0): 0.0965238978315, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0541262631137, }, ]
+10 Stop [{(2.0, 7.0): 0.0303327257853, (4.0, 7.0): 0.0303327257853, (1.0, 3.0): 0.0987707879811, (3.0, 3.0): 0.0806794624077, (1.0, 6.0): 0.038569921303, (5.0, 6.0): 0.038569921303, (5.0, 7.0): 0.0283496955337, (5.0, 4.0): 0.0788129614895, (1.0, 4.0): 0.0788129614895, (1.0, 5.0): 0.0531820448086, (2.0, 3.0): 0.0854083342919, (4.0, 3.0): 0.0854083342919, (1.0, 7.0): 0.0283496955337, (3.0, 7.0): 0.0336440657943, (5.0, 3.0): 0.0987707879811, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0531820448086, }, ]
+11 Stop [{(2.0, 7.0): 0.0284130210766, (4.0, 7.0): 0.0284130210766, (1.0, 3.0): 0.100799521502, (3.0, 3.0): 0.0838320436638, (1.0, 6.0): 0.0371128392948, (5.0, 6.0): 0.0371128392948, (5.0, 7.0): 0.0268408128043, (5.0, 4.0): 0.0792178587704, (1.0, 4.0): 0.0792178587704, (1.0, 5.0): 0.052283712226, (2.0, 3.0): 0.0882861948936, (4.0, 3.0): 0.0882861948936, (1.0, 7.0): 0.0268408128043, (3.0, 7.0): 0.0314365057883, (5.0, 3.0): 0.100799521502, (1, 1): 0.0588235294118, (5.0, 5.0): 0.052283712226, }, ]
+12 Stop [{(2.0, 7.0): 0.0266600454894, (4.0, 7.0): 0.0266600454894, (1.0, 3.0): 0.102637501517, (3.0, 3.0): 0.086801477817, (1.0, 6.0): 0.0357695442823, (5.0, 6.0): 0.0357695442823, (5.0, 7.0): 0.0254594200503, (5.0, 4.0): 0.0795461613064, (1.0, 4.0): 0.0795461613064, (1.0, 5.0): 0.0514318123965, (2.0, 3.0): 0.0909725866866, (4.0, 3.0): 0.0909725866866, (1.0, 7.0): 0.0254594200503, (3.0, 7.0): 0.0294208493139, (5.0, 3.0): 0.102637501517, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0514318123965, }, ]
+13 Stop [{(2.0, 7.0): 0.0250584866137, (4.0, 7.0): 0.0250584866137, (1.0, 3.0): 0.104309776721, (3.0, 3.0): 0.0895822170634, (1.0, 6.0): 0.0345300491949, (5.0, 6.0): 0.0345300491949, (5.0, 7.0): 0.024193922913, (5.0, 4.0): 0.0798149470306, (1.0, 4.0): 0.0798149470306, (1.0, 5.0): 0.0506292655669, (2.0, 3.0): 0.0934705220069, (4.0, 3.0): 0.0934705220069, (1.0, 7.0): 0.024193922913, (3.0, 7.0): 0.0275803134309, (5.0, 3.0): 0.104309776721, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0506292655669, }, ]
+14 Stop [{(2.0, 7.0): 0.0235947474097, (4.0, 7.0): 0.0235947474097, (1.0, 3.0): 0.105834239758, (3.0, 3.0): 0.0921744203591, (1.0, 6.0): 0.0333867901469, (5.0, 6.0): 0.0333867901469, (5.0, 7.0): 0.0230338218982, (5.0, 4.0): 0.0800382951148, (1.0, 4.0): 0.0800382951148, (1.0, 5.0): 0.0498760777468, (2.0, 3.0): 0.0957875052638, (4.0, 3.0): 0.0957875052638, (1.0, 7.0): 0.0230338218982, (3.0, 7.0): 0.0258990955527, (5.0, 3.0): 0.105834239758, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0498760777468, }, ]
+15 Stop [{(2.0, 7.0): 0.0222564031287, (4.0, 7.0): 0.0222564031287, (1.0, 3.0): 0.107226395898, (3.0, 3.0): 0.0945831436289, (1.0, 6.0): 0.0323326279225, (5.0, 6.0): 0.0323326279225, (5.0, 7.0): 0.0219700688145, (5.0, 4.0): 0.0802256925714, (1.0, 4.0): 0.0802256925714, (1.0, 5.0): 0.0491719882889, (2.0, 3.0): 0.097932055127, (4.0, 3.0): 0.097932055127, (1.0, 7.0): 0.0219700688145, (3.0, 7.0): 0.0243628634574, (5.0, 3.0): 0.107226395898, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0491719882889, }, ]
+16 Stop [{(2.0, 7.0): 0.0210322727323, (4.0, 7.0): 0.0210322727323, (1.0, 3.0): 0.108498996627, (3.0, 3.0): 0.0968157512943, (1.0, 6.0): 0.0313611884601, (5.0, 6.0): 0.0313611884601, (5.0, 7.0): 0.0209944752272, (5.0, 4.0): 0.0803845492533, (1.0, 4.0): 0.0803845492533, (1.0, 5.0): 0.0485157341763, (2.0, 3.0): 0.0999138648846, (4.0, 3.0): 0.0999138648846, (1.0, 7.0): 0.0209944752272, (3.0, 7.0): 0.0229585565716, (5.0, 3.0): 0.108498996627, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0485157341763, }, ]
+17 Stop [{(2.0, 7.0): 0.0199122285748, (4.0, 7.0): 0.0199122285748, (1.0, 3.0): 0.109663228464, (3.0, 3.0): 0.0988811603545, (1.0, 6.0): 0.0304664682727, (5.0, 6.0): 0.0304664682727, (5.0, 7.0): 0.0200996734993, (5.0, 4.0): 0.0805203366106, (1.0, 4.0): 0.0805203366106, (1.0, 5.0): 0.0479056650875, (2.0, 3.0): 0.101742870935, (4.0, 3.0): 0.101742870935, (1.0, 7.0): 0.0200996734993, (3.0, 7.0): 0.0216743673454, (5.0, 3.0): 0.109663228464, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0479056650875, }, ]
+18 Stop [{(2.0, 7.0): 0.0188871170149, (4.0, 7.0): 0.0188871170149, (1.0, 3.0): 0.110728868105, (3.0, 3.0): 0.100788967408, (1.0, 6.0): 0.0296428700897, (5.0, 6.0): 0.0296428700897, (5.0, 7.0): 0.0192789449679, (5.0, 4.0): 0.0806373261844, (1.0, 4.0): 0.0806373261844, (1.0, 5.0): 0.0473397345609, (2.0, 3.0): 0.103429086585, (4.0, 3.0): 0.103429086585, (1.0, 7.0): 0.0192789449679, (3.0, 7.0): 0.020499608165, (5.0, 3.0): 0.110728868105, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0473397345609, }, ]
+19 Stop [{(2.0, 7.0): 0.0179486446353, (4.0, 7.0): 0.0179486446353, (1.0, 3.0): 0.111704647989, (3.0, 3.0): 0.102549046859, (1.0, 6.0): 0.0288851236466, (5.0, 6.0): 0.0288851236466, (5.0, 7.0): 0.018526159436, (5.0, 4.0): 0.0807388215282, (1.0, 4.0): 0.0807388215282, (1.0, 5.0): 0.0468157002311, (2.0, 3.0): 0.104982307366, (4.0, 3.0): 0.104982307366, (1.0, 7.0): 0.018526159436, (3.0, 7.0): 0.0194246140649, (5.0, 3.0): 0.111704647989, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0468157002311, }, ]
+20 Stop [{(2.0, 7.0): 0.0170892927591, (4.0, 7.0): 0.0170892927591, (1.0, 3.0): 0.112598395882, (3.0, 3.0): 0.10417122053, (1.0, 6.0): 0.0281882856874, (5.0, 6.0): 0.0281882856874, (5.0, 7.0): 0.0178357023157, (5.0, 4.0): 0.0808274381605, (1.0, 4.0): 0.0808274381605, (1.0, 5.0): 0.0463311922631, (2.0, 3.0): 0.106412000738, (4.0, 3.0): 0.106412000738, (1.0, 7.0): 0.0178357023157, (3.0, 7.0): 0.0184406344451, (5.0, 3.0): 0.112598395882, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0463311922631, }, ]
+21 Stop [{(2.0, 7.0): 0.016302234647, (4.0, 7.0): 0.016302234647, (1.0, 3.0): 0.11341718462, (3.0, 3.0): 0.105665074002, (1.0, 6.0): 0.0275477206455, (5.0, 6.0): 0.0275477206455, (5.0, 7.0): 0.0172024279205, (5.0, 4.0): 0.0809052542992, (1.0, 4.0): 0.0809052542992, (1.0, 5.0): 0.0458838004496, (2.0, 3.0): 0.107727205717, (4.0, 3.0): 0.107727205717, (1.0, 7.0): 0.0172024279205, (3.0, 7.0): 0.0175397399878, (5.0, 3.0): 0.11341718462, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0458838004496, }, ]
+22 Stop [{(2.0, 7.0): 0.0155812651917, (4.0, 7.0): 0.0155812651917, (1.0, 3.0): 0.114167423929, (3.0, 3.0): 0.107039828479, (1.0, 6.0): 0.026959094234, (5.0, 6.0): 0.026959094234, (5.0, 7.0): 0.0166216153572, (5.0, 4.0): 0.0809739420064, (1.0, 4.0): 0.0809739420064, (1.0, 5.0): 0.04547112401, (2.0, 3.0): 0.108936488113, (4.0, 3.0): 0.108936488113, (1.0, 7.0): 0.0166216153572, (3.0, 7.0): 0.0167147364273, (5.0, 3.0): 0.114167423929, (1, 1): 0.0588235294118, (5.0, 5.0): 0.04547112401, }, ]
+23 Stop [{(2.0, 7.0): 0.0149207377123, (4.0, 7.0): 0.0149207377123, (1.0, 3.0): 0.114854941684, (3.0, 3.0): 0.108304268235, (1.0, 6.0): 0.0264183622396, (5.0, 6.0): 0.0264183622396, (5.0, 7.0): 0.0160889324617, (5.0, 4.0): 0.0810348554828, (1.0, 4.0): 0.0810348554828, (1.0, 5.0): 0.0450908136211, (2.0, 3.0): 0.110047913507, (4.0, 3.0): 0.110047913507, (1.0, 7.0): 0.0160889324617, (3.0, 7.0): 0.0159590889369, (5.0, 3.0): 0.114854941684, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0450908136211, }, ]
+24 Stop [{(2.0, 7.0): 0.0143155086651, (4.0, 7.0): 0.0143155086651, (1.0, 3.0): 0.115485046138, (3.0, 3.0): 0.109466698416, (1.0, 6.0): 0.025921760196, (5.0, 6.0): 0.025921760196, (5.0, 7.0): 0.0156004029127, (5.0, 4.0): 0.0810891012425, (1.0, 4.0): 0.0810891012425, (1.0, 5.0): 0.0447405983958, (2.0, 3.0): 0.111069041142, (4.0, 3.0): 0.111069041142, (1.0, 7.0): 0.0156004029127, (3.0, 7.0): 0.0152668547871, (5.0, 3.0): 0.115485046138, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0447405983958, }, ]
+25 Stop [{(2.0, 7.0): 0.0137608885456, (4.0, 7.0): 0.0137608885456, (1.0, 3.0): 0.116062579715, (3.0, 3.0): 0.1105349269, (1.0, 6.0): 0.0254657911043, (5.0, 6.0): 0.0254657911043, (5.0, 7.0): 0.0151523769989, (5.0, 4.0): 0.081137589888, (1.0, 4.0): 0.081137589888, (1.0, 5.0): 0.0444183050076, (2.0, 3.0): 0.112006928565, (4.0, 3.0): 0.112006928565, (1.0, 7.0): 0.0151523769989, (3.0, 7.0): 0.0146326240391, (5.0, 3.0): 0.116062579715, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0444183050076, }, ]
+26 Stop [{(2.0, 7.0): 0.0132525984446, (4.0, 7.0): 0.0132525984446, (1.0, 3.0): 0.116591964371, (3.0, 3.0): 0.111516261344, (1.0, 6.0): 0.0250472125274, (5.0, 6.0): 0.0250472125274, (5.0, 7.0): 0.0147415048743, (5.0, 4.0): 0.0811810765473, (1.0, 4.0): 0.0811810765473, (1.0, 5.0): 0.044121869276, (2.0, 3.0): 0.11286814506, (4.0, 3.0): 0.11286814506, (1.0, 7.0): 0.0147415048743, (3.0, 7.0): 0.0140514670435, (5.0, 3.0): 0.116591964371, (1, 1): 0.0588235294118, (5.0, 5.0): 0.044121869276, }, ]
+27 Stop [{(2.0, 7.0): 0.0127867313813, (4.0, 7.0): 0.0127867313813, (1.0, 3.0): 0.117077241417, (3.0, 3.0): 0.112417517155, (1.0, 6.0): 0.024663022888, (5.0, 6.0): 0.024663022888, (5.0, 7.0): 0.0143647121653, (5.0, 4.0): 0.0812201918984, (1.0, 4.0): 0.0812201918984, (1.0, 5.0): 0.0438493427196, (2.0, 3.0): 0.113658790258, (4.0, 3.0): 0.113658790258, (1.0, 7.0): 0.0143647121653, (3.0, 7.0): 0.0135188879776, (5.0, 3.0): 0.117077241417, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0438493427196, }, ]
+28 Stop [{(2.0, 7.0): 0.0123597178276, (4.0, 7.0): 0.0123597178276, (1.0, 3.0): 0.117522106508, (3.0, 3.0): 0.113245032557, (1.0, 6.0): 0.0243104474846, (5.0, 6.0): 0.0243104474846, (5.0, 7.0): 0.0140191775571, (5.0, 4.0): 0.0812554664735, (1.0, 4.0): 0.0812554664735, (1.0, 5.0): 0.0435988950985, (2.0, 3.0): 0.114384516277, (4.0, 3.0): 0.114384516277, (1.0, 7.0): 0.0140191775571, (3.0, 7.0): 0.0130307835801, (5.0, 3.0): 0.117522106508, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0435988950985, }, ]
+29 Stop [{(2.0, 7.0): 0.0119682948585, (4.0, 7.0): 0.0119682948585, (1.0, 3.0): 0.117929940832, (3.0, 3.0): 0.11400468837, (1.0, 6.0): 0.0239869244243, (5.0, 6.0): 0.0239869244243, (5.0, 7.0): 0.013702312203, (5.0, 4.0): 0.0812873496702, (1.0, 4.0): 0.0812873496702, (1.0, 5.0): 0.0433688141353, (2.0, 3.0): 0.11505055178, (4.0, 3.0): 0.11505055178, (1.0, 7.0): 0.013702312203, (3.0, 7.0): 0.0125834064118, (5.0, 3.0): 0.117929940832, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0433688141353, }, ]
+30 Stop [{(2.0, 7.0): 0.0116094784742, (4.0, 7.0): 0.0116094784742, (1.0, 3.0): 0.118303839039, (3.0, 3.0): 0.114701930644, (1.0, 6.0): 0.0236900907414, (5.0, 6.0): 0.0236900907414, (5.0, 7.0): 0.0134117407763, (5.0, 4.0): 0.0813162247624, (1.0, 4.0): 0.0813162247624, (1.0, 5.0): 0.0431575031635, (2.0, 3.0): 0.115661726994, (4.0, 3.0): 0.115661726994, (1.0, 7.0): 0.0134117407763, (3.0, 7.0): 0.0121733320429, (5.0, 3.0): 0.118303839039, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0431575031635, }, ]
+31 Stop [{(2.0, 7.0): 0.0112805386998, (4.0, 7.0): 0.0112805386998, (1.0, 3.0): 0.118646634392, (3.0, 3.0): 0.115341794877, (1.0, 6.0): 0.0234177688644, (5.0, 6.0): 0.0234177688644, (5.0, 7.0): 0.0131452840375, (5.0, 4.0): 0.0813424207854, (1.0, 4.0): 0.0813424207854, (1.0, 5.0): 0.0429634773522, (2.0, 3.0): 0.116222498892, (4.0, 3.0): 0.116222498892, (1.0, 7.0): 0.0131452840375, (3.0, 7.0): 0.0117974296638, (5.0, 3.0): 0.118646634392, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0429634773522, }, ]
+32 Stop [{(2.0, 7.0): 0.0109789771305, (4.0, 7.0): 0.0109789771305, (1.0, 3.0): 0.118960921488, (3.0, 3.0): 0.115928930887, (1.0, 6.0): 0.0231679535729, (5.0, 6.0): 0.0231679535729, (5.0, 7.0): 0.0129009427921, (5.0, 4.0): 0.0813662220032, (1.0, 4.0): 0.0813662220032, (1.0, 5.0): 0.0427853589666, (2.0, 3.0): 0.116736976054, (4.0, 3.0): 0.116736976054, (1.0, 7.0): 0.0129009427921, (3.0, 7.0): 0.0114528356878, (5.0, 3.0): 0.118960921488, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0427853589666, }, ]
+33 Stop [{(2.0, 7.0): 0.0107025066375, (4.0, 7.0): 0.0107025066375, (1.0, 3.0): 0.119249076849, (3.0, 3.0): 0.116467627665, (1.0, 6.0): 0.0229387995309, (5.0, 6.0): 0.0229387995309, (5.0, 7.0): 0.0126768831347, (5.0, 4.0): 0.0813878754799, (1.0, 4.0): 0.0813878754799, (1.0, 5.0): 0.0426218720289, (2.0, 3.0): 0.11720894281, (4.0, 3.0): 0.11720894281, (1.0, 7.0): 0.0126768831347, (3.0, 7.0): 0.011136929983, (5.0, 3.0): 0.119249076849, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0426218720289, }, ]
+34 Stop [{(2.0, 7.0): 0.0104490329905, (4.0, 7.0): 0.0104490329905, (1.0, 3.0): 0.119513277626, (3.0, 3.0): 0.116961837761, (1.0, 6.0): 0.0227286094573, (5.0, 6.0): 0.0227286094573, (5.0, 7.0): 0.0124714228789, (5.0, 4.0): 0.0814075971673, (1.0, 4.0): 0.0814075971673, (1.0, 5.0): 0.0424718366426, (2.0, 3.0): 0.117641882441, (4.0, 3.0): 0.117641882441, (1.0, 7.0): 0.0124714228789, (3.0, 7.0): 0.0108473144193, (5.0, 3.0): 0.119513277626, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0424718366426, }, ]
+35 Stop [{(2.0, 7.0): 0.0102166381896, (4.0, 7.0): 0.0102166381896, (1.0, 3.0): 0.119755518606, (3.0, 3.0): 0.117415200881, (1.0, 6.0): 0.0225358229644, (5.0, 6.0): 0.0225358229644, (5.0, 7.0): 0.0122830190809, (5.0, 4.0): 0.0814255768218, (1.0, 4.0): 0.0814255768218, (1.0, 5.0): 0.0423341631811, (2.0, 3.0): 0.118038999276, (4.0, 3.0): 0.118038999276, (1.0, 7.0): 0.0122830190809, (3.0, 7.0): 0.0105817934668, (5.0, 3.0): 0.119755518606, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0423341631811, }, ]
+36 Stop [{(2.0, 7.0): 0.0100035653224, (4.0, 7.0): 0.0100035653224, (1.0, 3.0): 0.119977627705, (3.0, 3.0): 0.117831066478, (1.0, 6.0): 0.0223590060768, (5.0, 6.0): 0.0223590060768, (5.0, 7.0): 0.0121102565745, (5.0, 4.0): 0.081441981998, (1.0, 4.0): 0.081441981998, (1.0, 5.0): 0.042207846483, (2.0, 3.0): 0.118403239588, (4.0, 3.0): 0.118403239588, (1.0, 7.0): 0.0121102565745, (3.0, 7.0): 0.0103383566154, (5.0, 3.0): 0.119977627705, (1, 1): 0.0588235294118, (5.0, 5.0): 0.042207846483, }, ]
+37 Stop [{(2.0, 7.0): 0.00980820478954, (4.0, 7.0): 0.00980820478954, (1.0, 3.0): 0.120181280097, (3.0, 3.0): 0.118212515218, (1.0, 6.0): 0.0221968414272, (5.0, 6.0): 0.0221968414272, (5.0, 7.0): 0.011951837437, (5.0, 4.0): 0.0814569613093, (1.0, 4.0): 0.0814569613093, (1.0, 5.0): 0.0420919601587, (2.0, 3.0): 0.118737311257, (4.0, 3.0): 0.118737311257, (1.0, 7.0): 0.011951837437, (3.0, 7.0): 0.01011516242, (5.0, 3.0): 0.120181280097, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0420919601587, }, ]
+38 Stop [{(2.0, 7.0): 0.00962908176243, (4.0, 7.0): 0.00962908176243, (1.0, 3.0): 0.120368011106, (3.0, 3.0): 0.118562379244, (1.0, 6.0): 0.022048119115, (5.0, 6.0): 0.022048119115, (5.0, 7.0): 0.0118065713126, (5.0, 4.0): 0.0814706471055, (1.0, 4.0): 0.0814706471055, (1.0, 5.0): 0.0419856510806, (2.0, 3.0): 0.11904370219, (4.0, 3.0): 0.11904370219, (1.0, 7.0): 0.0118065713126, (3.0, 7.0): 0.0099105239997, (5.0, 3.0): 0.120368011106, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0419856510806, }, ]
+39 Stop [{(2.0, 7.0): 0.00946484474885, (4.0, 7.0): 0.00946484474885, (1.0, 3.0): 0.120539227985, (3.0, 3.0): 0.118883261208, (1.0, 6.0): 0.0219117282052, (5.0, 6.0): 0.0219117282052, (5.0, 7.0): 0.0116733665277, (5.0, 4.0): 0.0814831576853, (1.0, 4.0): 0.0814831576853, (1.0, 5.0): 0.041888134104, (2.0, 3.0): 0.119324697513, (4.0, 3.0): 0.119324697513, (1.0, 7.0): 0.0116733665277, (3.0, 7.0): 0.00972289584152, (5.0, 3.0): 0.120539227985, (1, 1): 0.0588235294118, (5.0, 5.0): 0.041888134104, }, ]
+40 Stop [{(2.0, 7.0): 0.00931425516204, (4.0, 7.0): 0.00931425516204, (1.0, 3.0): 0.120696220675, (3.0, 3.0): 0.119177552078, (1.0, 6.0): 0.0217866488411, (5.0, 6.0): 0.0217866488411, (5.0, 7.0): 0.0115512219328, (5.0, 4.0): 0.0814945991349, (1.0, 4.0): 0.0814945991349, (1.0, 5.0): 0.0417986870499, (2.0, 3.0): 0.119582395569, (4.0, 3.0): 0.119582395569, (1.0, 7.0): 0.0115512219328, (3.0, 7.0): 0.00955086177974, (5.0, 3.0): 0.120696220675, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0417986870499, }, ]
+41 None [{(2.0, 7.0): 0.00931425516204, (4.0, 7.0): 0.00931425516204, (1.0, 3.0): 0.120696220675, (3.0, 3.0): 0.119177552078, (1.0, 6.0): 0.0217866488411, (5.0, 6.0): 0.0217866488411, (5.0, 7.0): 0.0115512219328, (5.0, 4.0): 0.0814945991349, (1.0, 4.0): 0.0814945991349, (1.0, 5.0): 0.0417986870499, (2.0, 3.0): 0.119582395569, (4.0, 3.0): 0.119582395569, (1.0, 7.0): 0.0115512219328, (3.0, 7.0): 0.00955086177974, (5.0, 3.0): 0.120696220675, (1, 1): 0.0588235294118, (5.0, 5.0): 0.0417986870499, }, ]
+"""

tracking/test_cases/q11/2-ParticlePredict.test

@@ -0,0 +1,29 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%%
+%  G  %
+% %%% %
+% %P% %
+% %%% %
+%     %
+%%%%%%%
+% %%%%%
+%%%%%%%
+"""
+
+observe: "False"
+
+elapse: "True"
+
+checkUniform: "False"
+maxMoves: "40"
+numParticles: "5000"
+numGhosts: "1"
+ghost: "GoSouthAgent"
+inference: "ParticleFilter"
+L2Tolerance: "0.05"
+errorMsg: "Particle filter full test: %d inference errors."
+

tracking/test_cases/q11/3-ParticlePredict.solution

@@ -0,0 +1,105 @@
+# move_number action likelihood_dictionary
+correctActions: """
+0 None [{(5, 9): 0.0151515151515, (4, 7): 0.0151515151515, (4, 4): 0.0151515151515, (4, 8): 0.0151515151515, (5, 6): 0.0151515151515, (2, 8): 0.0151515151515, (3, 11): 0.0151515151515, (4, 6): 0.0151515151515, (8, 9): 0.0151515151515, (4, 12): 0.0151515151515, (2, 12): 0.0151515151515, (7, 11): 0.0151515151515, (3, 7): 0.0151515151515, (2, 5): 0.0151515151515, (1, 11): 0.0151515151515, (8, 5): 0.0151515151515, (5, 8): 0.0151515151515, (7, 5): 0.0151515151515, (4, 9): 0.0151515151515, (5, 5): 0.0151515151515, (2, 9): 0.0151515151515, (3, 10): 0.0151515151515, (6, 10): 0.0151515151515, (8, 10): 0.0151515151515, (1, 5): 0.0151515151515, (9, 11): 0.0151515151515, (4, 11): 0.0151515151515, (3, 6): 0.0151515151515, (8, 6): 0.0151515151515, (3, 5): 0.0151515151515, (9, 7): 0.0151515151515, (4, 10): 0.0151515151515, (2, 6): 0.0151515151515, (6, 6): 0.0151515151515, (8, 2): 0.0151515151515, (5, 11): 0.0151515151515, (4, 5): 0.0151515151515, (8, 11): 0.0151515151515, (6, 11): 0.0151515151515, (2, 7): 0.0151515151515, (7, 7): 0.0151515151515, (7, 10): 0.0151515151515, (3, 9): 0.0151515151515, (1, 9): 0.0151515151515, (8, 7): 0.0151515151515, (6, 12): 0.0151515151515, (6, 4): 0.0151515151515, (2, 10): 0.0151515151515, (6, 5): 0.0151515151515, (7, 9): 0.0151515151515, (6, 7): 0.0151515151515, (6, 9): 0.0151515151515, (5, 10): 0.0151515151515, (6, 8): 0.0151515151515, (8, 12): 0.0151515151515, (5, 7): 0.0151515151515, (2, 11): 0.0151515151515, (9, 9): 0.0151515151515, (2, 4): 0.0151515151515, (3, 8): 0.0151515151515, (8, 8): 0.0151515151515, (1, 7): 0.0151515151515, (9, 5): 0.0151515151515, (7, 8): 0.0151515151515, (7, 6): 0.0151515151515, (8, 4): 0.0151515151515, }, ]
+1 Stop [{(5, 9): 0.0151515151515, (4, 7): 0.0151515151515, (4, 4): 0.0151515151515, (4, 8): 0.0151515151515, (5, 6): 0.0151515151515, (2, 8): 0.0151515151515, (3, 11): 0.0151515151515, (4, 6): 0.0151515151515, (8, 9): 0.0151515151515, (4, 12): 0.0151515151515, (2, 12): 0.0151515151515, (7, 11): 0.0151515151515, (3, 7): 0.0151515151515, (2, 5): 0.0151515151515, (1, 11): 0.0151515151515, (8, 5): 0.0151515151515, (5, 8): 0.0151515151515, (7, 5): 0.0151515151515, (4, 9): 0.0151515151515, (5, 5): 0.0151515151515, (2, 9): 0.0151515151515, (3, 10): 0.0151515151515, (6, 10): 0.0151515151515, (8, 10): 0.0151515151515, (1, 5): 0.0151515151515, (9, 11): 0.0151515151515, (4, 11): 0.0151515151515, (3, 6): 0.0151515151515, (8, 6): 0.0151515151515, (3, 5): 0.0151515151515, (9, 7): 0.0151515151515, (4, 10): 0.0151515151515, (2, 6): 0.0151515151515, (6, 6): 0.0151515151515, (8, 2): 0.0151515151515, (5, 11): 0.0151515151515, (4, 5): 0.0151515151515, (8, 11): 0.0151515151515, (6, 11): 0.0151515151515, (2, 7): 0.0151515151515, (7, 7): 0.0151515151515, (7, 10): 0.0151515151515, (3, 9): 0.0151515151515, (1, 9): 0.0151515151515, (8, 7): 0.0151515151515, (6, 12): 0.0151515151515, (6, 4): 0.0151515151515, (2, 10): 0.0151515151515, (6, 5): 0.0151515151515, (7, 9): 0.0151515151515, (6, 7): 0.0151515151515, (6, 9): 0.0151515151515, (5, 10): 0.0151515151515, (6, 8): 0.0151515151515, (8, 12): 0.0151515151515, (5, 7): 0.0151515151515, (2, 11): 0.0151515151515, (9, 9): 0.0151515151515, (2, 4): 0.0151515151515, (3, 8): 0.0151515151515, (8, 8): 0.0151515151515, (1, 7): 0.0151515151515, (9, 5): 0.0151515151515, (7, 8): 0.0151515151515, (7, 6): 0.0151515151515, (8, 4): 0.0151515151515, }, ]
+2 Stop [{(5.0, 9.0): 0.0151515151515, (4.0, 7.0): 0.0151515151515, (6.0, 10.0): 0.0151515151515, (4.0, 8.0): 0.0151515151515, (5.0, 6.0): 0.0164141414141, (2.0, 8.0): 0.0131313131313, (3.0, 11.0): 0.0106060606061, (4.0, 6.0): 0.0151515151515, (8.0, 9.0): 0.0217171717172, (4.0, 12.0): 0.00757575757576, (2.0, 12.0): 0.00757575757576, (7.0, 11.0): 0.0106060606061, (3.0, 7.0): 0.0151515151515, (2.0, 5.0): 0.0275252525253, (1.0, 11.0): 0.010101010101, (8.0, 5.0): 0.0275252525253, (5.0, 8.0): 0.0151515151515, (7.0, 5.0): 0.0138888888889, (4.0, 9.0): 0.0151515151515, (5.0, 5.0): 0.0138888888889, (2.0, 9.0): 0.0217171717172, (3.0, 10.0): 0.0166666666667, (4.0, 4.0): 0.0126262626263, (8.0, 10.0): 0.0131313131313, (1.0, 5.0): 0.010101010101, (9.0, 11.0): 0.010101010101, (4.0, 11.0): 0.0212121212121, (3.0, 6.0): 0.0169191919192, (8.0, 6.0): 0.0131313131313, (3.0, 5.0): 0.0138888888889, (1, 1): 0.0151515151515, (9.0, 7.0): 0.010101010101, (4.0, 10.0): 0.0151515151515, (2.0, 6.0): 0.0131313131313, (6.0, 6.0): 0.0151515151515, (7.0, 10.0): 0.0166666666667, (5.0, 11.0): 0.0106060606061, (4.0, 5.0): 0.0227272727273, (8.0, 11.0): 0.0262626262626, (6.0, 11.0): 0.0212121212121, (2.0, 7.0): 0.0217171717172, (7.0, 7.0): 0.0151515151515, (3.0, 9.0): 0.0151515151515, (1.0, 9.0): 0.010101010101, (8.0, 7.0): 0.0217171717172, (6.0, 12.0): 0.00757575757576, (6.0, 4.0): 0.0126262626263, (2.0, 10.0): 0.0131313131313, (6.0, 5.0): 0.0227272727273, (7.0, 9.0): 0.0151515151515, (6.0, 7.0): 0.0151515151515, (6.0, 9.0): 0.0151515151515, (5.0, 10.0): 0.0161616161616, (6.0, 8.0): 0.0151515151515, (8.0, 12.0): 0.00757575757576, (5.0, 7.0): 0.0151515151515, (2.0, 11.0): 0.0262626262626, (9.0, 9.0): 0.010101010101, (2.0, 4.0): 0.0126262626263, (3.0, 8.0): 0.0156565656566, (8.0, 8.0): 0.0131313131313, (1.0, 7.0): 0.010101010101, (9.0, 5.0): 0.010101010101, (7.0, 8.0): 0.0156565656566, (7.0, 6.0): 0.0169191919192, (8.0, 4.0): 0.0126262626263, }, ]
+3 Stop [{(5.0, 9.0): 0.0154882154882, (4.0, 7.0): 0.0151515151515, (6.0, 10.0): 0.0175925925926, (4.0, 8.0): 0.0152356902357, (5.0, 6.0): 0.0163089225589, (2.0, 8.0): 0.0160942760943, (3.0, 11.0): 0.0128114478114, (4.0, 6.0): 0.0169191919192, (8.0, 9.0): 0.0190740740741, (4.0, 12.0): 0.00606060606061, (2.0, 12.0): 0.00690235690236, (7.0, 11.0): 0.0128114478114, (3.0, 7.0): 0.0167087542088, (2.0, 5.0): 0.0246759259259, (1.0, 11.0): 0.00942760942761, (8.0, 5.0): 0.0246759259259, (5.0, 8.0): 0.0151515151515, (7.0, 5.0): 0.0174873737374, (4.0, 9.0): 0.0151515151515, (5.0, 5.0): 0.0165193602694, (2.0, 9.0): 0.0190740740741, (3.0, 10.0): 0.014696969697, (4.0, 4.0): 0.0138888888889, (8.0, 10.0): 0.0177777777778, (1.0, 5.0): 0.00963804713805, (9.0, 11.0): 0.00942760942761, (4.0, 11.0): 0.0153535353535, (3.0, 6.0): 0.0164941077441, (8.0, 6.0): 0.0172727272727, (3.0, 5.0): 0.0174873737374, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00867003367003, (4.0, 10.0): 0.0175925925926, (2.0, 6.0): 0.0172727272727, (6.0, 6.0): 0.0169191919192, (7.0, 10.0): 0.014696969697, (5.0, 11.0): 0.0118855218855, (4.0, 5.0): 0.022095959596, (8.0, 11.0): 0.0192255892256, (6.0, 11.0): 0.0153535353535, (2.0, 7.0): 0.0190740740741, (7.0, 7.0): 0.0167087542088, (3.0, 9.0): 0.016835016835, (1.0, 9.0): 0.00867003367003, (8.0, 7.0): 0.0190740740741, (6.0, 12.0): 0.00606060606061, (6.0, 4.0): 0.0138888888889, (2.0, 10.0): 0.0177777777778, (6.0, 5.0): 0.022095959596, (7.0, 9.0): 0.016835016835, (6.0, 7.0): 0.0151515151515, (6.0, 9.0): 0.0151515151515, (5.0, 10.0): 0.0145117845118, (6.0, 8.0): 0.0152356902357, (8.0, 12.0): 0.00690235690236, (5.0, 7.0): 0.015361952862, (2.0, 11.0): 0.0192255892256, (9.0, 9.0): 0.00867003367003, (2.0, 4.0): 0.0154882154882, (3.0, 8.0): 0.0153367003367, (8.0, 8.0): 0.0160942760943, (1.0, 7.0): 0.00867003367003, (9.0, 5.0): 0.00963804713805, (7.0, 8.0): 0.0153367003367, (7.0, 6.0): 0.0164941077441, (8.0, 4.0): 0.0154882154882, }, ]
+4 Stop [{(5.0, 9.0): 0.0149943883277, (4.0, 7.0): 0.0157687991021, (6.0, 10.0): 0.01544332211, (4.0, 8.0): 0.0151964085297, (5.0, 6.0): 0.0176083754209, (2.0, 8.0): 0.0153120089787, (3.0, 11.0): 0.0107749719416, (4.0, 6.0): 0.0170202020202, (8.0, 9.0): 0.0206498316498, (4.0, 12.0): 0.00457912457912, (2.0, 12.0): 0.00550505050505, (7.0, 11.0): 0.0107749719416, (3.0, 7.0): 0.016350308642, (2.0, 5.0): 0.0279567199776, (1.0, 11.0): 0.00791806958474, (8.0, 5.0): 0.0279567199776, (5.0, 8.0): 0.0153268799102, (7.0, 5.0): 0.0176651936027, (4.0, 9.0): 0.0163159371493, (5.0, 5.0): 0.0169314674523, (2.0, 9.0): 0.0206498316498, (3.0, 10.0): 0.0168675645342, (4.0, 4.0): 0.0143097643098, (8.0, 10.0): 0.0155925925926, (1.0, 5.0): 0.00893167789001, (9.0, 11.0): 0.00791806958474, (4.0, 11.0): 0.0144708193042, (3.0, 6.0): 0.0189648569024, (8.0, 6.0): 0.0166742424242, (3.0, 5.0): 0.0176651936027, (1, 1): 0.0151515151515, (9.0, 7.0): 0.0075140291807, (4.0, 10.0): 0.01544332211, (2.0, 6.0): 0.0166742424242, (6.0, 6.0): 0.0170202020202, (7.0, 10.0): 0.0168675645342, (5.0, 11.0): 0.00991358024691, (4.0, 5.0): 0.0247685185185, (8.0, 11.0): 0.0186374859708, (6.0, 11.0): 0.0144708193042, (2.0, 7.0): 0.0201910774411, (7.0, 7.0): 0.016350308642, (3.0, 9.0): 0.0159652076319, (1.0, 9.0): 0.0075140291807, (8.0, 7.0): 0.0201910774411, (6.0, 12.0): 0.00457912457912, (6.0, 4.0): 0.0143097643098, (2.0, 10.0): 0.0155925925926, (6.0, 5.0): 0.0247685185185, (7.0, 9.0): 0.0159652076319, (6.0, 7.0): 0.0157687991021, (6.0, 9.0): 0.0163159371493, (5.0, 10.0): 0.0156184062851, (6.0, 8.0): 0.0151964085297, (8.0, 12.0): 0.00550505050505, (5.0, 7.0): 0.0153794893378, (2.0, 11.0): 0.0186374859708, (9.0, 9.0): 0.0075140291807, (2.0, 4.0): 0.0159694163861, (3.0, 8.0): 0.0167107182941, (8.0, 8.0): 0.0153120089787, (1.0, 7.0): 0.0075140291807, (9.0, 5.0): 0.00893167789001, (7.0, 8.0): 0.0167107182941, (7.0, 6.0): 0.0189648569024, (8.0, 4.0): 0.0159694163861, }, ]
+5 Stop [{(5.0, 9.0): 0.0156983258511, (4.0, 7.0): 0.0158186026936, (6.0, 10.0): 0.0155311447811, (4.0, 8.0): 0.0159391133558, (5.0, 6.0): 0.0179674932192, (2.0, 8.0): 0.0160959783015, (3.0, 11.0): 0.0104843060232, (4.0, 6.0): 0.0183165918444, (8.0, 9.0): 0.0191589599701, (4.0, 12.0): 0.00393817807707, (2.0, 12.0): 0.00494126449682, (7.0, 11.0): 0.0104843060232, (3.0, 7.0): 0.0174494131126, (2.0, 5.0): 0.028195995838, (1.0, 11.0): 0.00706528245417, (8.0, 5.0): 0.028195995838, (5.0, 8.0): 0.0151813271605, (7.0, 5.0): 0.0195254571175, (4.0, 9.0): 0.0155597643098, (5.0, 5.0): 0.0183584981762, (2.0, 9.0): 0.0191589599701, (3.0, 10.0): 0.0154745230079, (4.0, 4.0): 0.0154110549944, (8.0, 10.0): 0.0155839132061, (1.0, 5.0): 0.0091252922746, (9.0, 11.0): 0.00706528245417, (4.0, 11.0): 0.0121761503928, (3.0, 6.0): 0.0191987595866, (8.0, 6.0): 0.0178854704452, (3.0, 5.0): 0.0195254571175, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00712219416386, (4.0, 10.0): 0.0155311447811, (2.0, 6.0): 0.0178854704452, (6.0, 6.0): 0.0183165918444, (7.0, 10.0): 0.0154745230079, (5.0, 11.0): 0.00940939019828, (4.0, 5.0): 0.0256055345118, (8.0, 11.0): 0.0160088290311, (6.0, 11.0): 0.0121761503928, (2.0, 7.0): 0.0193068976805, (7.0, 7.0): 0.0174494131126, (3.0, 9.0): 0.0172294706322, (1.0, 9.0): 0.00719865319865, (8.0, 7.0): 0.0193068976805, (6.0, 12.0): 0.00393817807707, (6.0, 4.0): 0.0154110549944, (2.0, 10.0): 0.0155839132061, (6.0, 5.0): 0.0256055345118, (7.0, 9.0): 0.0172294706322, (6.0, 7.0): 0.0158186026936, (6.0, 9.0): 0.0155597643098, (5.0, 10.0): 0.0142153385709, (6.0, 8.0): 0.0159391133558, (8.0, 12.0): 0.00494126449682, (5.0, 7.0): 0.0158632037972, (2.0, 11.0): 0.0160088290311, (9.0, 9.0): 0.00719865319865, (2.0, 4.0): 0.0173036148522, (3.0, 8.0): 0.016427043584, (8.0, 8.0): 0.0160959783015, (1.0, 7.0): 0.00712219416386, (9.0, 5.0): 0.0091252922746, (7.0, 8.0): 0.016427043584, (7.0, 6.0): 0.0191987595866, (8.0, 4.0): 0.0173036148522, }, ]
+6 Stop [{(5.0, 9.0): 0.0150716431288, (4.0, 7.0): 0.0165543396932, (6.0, 10.0): 0.0141888452425, (4.0, 8.0): 0.0157476025689, (5.0, 6.0): 0.0189774719611, (2.0, 8.0): 0.0155611725277, (3.0, 11.0): 0.00937344494326, (4.0, 6.0): 0.0187875974249, (8.0, 9.0): 0.0191168259758, (4.0, 12.0): 0.00334208442449, (2.0, 12.0): 0.00431522633745, (7.0, 11.0): 0.00937344494326, (3.0, 7.0): 0.0174379600402, (2.0, 5.0): 0.0299493386605, (1.0, 11.0): 0.00620077939893, (8.0, 5.0): 0.0299493386605, (5.0, 8.0): 0.0157199015619, (7.0, 5.0): 0.0202478725332, (4.0, 9.0): 0.0159148272852, (5.0, 5.0): 0.0191139671211, (2.0, 9.0): 0.0191168259758, (3.0, 10.0): 0.0153496947874, (4.0, 4.0): 0.0162407056678, (8.0, 10.0): 0.0142253061479, (1.0, 5.0): 0.00926197877697, (9.0, 11.0): 0.00620077939893, (4.0, 11.0): 0.0112220738247, (3.0, 6.0): 0.0205274879778, (8.0, 6.0): 0.0179118525533, (3.0, 5.0): 0.0202478725332, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00677891336202, (4.0, 10.0): 0.0141888452425, (2.0, 6.0): 0.0179118525533, (6.0, 6.0): 0.0187875974249, (7.0, 10.0): 0.0153496947874, (5.0, 11.0): 0.00830981793241, (4.0, 5.0): 0.0275496360207, (8.0, 11.0): 0.014708599576, (6.0, 11.0): 0.0112220738247, (2.0, 7.0): 0.0197026342904, (7.0, 7.0): 0.0174379600402, (3.0, 9.0): 0.0165540474186, (1.0, 9.0): 0.00679248659434, (8.0, 7.0): 0.0197026342904, (6.0, 12.0): 0.00334208442449, (6.0, 4.0): 0.0162407056678, (2.0, 10.0): 0.0142253061479, (6.0, 5.0): 0.0275496360207, (7.0, 9.0): 0.0165540474186, (6.0, 7.0): 0.0165543396932, (6.0, 9.0): 0.0159148272852, (5.0, 10.0): 0.0139264150767, (6.0, 8.0): 0.0157476025689, (8.0, 12.0): 0.00431522633745, (5.0, 7.0): 0.0159717594541, (2.0, 11.0): 0.014708599576, (9.0, 9.0): 0.00679248659434, (2.0, 4.0): 0.0180504727054, (3.0, 8.0): 0.0172649475465, (8.0, 8.0): 0.0155611725277, (1.0, 7.0): 0.00677891336202, (9.0, 5.0): 0.00926197877697, (7.0, 8.0): 0.0172649475465, (7.0, 6.0): 0.0205274879778, (8.0, 4.0): 0.0180504727054, }, ]
+7 Stop [{(5.0, 9.0): 0.0150790049024, (4.0, 7.0): 0.0167078102917, (6.0, 10.0): 0.0136373216735, (4.0, 8.0): 0.0161860743235, (5.0, 6.0): 0.0195278560668, (2.0, 8.0): 0.0156457734703, (3.0, 11.0): 0.00875475035333, (4.0, 6.0): 0.0198251454618, (8.0, 9.0): 0.0181437458276, (4.0, 12.0): 0.00298437377894, (2.0, 12.0): 0.00388984204182, (7.0, 11.0): 0.00875475035333, (3.0, 7.0): 0.0181253861824, (2.0, 5.0): 0.0308744913393, (1.0, 11.0): 0.00555182296213, (8.0, 5.0): 0.0308744913393, (5.0, 8.0): 0.0155550254019, (7.0, 5.0): 0.0214876265728, (4.0, 9.0): 0.0152776351478, (5.0, 5.0): 0.0202875277742, (2.0, 9.0): 0.0181437458276, (3.0, 10.0): 0.014276537115, (4.0, 4.0): 0.0173035648408, (8.0, 10.0): 0.0134923478821, (1.0, 5.0): 0.0096225458319, (9.0, 11.0): 0.00555182296213, (4.0, 11.0): 0.009999862036, (3.0, 6.0): 0.0210095062245, (8.0, 6.0): 0.0185627197139, (3.0, 5.0): 0.0214876265728, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00667322906275, (4.0, 10.0): 0.0136373216735, (2.0, 6.0): 0.0185627197139, (6.0, 6.0): 0.0198251454618, (7.0, 10.0): 0.014276537115, (5.0, 11.0): 0.00772372404082, (4.0, 5.0): 0.0288149512259, (8.0, 11.0): 0.0131483907387, (6.0, 11.0): 0.009999862036, (2.0, 7.0): 0.0193863952579, (7.0, 7.0): 0.0181253861824, (3.0, 9.0): 0.0165916729668, (1.0, 9.0): 0.0065823809598, (8.0, 7.0): 0.0193863952579, (6.0, 12.0): 0.00298437377894, (6.0, 4.0): 0.0173035648408, (2.0, 10.0): 0.0134923478821, (6.0, 5.0): 0.0288149512259, (7.0, 9.0): 0.0165916729668, (6.0, 7.0): 0.0167078102917, (6.0, 9.0): 0.0152776351478, (5.0, 10.0): 0.0128865519547, (6.0, 8.0): 0.0161860743235, (8.0, 12.0): 0.00388984204182, (5.0, 7.0): 0.0165829523209, (2.0, 11.0): 0.0131483907387, (9.0, 9.0): 0.0065823809598, (2.0, 4.0): 0.0190083492396, (3.0, 8.0): 0.017038668671, (8.0, 8.0): 0.0156457734703, (1.0, 7.0): 0.00667322906275, (9.0, 5.0): 0.0096225458319, (7.0, 8.0): 0.017038668671, (7.0, 6.0): 0.0210095062245, (8.0, 4.0): 0.0190083492396, }, ]
+8 Stop [{(5.0, 9.0): 0.0144937340849, (4.0, 7.0): 0.0172689071506, (6.0, 10.0): 0.0126796283272, (4.0, 8.0): 0.0160071414973, (5.0, 6.0): 0.0204625572156, (2.0, 8.0): 0.0152479139581, (3.0, 11.0): 0.00798841505227, (4.0, 6.0): 0.0204321799271, (8.0, 9.0): 0.0176065207925, (4.0, 12.0): 0.00266143493231, (2.0, 12.0): 0.00348801247038, (7.0, 11.0): 0.00798841505227, (3.0, 7.0): 0.0182177392164, (2.0, 5.0): 0.032258190621, (1.0, 11.0): 0.00496730993751, (8.0, 5.0): 0.032258190621, (5.0, 8.0): 0.0157780226958, (7.0, 5.0): 0.0223233158122, (4.0, 9.0): 0.0150681717813, (5.0, 5.0): 0.0211861510411, (2.0, 9.0): 0.0176065207925, (3.0, 10.0): 0.0136179583436, (4.0, 4.0): 0.0182567661624, (8.0, 10.0): 0.0124846469797, (1.0, 5.0): 0.00995702147249, (9.0, 11.0): 0.00496730993751, (4.0, 11.0): 0.00922480801638, (3.0, 6.0): 0.0219320212612, (8.0, 6.0): 0.0188220086227, (3.0, 5.0): 0.0223233158122, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00656768040769, (4.0, 10.0): 0.0126796283272, (2.0, 6.0): 0.0188220086227, (6.0, 6.0): 0.0204321799271, (7.0, 10.0): 0.0136179583436, (5.0, 11.0): 0.00702579081262, (4.0, 5.0): 0.0305064446987, (8.0, 11.0): 0.0120099576125, (6.0, 11.0): 0.00922480801638, (2.0, 7.0): 0.0195594314356, (7.0, 7.0): 0.0182177392164, (3.0, 9.0): 0.0159341328072, (1.0, 9.0): 0.00631514811784, (8.0, 7.0): 0.0195594314356, (6.0, 12.0): 0.00266143493231, (6.0, 4.0): 0.0182567661624, (2.0, 10.0): 0.0124846469797, (6.0, 5.0): 0.0305064446987, (7.0, 9.0): 0.0159341328072, (6.0, 7.0): 0.0172689071506, (6.0, 9.0): 0.0150681717813, (5.0, 10.0): 0.0122961896504, (6.0, 8.0): 0.0160071414973, (8.0, 12.0): 0.00348801247038, (5.0, 7.0): 0.0167727466292, (2.0, 11.0): 0.0120099576125, (9.0, 9.0): 0.00631514811784, (2.0, 4.0): 0.0197956717329, (3.0, 8.0): 0.0172180672125, (8.0, 8.0): 0.0152479139581, (1.0, 7.0): 0.00656768040769, (9.0, 5.0): 0.00995702147249, (7.0, 8.0): 0.0172180672125, (7.0, 6.0): 0.0219320212612, (8.0, 4.0): 0.0197956717329, }, ]
+9 Stop [{(5.0, 9.0): 0.0141667466073, (4.0, 7.0): 0.0174509759863, (6.0, 10.0): 0.0120185940225, (4.0, 8.0): 0.0160680803531, (5.0, 6.0): 0.0211086061483, (2.0, 8.0): 0.0150480061638, (3.0, 11.0): 0.0074064703392, (4.0, 6.0): 0.021311836234, (8.0, 9.0): 0.0167911245757, (4.0, 12.0): 0.00242461298017, (2.0, 12.0): 0.00316433042554, (7.0, 11.0): 0.0074064703392, (3.0, 7.0): 0.0185690389148, (2.0, 5.0): 0.0333623441083, (1.0, 11.0): 0.00448531457083, (8.0, 5.0): 0.0333623441083, (5.0, 8.0): 0.0155920867482, (7.0, 5.0): 0.0233522752601, (4.0, 9.0): 0.0144770728042, (5.0, 5.0): 0.022286205065, (2.0, 9.0): 0.0167911245757, (3.0, 10.0): 0.0127309153299, (4.0, 4.0): 0.0192971979808, (8.0, 10.0): 0.0117043284561, (1.0, 5.0): 0.0103548758397, (9.0, 11.0): 0.00448531457083, (4.0, 11.0): 0.00842787051844, (3.0, 6.0): 0.0224785106144, (8.0, 6.0): 0.0193159141834, (3.0, 5.0): 0.0233522752601, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00654374544312, (4.0, 10.0): 0.0120185940225, (2.0, 6.0): 0.0193159141834, (6.0, 6.0): 0.021311836234, (7.0, 10.0): 0.0127309153299, (5.0, 11.0): 0.00652945910971, (4.0, 5.0): 0.0319008838866, (8.0, 11.0): 0.0109052686242, (6.0, 11.0): 0.00842787051844, (2.0, 7.0): 0.0194436026203, (7.0, 7.0): 0.0185690389148, (3.0, 9.0): 0.0155104682135, (1.0, 9.0): 0.00609199419101, (8.0, 7.0): 0.0194436026203, (6.0, 12.0): 0.00242461298017, (6.0, 4.0): 0.0192971979808, (2.0, 10.0): 0.0117043284561, (6.0, 5.0): 0.0319008838866, (7.0, 9.0): 0.0155104682135, (6.0, 7.0): 0.0174509759863, (6.0, 9.0): 0.0144770728042, (5.0, 10.0): 0.01150184639, (6.0, 8.0): 0.0160680803531, (8.0, 12.0): 0.00316433042554, (5.0, 7.0): 0.0172215272563, (2.0, 11.0): 0.0109052686242, (9.0, 9.0): 0.00609199419101, (2.0, 4.0): 0.0206505660734, (3.0, 8.0): 0.0169347850484, (8.0, 8.0): 0.0150480061638, (1.0, 7.0): 0.00654374544312, (9.0, 5.0): 0.0103548758397, (7.0, 8.0): 0.0169347850484, (7.0, 6.0): 0.0224785106144, (8.0, 4.0): 0.0206505660734, }, ]
+10 Stop [{(5.0, 9.0): 0.0136194452906, (4.0, 7.0): 0.0177815898496, (6.0, 10.0): 0.0112640282639, (4.0, 8.0): 0.0158333456241, (5.0, 6.0): 0.0219341067877, (2.0, 8.0): 0.0146697073694, (3.0, 11.0): 0.00682530314658, (4.0, 6.0): 0.0219502981427, (8.0, 9.0): 0.0161209285089, (4.0, 12.0): 0.00221284941313, (2.0, 12.0): 0.00287232157921, (7.0, 11.0): 0.00682530314658, (3.0, 7.0): 0.0186352830388, (2.0, 5.0): 0.0346275461297, (1.0, 11.0): 0.00406020205611, (8.0, 5.0): 0.0346275461297, (5.0, 8.0): 0.0155472113209, (7.0, 5.0): 0.0242081103523, (4.0, 9.0): 0.0140432593372, (5.0, 5.0): 0.0232413812779, (2.0, 9.0): 0.0161209285089, (3.0, 10.0): 0.0120134500879, (4.0, 4.0): 0.0202822269526, (8.0, 10.0): 0.0108962952169, (1.0, 5.0): 0.0107378286046, (9.0, 11.0): 0.00406020205611, (4.0, 11.0): 0.00781133863339, (3.0, 6.0): 0.0231893224314, (8.0, 6.0): 0.0196511928306, (3.0, 5.0): 0.0242081103523, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00651247315827, (4.0, 10.0): 0.0112640282639, (2.0, 6.0): 0.0196511928306, (6.0, 6.0): 0.0219502981427, (7.0, 10.0): 0.0120134500879, (5.0, 11.0): 0.00603215639309, (4.0, 5.0): 0.0334789784641, (8.0, 11.0): 0.00999191543221, (6.0, 11.0): 0.00781133863339, (2.0, 7.0): 0.0194896982796, (7.0, 7.0): 0.0186352830388, (3.0, 9.0): 0.0148625468836, (1.0, 9.0): 0.00584451785813, (8.0, 7.0): 0.0194896982796, (6.0, 12.0): 0.00221284941313, (6.0, 4.0): 0.0202822269526, (2.0, 10.0): 0.0108962952169, (6.0, 5.0): 0.0334789784641, (7.0, 9.0): 0.0148625468836, (6.0, 7.0): 0.0177815898496, (6.0, 9.0): 0.0140432593372, (5.0, 10.0): 0.0108960804843, (6.0, 8.0): 0.0158333456241, (8.0, 12.0): 0.00287232157921, (5.0, 7.0): 0.0174027098123, (2.0, 11.0): 0.00999191543221, (9.0, 9.0): 0.00584451785813, (2.0, 4.0): 0.0214460644062, (3.0, 8.0): 0.01677507469, (8.0, 8.0): 0.0146697073694, (1.0, 7.0): 0.00651247315827, (9.0, 5.0): 0.0107378286046, (7.0, 8.0): 0.01677507469, (7.0, 6.0): 0.0231893224314, (8.0, 4.0): 0.0214460644062, }, ]
+11 Stop [{(5.0, 9.0): 0.0131742227091, (4.0, 7.0): 0.0179060953486, (6.0, 10.0): 0.0106399159067, (4.0, 8.0): 0.0156706233598, (5.0, 6.0): 0.0225836991024, (2.0, 8.0): 0.0143517131385, (3.0, 11.0): 0.00633451132156, (4.0, 6.0): 0.0226859809208, (8.0, 9.0): 0.0153786310551, (4.0, 12.0): 0.00203950624327, (2.0, 12.0): 0.0026227597651, (7.0, 11.0): 0.00633451132156, (3.0, 7.0): 0.0187743404965, (2.0, 5.0): 0.035775708914, (1.0, 11.0): 0.00369542026676, (8.0, 5.0): 0.035775708914, (5.0, 8.0): 0.0153092504938, (7.0, 5.0): 0.0251328891642, (4.0, 9.0): 0.0134811089439, (5.0, 5.0): 0.0242813737367, (2.0, 9.0): 0.0153786310551, (3.0, 10.0): 0.0112660511746, (4.0, 4.0): 0.0213007729643, (8.0, 10.0): 0.0101989606202, (1.0, 5.0): 0.0111401719906, (9.0, 11.0): 0.00369542026676, (4.0, 11.0): 0.00722595266624, (3.0, 6.0): 0.0237172972628, (8.0, 6.0): 0.0200629494195, (3.0, 5.0): 0.0251328891642, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00650451962574, (4.0, 10.0): 0.0106399159067, (2.0, 6.0): 0.0200629494195, (6.0, 6.0): 0.0226859809208, (7.0, 10.0): 0.0112660511746, (5.0, 11.0): 0.00562622423713, (4.0, 5.0): 0.0349000821754, (8.0, 11.0): 0.00915462099225, (6.0, 11.0): 0.00722595266624, (2.0, 7.0): 0.0194085216461, (7.0, 7.0): 0.0187743404965, (3.0, 9.0): 0.0143047849326, (1.0, 9.0): 0.00560908034722, (8.0, 7.0): 0.0194085216461, (6.0, 12.0): 0.00203950624327, (6.0, 4.0): 0.0213007729643, (2.0, 10.0): 0.0101989606202, (6.0, 5.0): 0.0349000821754, (7.0, 9.0): 0.0143047849326, (6.0, 7.0): 0.0179060953486, (6.0, 9.0): 0.0134811089439, (5.0, 10.0): 0.0102534596077, (6.0, 8.0): 0.0156706233598, (8.0, 12.0): 0.0026227597651, (5.0, 7.0): 0.0176657364902, (2.0, 11.0): 0.00915462099225, (9.0, 9.0): 0.00560908034722, (2.0, 4.0): 0.0222655475796, (3.0, 8.0): 0.0164287409939, (8.0, 8.0): 0.0143517131385, (1.0, 7.0): 0.00650451962574, (9.0, 5.0): 0.0111401719906, (7.0, 8.0): 0.0164287409939, (7.0, 6.0): 0.0237172972628, (8.0, 4.0): 0.0222655475796, }, ]
+12 Stop [{(5.0, 9.0): 0.0126587683843, (4.0, 7.0): 0.0180622333293, (6.0, 10.0): 0.0100154068275, (4.0, 8.0): 0.015379488014, (5.0, 6.0): 0.0232848657549, (2.0, 8.0): 0.0139694300861, (3.0, 11.0): 0.00587467306982, (4.0, 6.0): 0.0232832083598, (8.0, 9.0): 0.0147017030473, (4.0, 12.0): 0.0018841608588, (2.0, 12.0): 0.00240002342041, (7.0, 11.0): 0.00587467306982, (3.0, 7.0): 0.018777289457, (2.0, 5.0): 0.0369738799963, (1.0, 11.0): 0.00337348029875, (8.0, 5.0): 0.0369738799963, (5.0, 8.0): 0.0151107798536, (7.0, 5.0): 0.0259682865602, (4.0, 9.0): 0.0129850952931, (5.0, 5.0): 0.0252316038601, (2.0, 9.0): 0.0147017030473, (3.0, 10.0): 0.0106087219883, (4.0, 4.0): 0.0222837472073, (8.0, 10.0): 0.00953211282641, (1.0, 5.0): 0.0115327041476, (9.0, 11.0): 0.00337348029875, (4.0, 11.0): 0.00672946270274, (3.0, 6.0): 0.0242878053711, (8.0, 6.0): 0.0203975981287, (3.0, 5.0): 0.0259682865602, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00648701342055, (4.0, 10.0): 0.0100154068275, (2.0, 6.0): 0.0203975981287, (6.0, 6.0): 0.0232832083598, (7.0, 10.0): 0.0106087219883, (5.0, 11.0): 0.00524280567079, (4.0, 5.0): 0.0363826262102, (8.0, 11.0): 0.00842868119718, (6.0, 11.0): 0.00672946270274, (2.0, 7.0): 0.0193693453093, (7.0, 7.0): 0.018777289457, (3.0, 9.0): 0.0136875613791, (1.0, 9.0): 0.00536764534946, (8.0, 7.0): 0.0193693453093, (6.0, 12.0): 0.0018841608588, (6.0, 4.0): 0.0222837472073, (2.0, 10.0): 0.00953211282641, (6.0, 5.0): 0.0363826262102, (7.0, 9.0): 0.0136875613791, (6.0, 7.0): 0.0180622333293, (6.0, 9.0): 0.0129850952931, (5.0, 10.0): 0.00970174204987, (6.0, 8.0): 0.015379488014, (8.0, 12.0): 0.00240002342041, (5.0, 7.0): 0.0177800212129, (2.0, 11.0): 0.00842868119718, (9.0, 9.0): 0.00536764534946, (2.0, 4.0): 0.0230580100945, (3.0, 8.0): 0.0161175550803, (8.0, 8.0): 0.0139694300861, (1.0, 7.0): 0.00648701342055, (9.0, 5.0): 0.0115327041476, (7.0, 8.0): 0.0161175550803, (7.0, 6.0): 0.0242878053711, (8.0, 4.0): 0.0230580100945, }, ]
+13 Stop [{(5.0, 9.0): 0.012190537154, (4.0, 7.0): 0.0181102880645, (6.0, 10.0): 0.00946164859405, (4.0, 8.0): 0.0151067078106, (5.0, 6.0): 0.0238764551151, (2.0, 8.0): 0.0136089370979, (3.0, 11.0): 0.005469412262, (4.0, 6.0): 0.0238938287258, (8.0, 9.0): 0.0140220978936, (4.0, 12.0): 0.00174963073672, (2.0, 12.0): 0.00220478800633, (7.0, 11.0): 0.005469412262, (3.0, 7.0): 0.0187886306045, (2.0, 5.0): 0.0381087813453, (1.0, 11.0): 0.00309152034891, (8.0, 5.0): 0.0381087813453, (5.0, 8.0): 0.0148278856438, (7.0, 5.0): 0.0268140911315, (4.0, 9.0): 0.0124569544543, (5.0, 5.0): 0.0261970649534, (2.0, 9.0): 0.0140220978936, (3.0, 10.0): 0.0099749068257, (4.0, 4.0): 0.0232694156737, (8.0, 10.0): 0.00893438713695, (1.0, 5.0): 0.0119286654065, (9.0, 11.0): 0.00309152034891, (4.0, 11.0): 0.0062704145757, (3.0, 6.0): 0.0247591900399, (8.0, 6.0): 0.0207462489567, (3.0, 5.0): 0.0268140911315, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00647173092849, (4.0, 10.0): 0.00946164859405, (2.0, 6.0): 0.0207462489567, (6.0, 6.0): 0.0238938287258, (7.0, 10.0): 0.0099749068257, (5.0, 11.0): 0.00490867237671, (4.0, 5.0): 0.037766686697, (8.0, 11.0): 0.00777289314176, (6.0, 11.0): 0.0062704145757, (2.0, 7.0): 0.0192685708315, (7.0, 7.0): 0.0187886306045, (3.0, 9.0): 0.0131182264627, (1.0, 9.0): 0.00513410651596, (8.0, 7.0): 0.0192685708315, (6.0, 12.0): 0.00174963073672, (6.0, 4.0): 0.0232694156737, (2.0, 10.0): 0.00893438713695, (6.0, 5.0): 0.037766686697, (7.0, 9.0): 0.0131182264627, (6.0, 7.0): 0.0181102880645, (6.0, 9.0): 0.0124569544543, (5.0, 10.0): 0.00916234294986, (6.0, 8.0): 0.0151067078106, (8.0, 12.0): 0.00220478800633, (5.0, 7.0): 0.0179018188889, (2.0, 11.0): 0.00777289314176, (9.0, 9.0): 0.00513410651596, (2.0, 4.0): 0.0238536317127, (3.0, 8.0): 0.0157354619021, (8.0, 8.0): 0.0136089370979, (1.0, 7.0): 0.00647173092849, (9.0, 5.0): 0.0119286654065, (7.0, 8.0): 0.0157354619021, (7.0, 6.0): 0.0247591900399, (8.0, 4.0): 0.0238536317127, }, ]
+14 Stop [{(5.0, 9.0): 0.0117095029343, (4.0, 7.0): 0.0181513303175, (6.0, 10.0): 0.00893278032921, (4.0, 8.0): 0.0147823753882, (5.0, 6.0): 0.0244605579624, (2.0, 8.0): 0.0132298255064, (3.0, 11.0): 0.00509691820959, (4.0, 6.0): 0.0244194561178, (8.0, 9.0): 0.0133859829251, (4.0, 12.0): 0.00162827934152, (2.0, 12.0): 0.00203041152574, (7.0, 11.0): 0.00509691820959, (3.0, 7.0): 0.0187329338908, (2.0, 5.0): 0.0392446344827, (1.0, 11.0): 0.00284124236475, (8.0, 5.0): 0.0392446344827, (5.0, 8.0): 0.0145540324103, (7.0, 5.0): 0.0276024974699, (4.0, 9.0): 0.0119659538449, (5.0, 5.0): 0.0270969801757, (2.0, 9.0): 0.0133859829251, (3.0, 10.0): 0.00940043931261, (4.0, 4.0): 0.0242236034025, (8.0, 10.0): 0.00837734259453, (1.0, 5.0): 0.0123157962608, (9.0, 11.0): 0.00284124236475, (4.0, 11.0): 0.00586404794718, (3.0, 6.0): 0.0252244859033, (8.0, 6.0): 0.021050101966, (3.0, 5.0): 0.0276024974699, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00644729393616, (4.0, 10.0): 0.00893278032921, (2.0, 6.0): 0.021050101966, (6.0, 6.0): 0.0244194561178, (7.0, 10.0): 0.00940043931261, (5.0, 11.0): 0.00459892982555, (4.0, 5.0): 0.0391465542162, (8.0, 11.0): 0.00719186091543, (6.0, 11.0): 0.00586404794718, (2.0, 7.0): 0.0191715570008, (7.0, 7.0): 0.0187329338908, (3.0, 9.0): 0.012547092394, (1.0, 9.0): 0.00490406957357, (8.0, 7.0): 0.0191715570008, (6.0, 12.0): 0.00162827934152, (6.0, 4.0): 0.0242236034025, (2.0, 10.0): 0.00837734259453, (6.0, 5.0): 0.0391465542162, (7.0, 9.0): 0.012547092394, (6.0, 7.0): 0.0181513303175, (6.0, 9.0): 0.0119659538449, (5.0, 10.0): 0.00867616516601, (6.0, 8.0): 0.0147823753882, (8.0, 12.0): 0.00203041152574, (5.0, 7.0): 0.0179424369034, (2.0, 11.0): 0.00719186091543, (9.0, 9.0): 0.00490406957357, (2.0, 4.0): 0.0246297429714, (3.0, 8.0): 0.0153663296267, (8.0, 8.0): 0.0132298255064, (1.0, 7.0): 0.00644729393616, (9.0, 5.0): 0.0123157962608, (7.0, 8.0): 0.0153663296267, (7.0, 6.0): 0.0252244859033, (8.0, 4.0): 0.0246297429714, }, ]
+15 Stop [{(5.0, 9.0): 0.0112579622278, (4.0, 7.0): 0.0181351513343, (6.0, 10.0): 0.00845057242452, (4.0, 8.0): 0.0144643292388, (5.0, 6.0): 0.0249716357114, (2.0, 8.0): 0.0128642738475, (3.0, 11.0): 0.00476210833779, (4.0, 6.0): 0.0249256191391, (8.0, 9.0): 0.0127711161457, (4.0, 12.0): 0.00152010110504, (2.0, 12.0): 0.00187544732782, (7.0, 11.0): 0.00476210833779, (3.0, 7.0): 0.0186688277276, (2.0, 5.0): 0.0403342071838, (1.0, 11.0): 0.00261926466828, (8.0, 5.0): 0.0403342071838, (5.0, 8.0): 0.0142467043265, (7.0, 5.0): 0.0283739844517, (4.0, 9.0): 0.0114784142033, (5.0, 5.0): 0.0279766157701, (2.0, 9.0): 0.0127711161457, (3.0, 10.0): 0.00886095447538, (4.0, 4.0): 0.0251606531067, (8.0, 10.0): 0.00787049253033, (1.0, 5.0): 0.0126986705442, (9.0, 11.0): 0.00261926466828, (4.0, 11.0): 0.00549082721411, (3.0, 6.0): 0.0256289463945, (8.0, 6.0): 0.0213453927911, (3.0, 5.0): 0.0283739844517, (1, 1): 0.0151515151515, (9.0, 7.0): 0.0064189064682, (4.0, 10.0): 0.00845057242452, (2.0, 6.0): 0.0213453927911, (6.0, 6.0): 0.0249256191391, (7.0, 10.0): 0.00886095447538, (5.0, 11.0): 0.00432049614184, (4.0, 5.0): 0.0404509155213, (8.0, 11.0): 0.00666772451293, (6.0, 11.0): 0.00549082721411, (2.0, 7.0): 0.0190430127124, (7.0, 7.0): 0.0186688277276, (3.0, 9.0): 0.0120110395693, (1.0, 9.0): 0.00468303194096, (8.0, 7.0): 0.0190430127124, (6.0, 12.0): 0.00152010110504, (6.0, 4.0): 0.0251606531067, (2.0, 10.0): 0.00787049253033, (6.0, 5.0): 0.0404509155213, (7.0, 9.0): 0.0120110395693, (6.0, 7.0): 0.0181351513343, (6.0, 9.0): 0.0114784142033, (5.0, 10.0): 0.00821477672335, (6.0, 8.0): 0.0144643292388, (8.0, 12.0): 0.00187544732782, (5.0, 7.0): 0.0179689533869, (2.0, 11.0): 0.00666772451293, (9.0, 9.0): 0.00468303194096, (2.0, 4.0): 0.0253964163133, (3.0, 8.0): 0.0149752690502, (8.0, 8.0): 0.0128642738475, (1.0, 7.0): 0.0064189064682, (9.0, 5.0): 0.0126986705442, (7.0, 8.0): 0.0149752690502, (7.0, 6.0): 0.0256289463945, (8.0, 4.0): 0.0253964163133, }, ]
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+18 Stop [{(5.0, 9.0): 0.0099936782856, (4.0, 7.0): 0.0179703903193, (6.0, 10.0): 0.00719004631663, (4.0, 8.0): 0.0134733092894, (5.0, 6.0): 0.0262823803756, (2.0, 8.0): 0.0118064248637, (3.0, 11.0): 0.0039224082989, (4.0, 6.0): 0.0261727824634, (8.0, 9.0): 0.0111271320209, (4.0, 12.0): 0.00125155310753, (2.0, 12.0): 0.00149935264715, (7.0, 11.0): 0.0039224082989, (3.0, 7.0): 0.0183411578457, (2.0, 5.0): 0.0434192570762, (1.0, 11.0): 0.0020838045863, (8.0, 5.0): 0.0434192570762, (5.0, 8.0): 0.0133238557385, (7.0, 5.0): 0.0304580136255, (4.0, 9.0): 0.0101564082138, (5.0, 5.0): 0.0303322804662, (2.0, 9.0): 0.0111271320209, (3.0, 10.0): 0.0074806168997, (4.0, 4.0): 0.0277753180355, (8.0, 10.0): 0.00657689731208, (1.0, 5.0): 0.0137892218211, (9.0, 11.0): 0.0020838045863, (4.0, 11.0): 0.0045604340855, (3.0, 6.0): 0.0266677322463, (8.0, 6.0): 0.0220887739637, (3.0, 5.0): 0.0304580136255, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00629834786213, (4.0, 10.0): 0.00719004631663, (2.0, 6.0): 0.0220887739637, (6.0, 6.0): 0.0261727824634, (7.0, 10.0): 0.0074806168997, (5.0, 11.0): 0.00361236536442, (4.0, 5.0): 0.0440679291686, (8.0, 11.0): 0.0053883479952, (6.0, 11.0): 0.0045604340855, (2.0, 7.0): 0.0186071851321, (7.0, 7.0): 0.0183411578457, (3.0, 9.0): 0.0105413950902, (1.0, 9.0): 0.00407415174676, (8.0, 7.0): 0.0186071851321, (6.0, 12.0): 0.00125155310753, (6.0, 4.0): 0.0277753180355, (2.0, 10.0): 0.00657689731208, (6.0, 5.0): 0.0440679291686, (7.0, 9.0): 0.0105413950902, (6.0, 7.0): 0.0179703903193, (6.0, 9.0): 0.0101564082138, (5.0, 10.0): 0.00702299339131, (6.0, 8.0): 0.0134733092894, (8.0, 12.0): 0.00149935264715, (5.0, 7.0): 0.0178582381322, (2.0, 11.0): 0.0053883479952, (9.0, 9.0): 0.00407415174676, (2.0, 4.0): 0.0275783280453, (3.0, 8.0): 0.013844626469, (8.0, 8.0): 0.0118064248637, (1.0, 7.0): 0.00629834786213, (9.0, 5.0): 0.0137892218211, (7.0, 8.0): 0.013844626469, (7.0, 6.0): 0.0266677322463, (8.0, 4.0): 0.0275783280453, }, ]
+19 Stop [{(5.0, 9.0): 0.0096127228724, (4.0, 7.0): 0.0178815312232, (6.0, 10.0): 0.00682848883208, (4.0, 8.0): 0.013154166374, (5.0, 6.0): 0.026640473711, (2.0, 8.0): 0.0114789642465, (3.0, 11.0): 0.00368938148985, (4.0, 6.0): 0.0265219341487, (8.0, 9.0): 0.0106405409561, (4.0, 12.0): 0.00117725671676, (2.0, 12.0): 0.00139784221492, (7.0, 11.0): 0.00368938148985, (3.0, 7.0): 0.0182126197469, (2.0, 5.0): 0.044370330771, (1.0, 11.0): 0.00193996029235, (8.0, 5.0): 0.044370330771, (5.0, 8.0): 0.0130193448368, (7.0, 5.0): 0.0310849451959, (4.0, 9.0): 0.00975748058539, (5.0, 5.0): 0.031033173298, (2.0, 9.0): 0.0106405409561, (3.0, 10.0): 0.00708635249973, (4.0, 4.0): 0.0285769687406, (8.0, 10.0): 0.00621278694758, (1.0, 5.0): 0.0141311537566, (9.0, 11.0): 0.00193996029235, (4.0, 11.0): 0.00429973687137, (3.0, 6.0): 0.0269527299326, (8.0, 6.0): 0.0223013147238, (3.0, 5.0): 0.0310849451959, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00625037145307, (4.0, 10.0): 0.00682848883208, (2.0, 6.0): 0.0223013147238, (6.0, 6.0): 0.0265219341487, (7.0, 10.0): 0.00708635249973, (5.0, 11.0): 0.0034131166666, (4.0, 5.0): 0.0451541482232, (8.0, 11.0): 0.00503938984597, (6.0, 11.0): 0.00429973687137, (2.0, 7.0): 0.0184475558322, (7.0, 7.0): 0.0182126197469, (3.0, 9.0): 0.0101051325989, (1.0, 9.0): 0.00389159787685, (8.0, 7.0): 0.0184475558322, (6.0, 12.0): 0.00117725671676, (6.0, 4.0): 0.0285769687406, (2.0, 10.0): 0.00621278694758, (6.0, 5.0): 0.0451541482232, (7.0, 9.0): 0.0101051325989, (6.0, 7.0): 0.0178815312232, (6.0, 9.0): 0.00975748058539, (5.0, 10.0): 0.00667774019746, (6.0, 8.0): 0.013154166374, (8.0, 12.0): 0.00139784221492, (5.0, 7.0): 0.0177881851039, (2.0, 11.0): 0.00503938984597, (9.0, 9.0): 0.00389159787685, (2.0, 4.0): 0.0282622497147, (3.0, 8.0): 0.0134849322701, (8.0, 8.0): 0.0114789642465, (1.0, 7.0): 0.00625037145307, (9.0, 5.0): 0.0141311537566, (7.0, 8.0): 0.0134849322701, (7.0, 6.0): 0.0269527299326, (8.0, 4.0): 0.0282622497147, }, ]
+20 Stop [{(5.0, 9.0): 0.00925041821248, (4.0, 7.0): 0.0177854338285, (6.0, 10.0): 0.00649158930957, (4.0, 8.0): 0.0128424893125, (5.0, 6.0): 0.0269684120272, (2.0, 8.0): 0.0111647211851, (3.0, 11.0): 0.00347545616748, (4.0, 6.0): 0.0268387247437, (8.0, 9.0): 0.0101843188259, (4.0, 12.0): 0.00110904171748, (2.0, 12.0): 0.00130584571263, (7.0, 11.0): 0.00347545616748, (3.0, 7.0): 0.0180773835459, (2.0, 5.0): 0.0452835190526, (1.0, 11.0): 0.00180987845384, (8.0, 5.0): 0.0452835190526, (5.0, 8.0): 0.0127235514056, (7.0, 5.0): 0.0316762261089, (4.0, 9.0): 0.0093810800158, (5.0, 5.0): 0.0316898339692, (2.0, 9.0): 0.0101843188259, (3.0, 10.0): 0.0067216389739, (4.0, 4.0): 0.0293398671114, (8.0, 10.0): 0.00587683624748, (1.0, 5.0): 0.0144606320068, (9.0, 11.0): 0.00180987845384, (4.0, 11.0): 0.00406004172637, (3.0, 6.0): 0.0272148165063, (8.0, 6.0): 0.0224966250061, (3.0, 5.0): 0.0316762261089, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00619977836524, (4.0, 10.0): 0.00649158930957, (2.0, 6.0): 0.0224966250061, (6.0, 6.0): 0.0268387247437, (7.0, 10.0): 0.0067216389739, (5.0, 11.0): 0.00322882565669, (4.0, 5.0): 0.0461843500806, (8.0, 11.0): 0.00472220695126, (6.0, 11.0): 0.00406004172637, (2.0, 7.0): 0.0182870636331, (7.0, 7.0): 0.0180773835459, (3.0, 9.0): 0.009693465235, (1.0, 9.0): 0.00371922243111, (8.0, 7.0): 0.0182870636331, (6.0, 12.0): 0.00110904171748, (6.0, 4.0): 0.0293398671114, (2.0, 10.0): 0.00587683624748, (6.0, 5.0): 0.0461843500806, (7.0, 9.0): 0.009693465235, (6.0, 7.0): 0.0177854338285, (6.0, 9.0): 0.0093810800158, (5.0, 10.0): 0.00635648678898, (6.0, 8.0): 0.0128424893125, (8.0, 12.0): 0.00130584571263, (5.0, 7.0): 0.0177050684892, (2.0, 11.0): 0.00472220695126, (9.0, 9.0): 0.00371922243111, (2.0, 4.0): 0.0289212351144, (3.0, 8.0): 0.0131394567808, (8.0, 8.0): 0.0111647211851, (1.0, 7.0): 0.00619977836524, (9.0, 5.0): 0.0144606320068, (7.0, 8.0): 0.0131394567808, (7.0, 6.0): 0.0272148165063, (8.0, 4.0): 0.0289212351144, }, ]
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+85 Stop [{(5.0, 9.0): 0.00385364129367, (4.0, 7.0): 0.0152090829053, (6.0, 10.0): 0.00194443883491, (4.0, 8.0): 0.0076475003074, (5.0, 6.0): 0.0303247943113, (2.0, 8.0): 0.00637217508978, (3.0, 11.0): 0.000816553570377, (4.0, 6.0): 0.0303177815188, (8.0, 9.0): 0.0038582328338, (4.0, 12.0): 0.000245844019352, (2.0, 12.0): 0.000246920758867, (7.0, 11.0): 0.000816553570377, (3.0, 7.0): 0.0152053381888, (2.0, 5.0): 0.0604634985257, (1.0, 11.0): 0.000329745618847, (8.0, 5.0): 0.0604634985257, (5.0, 8.0): 0.00764780064681, (7.0, 5.0): 0.0403523014289, (4.0, 9.0): 0.00385403093888, (5.0, 5.0): 0.0403813085404, (2.0, 9.0): 0.0038582328338, (3.0, 10.0): 0.00194524021081, (4.0, 4.0): 0.0403513815023, (8.0, 10.0): 0.00162250719499, (1.0, 5.0): 0.0201437553055, (9.0, 11.0): 0.000329745618847, (4.0, 11.0): 0.000979791111912, (3.0, 6.0): 0.0303068541925, (8.0, 6.0): 0.0252413294412, (3.0, 5.0): 0.0403523014289, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00506903984655, (4.0, 10.0): 0.00194443883491, (2.0, 6.0): 0.0252413294412, (6.0, 6.0): 0.0303177815188, (7.0, 10.0): 0.00194524021081, (5.0, 11.0): 0.00081532624301, (4.0, 5.0): 0.0605474887259, (8.0, 11.0): 0.000983653956823, (6.0, 11.0): 0.000979791111912, (2.0, 7.0): 0.0152018027171, (7.0, 7.0): 0.0152053381888, (3.0, 9.0): 0.00385505901906, (1.0, 9.0): 0.00128953306957, (8.0, 7.0): 0.0152018027171, (6.0, 12.0): 0.000245844019352, (6.0, 4.0): 0.0403513815023, (2.0, 10.0): 0.00162250719499, (6.0, 5.0): 0.0605474887259, (7.0, 9.0): 0.00385505901906, (6.0, 7.0): 0.0152090829053, (6.0, 9.0): 0.00385403093888, (5.0, 10.0): 0.00194372373282, (6.0, 8.0): 0.0076475003074, (8.0, 12.0): 0.000246920758867, (5.0, 7.0): 0.0152110952645, (2.0, 11.0): 0.000983653956823, (9.0, 9.0): 0.00128953306957, (2.0, 4.0): 0.0402875106109, (3.0, 8.0): 0.00764700596318, (8.0, 8.0): 0.00637217508978, (1.0, 7.0): 0.00506903984655, (9.0, 5.0): 0.0201437553055, (7.0, 8.0): 0.00764700596318, (7.0, 6.0): 0.0303068541925, (8.0, 4.0): 0.0402875106109, }, ]
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+89 Stop [{(5.0, 9.0): 0.00383864808998, (4.0, 7.0): 0.0151993100192, (6.0, 10.0): 0.00193262811071, (4.0, 8.0): 0.00763210974996, (5.0, 6.0): 0.030328190726, (2.0, 8.0): 0.00635943952577, (3.0, 11.0): 0.000810069436717, (4.0, 6.0): 0.0303227894348, (8.0, 9.0): 0.00384205609204, (4.0, 12.0): 0.000243685785681, (2.0, 12.0): 0.000244495769527, (7.0, 11.0): 0.000810069436717, (3.0, 7.0): 0.0151963446992, (2.0, 5.0): 0.0605141595651, (1.0, 11.0): 0.000326385453241, (8.0, 5.0): 0.0605141595651, (5.0, 8.0): 0.00763236132986, (7.0, 5.0): 0.040375989833, (4.0, 9.0): 0.00383893119758, (5.0, 5.0): 0.0403984888833, (2.0, 9.0): 0.00384205609204, (3.0, 10.0): 0.00193322138389, (4.0, 4.0): 0.0403758787434, (8.0, 10.0): 0.00161212254621, (1.0, 5.0): 0.0201632097699, (9.0, 11.0): 0.000326385453241, (4.0, 11.0): 0.000972034077698, (3.0, 6.0): 0.0303142213173, (8.0, 6.0): 0.0252507452189, (3.0, 5.0): 0.040375989833, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00506584682923, (4.0, 10.0): 0.00193262811071, (2.0, 6.0): 0.0252507452189, (6.0, 6.0): 0.0303227894348, (7.0, 10.0): 0.00193322138389, (5.0, 11.0): 0.000809149098127, (4.0, 5.0): 0.0605790979644, (8.0, 11.0): 0.000974940522014, (6.0, 11.0): 0.000972034077698, (2.0, 7.0): 0.0151935500346, (7.0, 7.0): 0.0151963446992, (3.0, 9.0): 0.00383968140561, (1.0, 9.0): 0.00128329425523, (8.0, 7.0): 0.0151935500346, (6.0, 12.0): 0.000243685785681, (6.0, 4.0): 0.0403758787434, (2.0, 10.0): 0.00161212254621, (6.0, 5.0): 0.0605790979644, (7.0, 9.0): 0.00383968140561, (6.0, 7.0): 0.0151993100192, (6.0, 9.0): 0.00383893119758, (5.0, 10.0): 0.00193209298603, (6.0, 8.0): 0.00763210974996, (8.0, 12.0): 0.000244495769527, (5.0, 7.0): 0.0152008832101, (2.0, 11.0): 0.000974940522014, (9.0, 9.0): 0.00128329425523, (2.0, 4.0): 0.0403264195397, (3.0, 8.0): 0.00763167698202, (8.0, 8.0): 0.00635943952577, (1.0, 7.0): 0.00506584682923, (9.0, 5.0): 0.0201632097699, (7.0, 8.0): 0.00763167698202, (7.0, 6.0): 0.0303142213173, (8.0, 4.0): 0.0403264195397, }, ]
+90 Stop [{(5.0, 9.0): 0.00383550963118, (4.0, 7.0): 0.0151972578105, (6.0, 10.0): 0.00193015697227, (4.0, 8.0): 0.00762888674603, (5.0, 6.0): 0.0303288782235, (2.0, 8.0): 0.00635677777194, (3.0, 11.0): 0.000808713217943, (4.0, 6.0): 0.0303238199135, (8.0, 9.0): 0.0038386736342, (4.0, 12.0): 0.000243234274843, (2.0, 12.0): 0.000243988676845, (7.0, 11.0): 0.000808713217943, (3.0, 7.0): 0.015194463339, (2.0, 5.0): 0.0605248034615, (1.0, 11.0): 0.000325682813623, (8.0, 5.0): 0.0605248034615, (5.0, 8.0): 0.00762912670331, (7.0, 5.0): 0.0403809474856, (4.0, 9.0): 0.00383577111076, (5.0, 5.0): 0.0404020517843, (2.0, 9.0): 0.0038386736342, (3.0, 10.0): 0.00193070743396, (4.0, 4.0): 0.0403809720265, (8.0, 10.0): 0.00160995092923, (1.0, 5.0): 0.0201672981458, (9.0, 11.0): 0.000325682813623, (4.0, 11.0): 0.000970411262158, (3.0, 6.0): 0.0303157631938, (8.0, 6.0): 0.0252527292024, (3.0, 5.0): 0.0403809474856, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00506518175372, (4.0, 10.0): 0.00193015697227, (2.0, 6.0): 0.0252527292024, (6.0, 6.0): 0.0303238199135, (7.0, 10.0): 0.00193070743396, (5.0, 11.0): 0.00080785667653, (4.0, 5.0): 0.0605856718564, (8.0, 11.0): 0.000973118389894, (6.0, 11.0): 0.000970411262158, (2.0, 7.0): 0.0151918307243, (7.0, 7.0): 0.015194463339, (3.0, 9.0): 0.00383646474084, (1.0, 9.0): 0.00128198980962, (8.0, 7.0): 0.0151918307243, (6.0, 12.0): 0.000243234274843, (6.0, 4.0): 0.0403809720265, (2.0, 10.0): 0.00160995092923, (6.0, 5.0): 0.0605856718564, (7.0, 9.0): 0.00383646474084, (6.0, 7.0): 0.0151972578105, (6.0, 9.0): 0.00383577111076, (5.0, 10.0): 0.00192965918882, (6.0, 8.0): 0.00762888674603, (8.0, 12.0): 0.000243988676845, (5.0, 7.0): 0.0151987361057, (2.0, 11.0): 0.000973118389894, (9.0, 9.0): 0.00128198980962, (2.0, 4.0): 0.0403345962916, (3.0, 8.0): 0.00762847027888, (8.0, 8.0): 0.00635677777194, (1.0, 7.0): 0.00506518175372, (9.0, 5.0): 0.0201672981458, (7.0, 8.0): 0.00762847027888, (7.0, 6.0): 0.0303157631938, (8.0, 4.0): 0.0403345962916, }, ]
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+96 Stop [{(5.0, 9.0): 0.00382065660904, (4.0, 7.0): 0.0151875098505, (6.0, 10.0): 0.0019184683617, (4.0, 8.0): 0.00761362611367, (5.0, 6.0): 0.0303320025563, (2.0, 8.0): 0.00634420353629, (3.0, 11.0): 0.000802300650353, (4.0, 6.0): 0.0303285986116, (8.0, 9.0): 0.0038226864151, (4.0, 12.0): 0.000241098890762, (2.0, 12.0): 0.000241591633299, (7.0, 11.0): 0.000802300650353, (3.0, 7.0): 0.015185566409, (2.0, 5.0): 0.0605753932381, (1.0, 11.0): 0.000322361459323, (8.0, 5.0): 0.0605753932381, (5.0, 8.0): 0.00761380327101, (7.0, 5.0): 0.0404044049102, (4.0, 9.0): 0.00382081942492, (5.0, 5.0): 0.0404187293602, (2.0, 9.0): 0.0038226864151, (3.0, 10.0): 0.00191882030608, (4.0, 4.0): 0.0404048853564, (8.0, 10.0): 0.00159968532142, (1.0, 5.0): 0.0201867353554, (9.0, 11.0): 0.000322361459323, (4.0, 11.0): 0.000962736212839, (3.0, 6.0): 0.0303230590074, (8.0, 6.0): 0.0252621905251, (3.0, 5.0): 0.0404044049102, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00506205252405, (4.0, 10.0): 0.0019184683617, (2.0, 6.0): 0.0252621905251, (6.0, 6.0): 0.0303285986116, (7.0, 10.0): 0.00191882030608, (5.0, 11.0): 0.000801743519022, (4.0, 5.0): 0.0606165475992, (8.0, 11.0): 0.000964504831175, (6.0, 11.0): 0.000962736212839, (2.0, 7.0): 0.0151837392219, (7.0, 7.0): 0.015185566409, (3.0, 9.0): 0.00382125400722, (1.0, 9.0): 0.00127582462395, (8.0, 7.0): 0.0151837392219, (6.0, 12.0): 0.000241098890762, (6.0, 4.0): 0.0404048853564, (2.0, 10.0): 0.00159968532142, (6.0, 5.0): 0.0606165475992, (7.0, 9.0): 0.00382125400722, (6.0, 7.0): 0.0151875098505, (6.0, 9.0): 0.00382081942492, (5.0, 10.0): 0.00191814548284, (6.0, 8.0): 0.00761362611367, (8.0, 12.0): 0.000241591633299, (5.0, 7.0): 0.0151885226869, (2.0, 11.0): 0.000964504831175, (9.0, 9.0): 0.00127582462395, (2.0, 4.0): 0.0403734707108, (3.0, 8.0): 0.00761330557387, (8.0, 8.0): 0.00634420353629, (1.0, 7.0): 0.00506205252405, (9.0, 5.0): 0.0201867353554, (7.0, 8.0): 0.00761330557387, (7.0, 6.0): 0.0303230590074, (8.0, 4.0): 0.0403734707108, }, ]
+97 Stop [{(5.0, 9.0): 0.00381873161593, (4.0, 7.0): 0.0151862416309, (6.0, 10.0): 0.00191695433354, (4.0, 8.0): 0.00761164727648, (5.0, 6.0): 0.0303323898656, (2.0, 8.0): 0.00634257697825, (3.0, 11.0): 0.000801470355086, (4.0, 6.0): 0.0303292045792, (8.0, 9.0): 0.00382061721819, (4.0, 12.0): 0.000240822332394, (2.0, 12.0): 0.000241281349629, (7.0, 11.0): 0.000801470355086, (3.0, 7.0): 0.0151844142728, (2.0, 5.0): 0.0605819793437, (1.0, 11.0): 0.000321931534857, (8.0, 5.0): 0.0605819793437, (5.0, 8.0): 0.00761181523388, (7.0, 5.0): 0.0404074443696, (4.0, 9.0): 0.00381888214638, (5.0, 5.0): 0.0404208657252, (2.0, 9.0): 0.00382061721819, (3.0, 10.0): 0.00191728110359, (4.0, 4.0): 0.0404079585446, (8.0, 10.0): 0.00159835646154, (1.0, 5.0): 0.0201892665507, (9.0, 11.0): 0.000321931534857, (4.0, 11.0): 0.00096174219014, (3.0, 6.0): 0.0303240044054, (8.0, 6.0): 0.0252634265532, (3.0, 5.0): 0.0404074443696, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00506164946568, (4.0, 10.0): 0.00191695433354, (2.0, 6.0): 0.0252634265532, (6.0, 6.0): 0.0303292045792, (7.0, 10.0): 0.00191728110359, (5.0, 11.0): 0.000800951688558, (4.0, 5.0): 0.0606205170495, (8.0, 11.0): 0.000963389818078, (6.0, 11.0): 0.00096174219014, (2.0, 7.0): 0.01518269672, (7.0, 7.0): 0.0151844142728, (3.0, 9.0): 0.00381928433888, (1.0, 9.0): 0.00127502671449, (8.0, 7.0): 0.01518269672, (6.0, 12.0): 0.000240822332394, (6.0, 4.0): 0.0404079585446, (2.0, 10.0): 0.00159835646154, (6.0, 5.0): 0.0606205170495, (7.0, 9.0): 0.00381928433888, (6.0, 7.0): 0.0151862416309, (6.0, 9.0): 0.00381888214638, (5.0, 10.0): 0.00191665387682, (6.0, 8.0): 0.00761164727648, (8.0, 12.0): 0.000241281349629, (5.0, 7.0): 0.0151871919144, (2.0, 11.0): 0.000963389818078, (9.0, 9.0): 0.00127502671449, (2.0, 4.0): 0.0403785331014, (3.0, 8.0): 0.00761134172576, (8.0, 8.0): 0.00634257697825, (1.0, 7.0): 0.00506164946568, (9.0, 5.0): 0.0201892665507, (7.0, 8.0): 0.00761134172576, (7.0, 6.0): 0.0303240044054, (8.0, 4.0): 0.0403785331014, }, ]
+98 Stop [{(5.0, 9.0): 0.0038169364827, (4.0, 7.0): 0.015185057825, (6.0, 10.0): 0.0019155426401, (4.0, 8.0): 0.00760980169329, (5.0, 6.0): 0.0303327469068, (2.0, 8.0): 0.00634106087601, (3.0, 11.0): 0.000800696256319, (4.0, 6.0): 0.0303297665269, (8.0, 9.0): 0.00381868826369, (4.0, 12.0): 0.000240564475821, (2.0, 12.0): 0.000240992086223, (7.0, 11.0): 0.000800696256319, (3.0, 7.0): 0.0151833400801, (2.0, 5.0): 0.060588128097, (1.0, 11.0): 0.000321530737108, (8.0, 5.0): 0.060588128097, (5.0, 8.0): 0.00760996082217, (7.0, 5.0): 0.0404102786264, (4.0, 9.0): 0.00381707567412, (5.0, 5.0): 0.0404228520697, (2.0, 9.0): 0.00381868826369, (3.0, 10.0): 0.00191584606368, (4.0, 4.0): 0.0404108182888, (8.0, 10.0): 0.00159711761863, (1.0, 5.0): 0.0201916298326, (9.0, 11.0): 0.000321530737108, (4.0, 11.0): 0.000960815384271, (3.0, 6.0): 0.0303248859914, (8.0, 6.0): 0.0252645815088, (3.0, 5.0): 0.0404102786264, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00506127418618, (4.0, 10.0): 0.0019155426401, (2.0, 6.0): 0.0252645815088, (6.0, 6.0): 0.0303297665269, (7.0, 10.0): 0.00191584606368, (5.0, 11.0): 0.000800213380562, (4.0, 5.0): 0.060624211164, (8.0, 11.0): 0.000962350333519, (6.0, 11.0): 0.000960815384271, (2.0, 7.0): 0.0151817260003, (7.0, 7.0): 0.0151833400801, (3.0, 9.0): 0.0038174479394, (1.0, 9.0): 0.00127428289361, (8.0, 7.0): 0.0151817260003, (6.0, 12.0): 0.000240564475821, (6.0, 4.0): 0.0404108182888, (2.0, 10.0): 0.00159711761863, (6.0, 5.0): 0.060624211164, (7.0, 9.0): 0.0038174479394, (6.0, 7.0): 0.015185057825, (6.0, 9.0): 0.00381707567412, (5.0, 10.0): 0.00191526303539, (6.0, 8.0): 0.00760980169329, (8.0, 12.0): 0.000240992086223, (5.0, 7.0): 0.0151859492516, (2.0, 11.0): 0.000962350333519, (9.0, 9.0): 0.00127428289361, (2.0, 4.0): 0.0403832596653, (3.0, 8.0): 0.00760951072111, (8.0, 8.0): 0.00634106087601, (1.0, 7.0): 0.00506127418618, (9.0, 5.0): 0.0201916298326, (7.0, 8.0): 0.00760951072111, (7.0, 6.0): 0.0303248859914, (8.0, 4.0): 0.0403832596653, }, ]
+99 Stop [{(5.0, 9.0): 0.00381526245399, (4.0, 7.0): 0.015183952845, (6.0, 10.0): 0.00191422636364, (4.0, 8.0): 0.00760808040164, (5.0, 6.0): 0.0303330760947, (2.0, 8.0): 0.00633964771666, (3.0, 11.0): 0.000799974548175, (4.0, 6.0): 0.0303302877065, (8.0, 9.0): 0.00381689003664, (4.0, 12.0): 0.000240324055986, (2.0, 12.0): 0.000240722417661, (7.0, 11.0): 0.000799974548175, (3.0, 7.0): 0.0151823385565, (2.0, 5.0): 0.0605938683586, (1.0, 11.0): 0.000321157090807, (8.0, 5.0): 0.0605938683586, (5.0, 8.0): 0.00760823107096, (7.0, 5.0): 0.0404129215306, (4.0, 9.0): 0.00381539117829, (5.0, 5.0): 0.040424699041, (2.0, 9.0): 0.00381689003664, (3.0, 10.0): 0.00191450813345, (4.0, 4.0): 0.0404134795324, (8.0, 10.0): 0.00159596268946, (1.0, 5.0): 0.0201938362658, (9.0, 11.0): 0.000321157090807, (4.0, 11.0): 0.000959951248653, (3.0, 6.0): 0.0303257080715, (8.0, 6.0): 0.0252656606499, (3.0, 5.0): 0.0404129215306, (1, 1): 0.0151515151515, (9.0, 7.0): 0.0050609247598, (4.0, 10.0): 0.00191422636364, (2.0, 6.0): 0.0252656606499, (6.0, 6.0): 0.0303302877065, (7.0, 10.0): 0.00191450813345, (5.0, 11.0): 0.000799524976769, (4.0, 5.0): 0.0606276491881, (8.0, 11.0): 0.000961381256613, (6.0, 11.0): 0.000959951248653, (2.0, 7.0): 0.015180822092, (7.0, 7.0): 0.0151823385565, (3.0, 9.0): 0.00381573578761, (1.0, 9.0): 0.00127358949075, (8.0, 7.0): 0.015180822092, (6.0, 12.0): 0.000240324055986, (6.0, 4.0): 0.0404134795324, (2.0, 10.0): 0.00159596268946, (6.0, 5.0): 0.0606276491881, (7.0, 9.0): 0.00381573578761, (6.0, 7.0): 0.015183952845, (6.0, 9.0): 0.00381539117829, (5.0, 10.0): 0.00191396615194, (6.0, 8.0): 0.00760808040164, (8.0, 12.0): 0.000240722417661, (5.0, 7.0): 0.0151847889088, (2.0, 11.0): 0.000961381256613, (9.0, 9.0): 0.00127358949075, (2.0, 4.0): 0.0403876725317, (3.0, 8.0): 0.00760780357075, (8.0, 8.0): 0.00633964771666, (1.0, 7.0): 0.0050609247598, (9.0, 5.0): 0.0201938362658, (7.0, 8.0): 0.00760780357075, (7.0, 6.0): 0.0303257080715, (8.0, 4.0): 0.0403876725317, }, ]
+100 Stop [{(5.0, 9.0): 0.00381370136423, (4.0, 7.0): 0.01518292147, (6.0, 10.0): 0.0019129990541, (4.0, 8.0): 0.00760647504083, (5.0, 6.0): 0.0303333796478, (2.0, 8.0): 0.00633833049934, (3.0, 11.0): 0.000799301682754, (4.0, 6.0): 0.0303307711251, (8.0, 9.0): 0.00381521366854, (4.0, 12.0): 0.000240099893437, (2.0, 12.0): 0.000240471015322, (7.0, 11.0): 0.000799301682754, (3.0, 7.0): 0.0151814047844, (2.0, 5.0): 0.0605992271011, (1.0, 11.0): 0.000320808754839, (8.0, 5.0): 0.0605992271011, (5.0, 8.0): 0.00760661761517, (7.0, 5.0): 0.0404153859976, (4.0, 9.0): 0.0038138204248, (5.0, 5.0): 0.0404264165212, (2.0, 9.0): 0.00381521366854, (3.0, 10.0): 0.00191326073795, (4.0, 4.0): 0.0404159561622, (8.0, 10.0): 0.00159488598511, (1.0, 5.0): 0.0201958961927, (9.0, 11.0): 0.000320808754839, (4.0, 11.0): 0.000959145544361, (3.0, 6.0): 0.0303264746611, (8.0, 6.0): 0.025266668899, (3.0, 5.0): 0.0404153859976, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00506059939523, (4.0, 10.0): 0.0019129990541, (2.0, 6.0): 0.025266668899, (6.0, 6.0): 0.0303307711251, (7.0, 10.0): 0.00191326073795, (5.0, 11.0): 0.000798883103562, (4.0, 5.0): 0.0606308490093, (8.0, 11.0): 0.00096047781414, (6.0, 11.0): 0.000959145544361, (2.0, 7.0): 0.0151799803713, (7.0, 7.0): 0.0151814047844, (3.0, 9.0): 0.00381413947337, (1.0, 9.0): 0.00127294308482, (8.0, 7.0): 0.0151799803713, (6.0, 12.0): 0.000240099893437, (6.0, 4.0): 0.0404159561622, (2.0, 10.0): 0.00159488598511, (6.0, 5.0): 0.0606308490093, (7.0, 9.0): 0.00381413947337, (6.0, 7.0): 0.01518292147, (6.0, 9.0): 0.0038138204248, (5.0, 10.0): 0.00191275687958, (6.0, 8.0): 0.00760647504083, (8.0, 12.0): 0.000240471015322, (5.0, 7.0): 0.0151837054726, (2.0, 11.0): 0.00096047781414, (9.0, 9.0): 0.00127294308482, (2.0, 4.0): 0.0403917923854, (3.0, 8.0): 0.00760621189402, (8.0, 8.0): 0.00633833049934, (1.0, 7.0): 0.00506059939523, (9.0, 5.0): 0.0201958961927, (7.0, 8.0): 0.00760621189402, (7.0, 6.0): 0.0303264746611, (8.0, 4.0): 0.0403917923854, }, ]
+101 None [{(5.0, 9.0): 0.00381370136423, (4.0, 7.0): 0.01518292147, (6.0, 10.0): 0.0019129990541, (4.0, 8.0): 0.00760647504083, (5.0, 6.0): 0.0303333796478, (2.0, 8.0): 0.00633833049934, (3.0, 11.0): 0.000799301682754, (4.0, 6.0): 0.0303307711251, (8.0, 9.0): 0.00381521366854, (4.0, 12.0): 0.000240099893437, (2.0, 12.0): 0.000240471015322, (7.0, 11.0): 0.000799301682754, (3.0, 7.0): 0.0151814047844, (2.0, 5.0): 0.0605992271011, (1.0, 11.0): 0.000320808754839, (8.0, 5.0): 0.0605992271011, (5.0, 8.0): 0.00760661761517, (7.0, 5.0): 0.0404153859976, (4.0, 9.0): 0.0038138204248, (5.0, 5.0): 0.0404264165212, (2.0, 9.0): 0.00381521366854, (3.0, 10.0): 0.00191326073795, (4.0, 4.0): 0.0404159561622, (8.0, 10.0): 0.00159488598511, (1.0, 5.0): 0.0201958961927, (9.0, 11.0): 0.000320808754839, (4.0, 11.0): 0.000959145544361, (3.0, 6.0): 0.0303264746611, (8.0, 6.0): 0.025266668899, (3.0, 5.0): 0.0404153859976, (1, 1): 0.0151515151515, (9.0, 7.0): 0.00506059939523, (4.0, 10.0): 0.0019129990541, (2.0, 6.0): 0.025266668899, (6.0, 6.0): 0.0303307711251, (7.0, 10.0): 0.00191326073795, (5.0, 11.0): 0.000798883103562, (4.0, 5.0): 0.0606308490093, (8.0, 11.0): 0.00096047781414, (6.0, 11.0): 0.000959145544361, (2.0, 7.0): 0.0151799803713, (7.0, 7.0): 0.0151814047844, (3.0, 9.0): 0.00381413947337, (1.0, 9.0): 0.00127294308482, (8.0, 7.0): 0.0151799803713, (6.0, 12.0): 0.000240099893437, (6.0, 4.0): 0.0404159561622, (2.0, 10.0): 0.00159488598511, (6.0, 5.0): 0.0606308490093, (7.0, 9.0): 0.00381413947337, (6.0, 7.0): 0.01518292147, (6.0, 9.0): 0.0038138204248, (5.0, 10.0): 0.00191275687958, (6.0, 8.0): 0.00760647504083, (8.0, 12.0): 0.000240471015322, (5.0, 7.0): 0.0151837054726, (2.0, 11.0): 0.00096047781414, (9.0, 9.0): 0.00127294308482, (2.0, 4.0): 0.0403917923854, (3.0, 8.0): 0.00760621189402, (8.0, 8.0): 0.00633833049934, (1.0, 7.0): 0.00506059939523, (9.0, 5.0): 0.0201958961927, (7.0, 8.0): 0.00760621189402, (7.0, 6.0): 0.0303264746611, (8.0, 4.0): 0.0403917923854, }, ]
+"""

tracking/test_cases/q11/3-ParticlePredict.test

@@ -0,0 +1,34 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%%%%%%
+%% % % % %%
+%    G    %
+%%       %%
+%         %
+%%       %%
+%         %
+%%       %%
+%         %
+%% % % % %%
+%%%%%%%%%%%
+%%%%%%%%P%%
+% %%%%%%%%%
+%%%%%%%%%%%
+"""
+
+observe: "False"
+
+elapse: "True"
+
+checkUniform: "False"
+maxMoves: "100"
+numParticles: "5000"
+numGhosts: "1"
+inference: "ParticleFilter"
+ghost: "GoSouthAgent"
+L2Tolerance: "0.05"
+errorMsg: "Particle filter full test: %d inference errors."
+

tracking/test_cases/q11/4-ParticlePredict.solution

@@ -0,0 +1,155 @@
+# move_number action likelihood_dictionary
+correctActions: """
+0 None [{(16, 6): 0.00763358778626, (11, 11): 0.00763358778626, (17, 7): 0.00763358778626, (16, 9): 0.00763358778626, (14, 4): 0.00763358778626, (13, 4): 0.00763358778626, (20, 20): 0.00763358778626, (18, 4): 0.00763358778626, (18, 19): 0.00763358778626, (2, 5): 0.00763358778626, (8, 5): 0.00763358778626, (10, 8): 0.00763358778626, (11, 5): 0.00763358778626, (10, 7): 0.00763358778626, (16, 3): 0.00763358778626, (12, 6): 0.00763358778626, (16, 22): 0.00763358778626, (13, 7): 0.00763358778626, (18, 9): 0.00763358778626, (15, 4): 0.00763358778626, (18, 20): 0.00763358778626, (15, 18): 0.00763358778626, (9, 3): 0.00763358778626, (19, 24): 0.00763358778626, (12, 11): 0.00763358778626, (13, 10): 0.00763358778626, (18, 10): 0.00763358778626, (17, 18): 0.00763358778626, (9, 9): 0.00763358778626, (15, 15): 0.00763358778626, (11, 8): 0.00763358778626, (14, 8): 0.00763358778626, (21, 24): 0.00763358778626, (20, 16): 0.00763358778626, (17, 8): 0.00763358778626, (18, 15): 0.00763358778626, (8, 9): 0.00763358778626, (9, 4): 0.00763358778626, (10, 3): 0.00763358778626, (16, 7): 0.00763358778626, (11, 10): 0.00763358778626, (17, 6): 0.00763358778626, (16, 10): 0.00763358778626, (14, 5): 0.00763358778626, (13, 3): 0.00763358778626, (18, 5): 0.00763358778626, (17, 11): 0.00763358778626, (18, 16): 0.00763358778626, (8, 6): 0.00763358778626, (10, 9): 0.00763358778626, (9, 7): 0.00763358778626, (11, 4): 0.00763358778626, (10, 4): 0.00763358778626, (16, 4): 0.00763358778626, (12, 7): 0.00763358778626, (11, 9): 0.00763358778626, (15, 10): 0.00763358778626, (14, 6): 0.00763358778626, (13, 6): 0.00763358778626, (18, 6): 0.00763358778626, (17, 22): 0.00763358778626, (15, 7): 0.00763358778626, (18, 21): 0.00763358778626, (8, 3): 0.00763358778626, (10, 10): 0.00763358778626, (11, 3): 0.00763358778626, (12, 4): 0.00763358778626, (15, 9): 0.00763358778626, (14, 3): 0.00763358778626, (13, 9): 0.00763358778626, (12, 8): 0.00763358778626, (18, 11): 0.00763358778626, (18, 22): 0.00763358778626, (9, 8): 0.00763358778626, (14, 9): 0.00763358778626, (12, 9): 0.00763358778626, (17, 15): 0.00763358778626, (21, 20): 0.00763358778626, (18, 12): 0.00763358778626, (15, 11): 0.00763358778626, (8, 10): 0.00763358778626, (9, 11): 0.00763358778626, (12, 3): 0.00763358778626, (17, 5): 0.00763358778626, (16, 11): 0.00763358778626, (14, 10): 0.00763358778626, (17, 10): 0.00763358778626, (15, 3): 0.00763358778626, (18, 17): 0.00763358778626, (8, 7): 0.00763358778626, (9, 6): 0.00763358778626, (11, 7): 0.00763358778626, (10, 5): 0.00763358778626, (16, 5): 0.00763358778626, (20, 24): 0.00763358778626, (19, 16): 0.00763358778626, (16, 8): 0.00763358778626, (14, 7): 0.00763358778626, (13, 5): 0.00763358778626, (18, 7): 0.00763358778626, (15, 6): 0.00763358778626, (18, 18): 0.00763358778626, (8, 4): 0.00763358778626, (10, 11): 0.00763358778626, (13, 8): 0.00763358778626, (10, 6): 0.00763358778626, (15, 22): 0.00763358778626, (12, 5): 0.00763358778626, (17, 3): 0.00763358778626, (15, 8): 0.00763358778626, (21, 16): 0.00763358778626, (18, 8): 0.00763358778626, (15, 5): 0.00763358778626, (18, 23): 0.00763358778626, (16, 15): 0.00763358778626, (12, 10): 0.00763358778626, (16, 18): 0.00763358778626, (13, 11): 0.00763358778626, (18, 13): 0.00763358778626, (18, 24): 0.00763358778626, (8, 11): 0.00763358778626, (9, 10): 0.00763358778626, (19, 20): 0.00763358778626, (17, 4): 0.00763358778626, (14, 11): 0.00763358778626, (18, 3): 0.00763358778626, (17, 9): 0.00763358778626, (18, 14): 0.00763358778626, (8, 8): 0.00763358778626, (9, 5): 0.00763358778626, (11, 6): 0.00763358778626, }, ]
+1 Stop [{(16, 6): 0.00763358778626, (11, 11): 0.00763358778626, (17, 7): 0.00763358778626, (16, 9): 0.00763358778626, (14, 4): 0.00763358778626, (13, 4): 0.00763358778626, (20, 20): 0.00763358778626, (18, 4): 0.00763358778626, (18, 19): 0.00763358778626, (2, 5): 0.00763358778626, (8, 5): 0.00763358778626, (10, 8): 0.00763358778626, (11, 5): 0.00763358778626, (10, 7): 0.00763358778626, (16, 3): 0.00763358778626, (12, 6): 0.00763358778626, (16, 22): 0.00763358778626, (13, 7): 0.00763358778626, (18, 9): 0.00763358778626, (15, 4): 0.00763358778626, (18, 20): 0.00763358778626, (15, 18): 0.00763358778626, (9, 3): 0.00763358778626, (19, 24): 0.00763358778626, (12, 11): 0.00763358778626, (13, 10): 0.00763358778626, (18, 10): 0.00763358778626, (17, 18): 0.00763358778626, (9, 9): 0.00763358778626, (15, 15): 0.00763358778626, (11, 8): 0.00763358778626, (14, 8): 0.00763358778626, (21, 24): 0.00763358778626, (20, 16): 0.00763358778626, (17, 8): 0.00763358778626, (18, 15): 0.00763358778626, (8, 9): 0.00763358778626, (9, 4): 0.00763358778626, (10, 3): 0.00763358778626, (16, 7): 0.00763358778626, (11, 10): 0.00763358778626, (17, 6): 0.00763358778626, (16, 10): 0.00763358778626, (14, 5): 0.00763358778626, (13, 3): 0.00763358778626, (18, 5): 0.00763358778626, (17, 11): 0.00763358778626, (18, 16): 0.00763358778626, (8, 6): 0.00763358778626, (10, 9): 0.00763358778626, (9, 7): 0.00763358778626, (11, 4): 0.00763358778626, (10, 4): 0.00763358778626, (16, 4): 0.00763358778626, (12, 7): 0.00763358778626, (11, 9): 0.00763358778626, (15, 10): 0.00763358778626, (14, 6): 0.00763358778626, (13, 6): 0.00763358778626, (18, 6): 0.00763358778626, (17, 22): 0.00763358778626, (15, 7): 0.00763358778626, (18, 21): 0.00763358778626, (8, 3): 0.00763358778626, (10, 10): 0.00763358778626, (11, 3): 0.00763358778626, (12, 4): 0.00763358778626, (15, 9): 0.00763358778626, (14, 3): 0.00763358778626, (13, 9): 0.00763358778626, (12, 8): 0.00763358778626, (18, 11): 0.00763358778626, (18, 22): 0.00763358778626, (9, 8): 0.00763358778626, (14, 9): 0.00763358778626, (12, 9): 0.00763358778626, (17, 15): 0.00763358778626, (21, 20): 0.00763358778626, (18, 12): 0.00763358778626, (15, 11): 0.00763358778626, (8, 10): 0.00763358778626, (9, 11): 0.00763358778626, (12, 3): 0.00763358778626, (17, 5): 0.00763358778626, (16, 11): 0.00763358778626, (14, 10): 0.00763358778626, (17, 10): 0.00763358778626, (15, 3): 0.00763358778626, (18, 17): 0.00763358778626, (8, 7): 0.00763358778626, (9, 6): 0.00763358778626, (11, 7): 0.00763358778626, (10, 5): 0.00763358778626, (16, 5): 0.00763358778626, (20, 24): 0.00763358778626, (19, 16): 0.00763358778626, (16, 8): 0.00763358778626, (14, 7): 0.00763358778626, (13, 5): 0.00763358778626, (18, 7): 0.00763358778626, (15, 6): 0.00763358778626, (18, 18): 0.00763358778626, (8, 4): 0.00763358778626, (10, 11): 0.00763358778626, (13, 8): 0.00763358778626, (10, 6): 0.00763358778626, (15, 22): 0.00763358778626, (12, 5): 0.00763358778626, (17, 3): 0.00763358778626, (15, 8): 0.00763358778626, (21, 16): 0.00763358778626, (18, 8): 0.00763358778626, (15, 5): 0.00763358778626, (18, 23): 0.00763358778626, (16, 15): 0.00763358778626, (12, 10): 0.00763358778626, (16, 18): 0.00763358778626, (13, 11): 0.00763358778626, (18, 13): 0.00763358778626, (18, 24): 0.00763358778626, (8, 11): 0.00763358778626, (9, 10): 0.00763358778626, (19, 20): 0.00763358778626, (17, 4): 0.00763358778626, (14, 11): 0.00763358778626, (18, 3): 0.00763358778626, (17, 9): 0.00763358778626, (18, 14): 0.00763358778626, (8, 8): 0.00763358778626, (9, 5): 0.00763358778626, (11, 6): 0.00763358778626, }, ]
+2 Stop [{(16.0, 6.0): 0.00763358778626, (11.0, 11.0): 0.00725190839695, (17.0, 7.0): 0.00801526717557, (16.0, 9.0): 0.00763358778626, (14.0, 4.0): 0.00801526717557, (13.0, 4.0): 0.00801526717557, (20.0, 20.0): 0.0089058524173, (18.0, 4.0): 0.00788804071247, (18.0, 19.0): 0.00636132315522, (8.0, 5.0): 0.00725190839695, (10.0, 8.0): 0.00763358778626, (11.0, 5.0): 0.00763358778626, (10.0, 7.0): 0.00763358778626, (16.0, 3.0): 0.00725190839695, (12.0, 6.0): 0.00763358778626, (16.0, 22.0): 0.0089058524173, (13.0, 7.0): 0.00763358778626, (18.0, 9.0): 0.00725190839695, (15.0, 4.0): 0.00801526717557, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00636132315522, (9.0, 3.0): 0.00788804071247, (19.0, 24.0): 0.00763358778626, (12.0, 11.0): 0.00725190839695, (13.0, 10.0): 0.00801526717557, (18.0, 10.0): 0.00725190839695, (17.0, 18.0): 0.00699745547074, (9.0, 9.0): 0.00801526717557, (16.0, 18.0): 0.0089058524173, (11.0, 8.0): 0.00763358778626, (14.0, 8.0): 0.00763358778626, (21.0, 24.0): 0.00636132315522, (20.0, 16.0): 0.0089058524173, (17.0, 8.0): 0.00801526717557, (18.0, 15.0): 0.0089058524173, (8.0, 9.0): 0.00725190839695, (9.0, 4.0): 0.00839694656489, (10.0, 3.0): 0.00725190839695, (16.0, 7.0): 0.00763358778626, (11.0, 10.0): 0.00801526717557, (17.0, 6.0): 0.00801526717557, (16.0, 10.0): 0.00801526717557, (14.0, 5.0): 0.00763358778626, (13.0, 3.0): 0.00725190839695, (18.0, 5.0): 0.00725190839695, (17.0, 11.0): 0.00725190839695, (18.0, 16.0): 0.0089058524173, (8.0, 6.0): 0.00725190839695, (10.0, 9.0): 0.00763358778626, (9.0, 7.0): 0.00801526717557, (11.0, 4.0): 0.00801526717557, (10.0, 4.0): 0.00801526717557, (16.0, 4.0): 0.00801526717557, (12.0, 7.0): 0.00763358778626, (11.0, 9.0): 0.00763358778626, (15.0, 10.0): 0.00801526717557, (14.0, 6.0): 0.00763358778626, (13.0, 6.0): 0.00763358778626, (18.0, 6.0): 0.00725190839695, (17.0, 22.0): 0.00699745547074, (15.0, 7.0): 0.00763358778626, (18.0, 21.0): 0.00636132315522, (8.0, 3.0): 0.00636132315522, (10.0, 10.0): 0.00801526717557, (11.0, 3.0): 0.00725190839695, (12.0, 4.0): 0.00801526717557, (15.0, 9.0): 0.00763358778626, (14.0, 3.0): 0.00725190839695, (13.0, 9.0): 0.00763358778626, (12.0, 8.0): 0.00763358778626, (18.0, 11.0): 0.00826972010178, (18.0, 22.0): 0.00954198473282, (9.0, 8.0): 0.00801526717557, (14.0, 9.0): 0.00763358778626, (12.0, 9.0): 0.00763358778626, (17.0, 15.0): 0.00699745547074, (21.0, 20.0): 0.00636132315522, (18.0, 12.0): 0.00699745547074, (15.0, 11.0): 0.00725190839695, (8.0, 10.0): 0.00788804071247, (9.0, 11.0): 0.00788804071247, (12.0, 3.0): 0.00725190839695, (17.0, 5.0): 0.00801526717557, (16.0, 11.0): 0.00725190839695, (14.0, 10.0): 0.00801526717557, (17.0, 10.0): 0.00839694656489, (15.0, 3.0): 0.00725190839695, (18.0, 17.0): 0.00636132315522, (8.0, 7.0): 0.00725190839695, (9.0, 6.0): 0.00801526717557, (11.0, 7.0): 0.00763358778626, (10.0, 5.0): 0.00763358778626, (16.0, 5.0): 0.00763358778626, (20.0, 24.0): 0.0089058524173, (19.0, 16.0): 0.00699745547074, (16.0, 8.0): 0.00763358778626, (14.0, 7.0): 0.00763358778626, (13.0, 5.0): 0.00763358778626, (18.0, 20.0): 0.00954198473282, (18.0, 7.0): 0.00725190839695, (15.0, 6.0): 0.00763358778626, (18.0, 18.0): 0.00954198473282, (8.0, 4.0): 0.00788804071247, (10.0, 11.0): 0.00725190839695, (13.0, 8.0): 0.00763358778626, (10.0, 6.0): 0.00763358778626, (15.0, 22.0): 0.00636132315522, (12.0, 5.0): 0.00763358778626, (17.0, 3.0): 0.00788804071247, (15.0, 8.0): 0.00763358778626, (21.0, 16.0): 0.00636132315522, (18.0, 8.0): 0.00725190839695, (15.0, 5.0): 0.00763358778626, (18.0, 23.0): 0.00699745547074, (16.0, 15.0): 0.0089058524173, (12.0, 10.0): 0.00801526717557, (15.0, 15.0): 0.00636132315522, (13.0, 11.0): 0.00725190839695, (18.0, 13.0): 0.00763358778626, (18.0, 24.0): 0.00763358778626, (8.0, 11.0): 0.00636132315522, (9.0, 10.0): 0.00839694656489, (19.0, 20.0): 0.00699745547074, (17.0, 4.0): 0.00839694656489, (14.0, 11.0): 0.00725190839695, (18.0, 3.0): 0.00636132315522, (17.0, 9.0): 0.00801526717557, (18.0, 14.0): 0.00699745547074, (8.0, 8.0): 0.00725190839695, (9.0, 5.0): 0.00801526717557, (11.0, 6.0): 0.00763358778626, }, ]
+3 Stop [{(16.0, 6.0): 0.00770992366412, (11.0, 11.0): 0.00704198473282, (17.0, 7.0): 0.00814885496183, (16.0, 9.0): 0.00778625954198, (14.0, 4.0): 0.00814885496183, (13.0, 4.0): 0.00814885496183, (20.0, 20.0): 0.00848176420696, (18.0, 4.0): 0.00758481764207, (18.0, 19.0): 0.00689143341815, (8.0, 5.0): 0.0072010178117, (10.0, 8.0): 0.00770992366412, (11.0, 5.0): 0.00770992366412, (10.0, 7.0): 0.00770992366412, (16.0, 3.0): 0.0072010178117, (12.0, 6.0): 0.00763358778626, (16.0, 22.0): 0.00848176420696, (13.0, 7.0): 0.00763358778626, (18.0, 9.0): 0.00704198473282, (15.0, 4.0): 0.00814885496183, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00614927905004, (9.0, 3.0): 0.00758481764207, (19.0, 24.0): 0.00805767599661, (12.0, 11.0): 0.00704198473282, (13.0, 10.0): 0.00814885496183, (18.0, 10.0): 0.0073727735369, (17.0, 18.0): 0.00768659881255, (9.0, 9.0): 0.00822519083969, (16.0, 18.0): 0.00848176420696, (11.0, 8.0): 0.00763358778626, (14.0, 8.0): 0.00763358778626, (21.0, 24.0): 0.00614927905004, (20.0, 16.0): 0.00848176420696, (17.0, 8.0): 0.00814885496183, (18.0, 15.0): 0.00911789652248, (8.0, 9.0): 0.0072010178117, (9.0, 4.0): 0.00882951653944, (10.0, 3.0): 0.0072010178117, (16.0, 7.0): 0.00770992366412, (11.0, 10.0): 0.00814885496183, (17.0, 6.0): 0.00814885496183, (16.0, 10.0): 0.00822519083969, (14.0, 5.0): 0.00770992366412, (13.0, 3.0): 0.00704198473282, (18.0, 5.0): 0.0072010178117, (17.0, 11.0): 0.0073727735369, (18.0, 16.0): 0.0089058524173, (8.0, 6.0): 0.00704198473282, (10.0, 9.0): 0.00778625954198, (9.0, 7.0): 0.00814885496183, (11.0, 4.0): 0.00814885496183, (10.0, 4.0): 0.00822519083969, (16.0, 4.0): 0.00822519083969, (12.0, 7.0): 0.00763358778626, (11.0, 9.0): 0.00770992366412, (15.0, 10.0): 0.00814885496183, (14.0, 6.0): 0.00763358778626, (13.0, 6.0): 0.00763358778626, (18.0, 6.0): 0.00704198473282, (17.0, 22.0): 0.00768659881255, (15.0, 7.0): 0.00763358778626, (18.0, 21.0): 0.00689143341815, (8.0, 3.0): 0.00606446140797, (10.0, 10.0): 0.00822519083969, (11.0, 3.0): 0.00704198473282, (12.0, 4.0): 0.00814885496183, (15.0, 9.0): 0.00770992366412, (14.0, 3.0): 0.00704198473282, (13.0, 9.0): 0.00770992366412, (12.0, 8.0): 0.00763358778626, (18.0, 11.0): 0.00802586938083, (18.0, 22.0): 0.00917090754877, (9.0, 8.0): 0.00814885496183, (14.0, 9.0): 0.00770992366412, (12.0, 9.0): 0.00770992366412, (17.0, 15.0): 0.00752756573367, (21.0, 20.0): 0.00614927905004, (18.0, 12.0): 0.00694444444444, (15.0, 11.0): 0.00704198473282, (8.0, 10.0): 0.00758481764207, (9.0, 11.0): 0.00758481764207, (12.0, 3.0): 0.00704198473282, (17.0, 5.0): 0.00822519083969, (16.0, 11.0): 0.00704198473282, (14.0, 10.0): 0.00814885496183, (17.0, 10.0): 0.00851145038168, (15.0, 3.0): 0.00704198473282, (18.0, 17.0): 0.00673240033927, (8.0, 7.0): 0.00704198473282, (9.0, 6.0): 0.00814885496183, (11.0, 7.0): 0.00763358778626, (10.0, 5.0): 0.00778625954198, (16.0, 5.0): 0.00778625954198, (20.0, 24.0): 0.00869380831213, (19.0, 16.0): 0.00752756573367, (16.0, 8.0): 0.00770992366412, (14.0, 7.0): 0.00763358778626, (13.0, 5.0): 0.00770992366412, (18.0, 20.0): 0.0089588634436, (18.0, 7.0): 0.00704198473282, (15.0, 6.0): 0.00763358778626, (18.0, 18.0): 0.0089588634436, (8.0, 4.0): 0.00758481764207, (10.0, 11.0): 0.0072010178117, (13.0, 8.0): 0.00763358778626, (10.0, 6.0): 0.00770992366412, (15.0, 22.0): 0.00614927905004, (12.0, 5.0): 0.00770992366412, (17.0, 3.0): 0.00758481764207, (15.0, 8.0): 0.00763358778626, (21.0, 16.0): 0.00614927905004, (18.0, 8.0): 0.00704198473282, (15.0, 5.0): 0.00770992366412, (18.0, 23.0): 0.00726251060221, (16.0, 15.0): 0.00848176420696, (12.0, 10.0): 0.00814885496183, (15.0, 15.0): 0.00614927905004, (13.0, 11.0): 0.00704198473282, (18.0, 13.0): 0.00720949957591, (18.0, 24.0): 0.00742154368109, (8.0, 11.0): 0.00606446140797, (9.0, 10.0): 0.00882951653944, (19.0, 20.0): 0.00768659881255, (17.0, 4.0): 0.00882951653944, (14.0, 11.0): 0.00704198473282, (18.0, 3.0): 0.00606446140797, (17.0, 9.0): 0.00822519083969, (18.0, 14.0): 0.00710347752332, (8.0, 8.0): 0.00704198473282, (9.0, 5.0): 0.00822519083969, (11.0, 6.0): 0.00763358778626, }, ]
+4 Stop [{(16.0, 6.0): 0.00779770992366, (11.0, 11.0): 0.0069510178117, (17.0, 7.0): 0.00819179389313, (16.0, 9.0): 0.00793129770992, (14.0, 4.0): 0.00819179389313, (13.0, 4.0): 0.00819179389313, (20.0, 20.0): 0.00846409386486, (18.0, 4.0): 0.00748384930732, (18.0, 19.0): 0.00677657619452, (8.0, 5.0): 0.00710199321459, (10.0, 8.0): 0.00779770992366, (11.0, 5.0): 0.00779770992366, (10.0, 7.0): 0.00778244274809, (16.0, 3.0): 0.00710199321459, (12.0, 6.0): 0.00764885496183, (16.0, 22.0): 0.00846409386486, (13.0, 7.0): 0.00763358778626, (18.0, 9.0): 0.00700922391858, (15.0, 4.0): 0.0082070610687, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00590189426067, (9.0, 3.0): 0.00748384930732, (19.0, 24.0): 0.00805767599661, (12.0, 11.0): 0.00691125954198, (13.0, 10.0): 0.00819179389313, (18.0, 10.0): 0.00731244698897, (17.0, 18.0): 0.00762917020074, (9.0, 9.0): 0.00839821882952, (16.0, 18.0): 0.00846409386486, (11.0, 8.0): 0.0076641221374, (14.0, 8.0): 0.00764885496183, (21.0, 24.0): 0.00597257562906, (20.0, 16.0): 0.00841108283856, (17.0, 8.0): 0.0082070610687, (18.0, 15.0): 0.00938295165394, (8.0, 9.0): 0.00710199321459, (9.0, 4.0): 0.0088483884648, (10.0, 3.0): 0.00710199321459, (16.0, 7.0): 0.00778244274809, (11.0, 10.0): 0.0082070610687, (17.0, 6.0): 0.0082070610687, (16.0, 10.0): 0.00829484732824, (14.0, 5.0): 0.00778244274809, (13.0, 3.0): 0.00691125954198, (18.0, 5.0): 0.00710199321459, (17.0, 11.0): 0.00731244698897, (18.0, 16.0): 0.00925925925926, (8.0, 6.0): 0.0069510178117, (10.0, 9.0): 0.00793129770992, (9.0, 7.0): 0.00819179389313, (11.0, 4.0): 0.0082070610687, (10.0, 4.0): 0.00839821882952, (16.0, 4.0): 0.00839821882952, (12.0, 7.0): 0.00763358778626, (11.0, 9.0): 0.00779770992366, (15.0, 10.0): 0.0082070610687, (14.0, 6.0): 0.00764885496183, (13.0, 6.0): 0.00764885496183, (18.0, 6.0): 0.0069510178117, (17.0, 22.0): 0.00768218122703, (15.0, 7.0): 0.00764885496183, (18.0, 21.0): 0.00682958722081, (8.0, 3.0): 0.00581389595703, (10.0, 10.0): 0.00839821882952, (11.0, 3.0): 0.0069510178117, (12.0, 4.0): 0.00819179389313, (15.0, 9.0): 0.00779770992366, (14.0, 3.0): 0.00691125954198, (13.0, 9.0): 0.00778244274809, (12.0, 8.0): 0.00764885496183, (18.0, 11.0): 0.00800766892847, (18.0, 22.0): 0.0095729078315, (9.0, 8.0): 0.0082070610687, (14.0, 9.0): 0.00778244274809, (12.0, 9.0): 0.00778244274809, (17.0, 15.0): 0.00761591744416, (21.0, 20.0): 0.00590189426067, (18.0, 12.0): 0.00672444868533, (15.0, 11.0): 0.00691125954198, (8.0, 10.0): 0.00748384930732, (9.0, 11.0): 0.00748384930732, (12.0, 3.0): 0.00691125954198, (17.0, 5.0): 0.00839821882952, (16.0, 11.0): 0.00700922391858, (14.0, 10.0): 0.00819179389313, (17.0, 10.0): 0.00867875318066, (15.0, 3.0): 0.0069510178117, (18.0, 17.0): 0.00671031241165, (8.0, 7.0): 0.00691125954198, (9.0, 6.0): 0.0082070610687, (11.0, 7.0): 0.00764885496183, (10.0, 5.0): 0.00793129770992, (16.0, 5.0): 0.00793129770992, (20.0, 24.0): 0.00865846762793, (19.0, 16.0): 0.00756290641787, (16.0, 8.0): 0.00779770992366, (14.0, 7.0): 0.00763358778626, (13.0, 5.0): 0.00778244274809, (18.0, 20.0): 0.00939620441052, (18.0, 7.0): 0.00691125954198, (15.0, 6.0): 0.0076641221374, (18.0, 18.0): 0.00934319338422, (8.0, 4.0): 0.00748384930732, (10.0, 11.0): 0.00710199321459, (13.0, 8.0): 0.00764885496183, (10.0, 6.0): 0.00779770992366, (15.0, 22.0): 0.00590189426067, (12.0, 5.0): 0.00778244274809, (17.0, 3.0): 0.00748384930732, (15.0, 8.0): 0.0076641221374, (21.0, 16.0): 0.00590189426067, (18.0, 8.0): 0.00691125954198, (15.0, 5.0): 0.00779770992366, (18.0, 23.0): 0.00718741164829, (16.0, 15.0): 0.00841108283856, (12.0, 10.0): 0.00819179389313, (15.0, 15.0): 0.00590189426067, (13.0, 11.0): 0.00691125954198, (18.0, 13.0): 0.00708580718123, (18.0, 24.0): 0.00758057675997, (8.0, 11.0): 0.00581389595703, (9.0, 10.0): 0.0088483884648, (19.0, 20.0): 0.00762917020074, (17.0, 4.0): 0.0088483884648, (14.0, 11.0): 0.00691125954198, (18.0, 3.0): 0.00581389595703, (17.0, 9.0): 0.00829484732824, (18.0, 14.0): 0.00705046649703, (8.0, 8.0): 0.0069510178117, (9.0, 5.0): 0.00839821882952, (11.0, 6.0): 0.0076641221374, }, ]
+5 Stop [{(16.0, 6.0): 0.00787652671756, (11.0, 11.0): 0.00688247985581, (17.0, 7.0): 0.00820548664122, (16.0, 9.0): 0.00802328244275, (14.0, 4.0): 0.00820243320611, (13.0, 4.0): 0.00819937977099, (20.0, 20.0): 0.00831536848553, (18.0, 4.0): 0.00735410364245, (18.0, 19.0): 0.00694370818019, (8.0, 5.0): 0.00706385884931, (10.0, 8.0): 0.00787652671756, (11.0, 5.0): 0.00787652671756, (10.0, 7.0): 0.00784370229008, (16.0, 3.0): 0.00706385884931, (12.0, 6.0): 0.00767557251908, (16.0, 22.0): 0.00833303882763, (13.0, 7.0): 0.00763969465649, (18.0, 9.0): 0.00696720207803, (15.0, 4.0): 0.00825671119593, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00577231175196, (9.0, 3.0): 0.00735410364245, (19.0, 24.0): 0.00809890679484, (12.0, 11.0): 0.00683174300254, (13.0, 10.0): 0.00819937977099, (18.0, 10.0): 0.00731808559514, (17.0, 18.0): 0.00770021970125, (9.0, 9.0): 0.00845249151824, (16.0, 18.0): 0.00831536848553, (11.0, 8.0): 0.00771145038168, (14.0, 8.0): 0.00767557251908, (21.0, 24.0): 0.00587244369051, (20.0, 16.0): 0.00827561021581, (17.0, 8.0): 0.00822609732824, (18.0, 15.0): 0.00954934737537, (8.0, 9.0): 0.00706385884931, (9.0, 4.0): 0.00887088987843, (10.0, 3.0): 0.00706385884931, (16.0, 7.0): 0.00784370229008, (11.0, 10.0): 0.00825671119593, (17.0, 6.0): 0.00825671119593, (16.0, 10.0): 0.00837469783715, (14.0, 5.0): 0.00784064885496, (13.0, 3.0): 0.00682180343511, (18.0, 5.0): 0.00706385884931, (17.0, 11.0): 0.00731808559514, (18.0, 16.0): 0.00941829233814, (8.0, 6.0): 0.00688247985581, (10.0, 9.0): 0.00806463104326, (9.0, 7.0): 0.00820548664122, (11.0, 4.0): 0.00825671119593, (10.0, 4.0): 0.00845249151824, (16.0, 4.0): 0.00845249151824, (12.0, 7.0): 0.0076427480916, (11.0, 9.0): 0.00787652671756, (15.0, 10.0): 0.00822609732824, (14.0, 6.0): 0.00767557251908, (13.0, 6.0): 0.00767251908397, (18.0, 6.0): 0.00688247985581, (17.0, 22.0): 0.00777531865517, (15.0, 7.0): 0.0076786259542, (18.0, 21.0): 0.00701880713411, (8.0, 3.0): 0.00567988997267, (10.0, 10.0): 0.00845249151824, (11.0, 3.0): 0.00688247985581, (12.0, 4.0): 0.00820243320611, (15.0, 9.0): 0.00787652671756, (14.0, 3.0): 0.00683174300254, (13.0, 9.0): 0.00783759541985, (12.0, 8.0): 0.00767557251908, (18.0, 11.0): 0.00789962362171, (18.0, 22.0): 0.00962628698992, (9.0, 8.0): 0.00825671119593, (14.0, 9.0): 0.00784064885496, (12.0, 9.0): 0.00784064885496, (17.0, 15.0): 0.00768807134106, (21.0, 20.0): 0.00577231175196, (18.0, 12.0): 0.0066053358543, (15.0, 11.0): 0.00684934796438, (8.0, 10.0): 0.00735410364245, (9.0, 11.0): 0.00735410364245, (12.0, 3.0): 0.00683174300254, (17.0, 5.0): 0.00845249151824, (16.0, 11.0): 0.00696720207803, (14.0, 10.0): 0.00820243320611, (17.0, 10.0): 0.00870991306192, (15.0, 3.0): 0.00688247985581, (18.0, 17.0): 0.00688738396475, (8.0, 7.0): 0.00684168256997, (9.0, 6.0): 0.00825671119593, (11.0, 7.0): 0.0076786259542, (10.0, 5.0): 0.00806463104326, (16.0, 5.0): 0.00806463104326, (20.0, 24.0): 0.00855833568938, (19.0, 16.0): 0.00763947790029, (16.0, 8.0): 0.00787652671756, (14.0, 7.0): 0.0076427480916, (13.0, 5.0): 0.00783759541985, (18.0, 20.0): 0.00942749564132, (18.0, 7.0): 0.00683174300254, (15.0, 6.0): 0.00771145038168, (18.0, 18.0): 0.00937448461502, (8.0, 4.0): 0.00735410364245, (10.0, 11.0): 0.00706385884931, (13.0, 8.0): 0.00767251908397, (10.0, 6.0): 0.00787652671756, (15.0, 22.0): 0.00577231175196, (12.0, 5.0): 0.00784064885496, (17.0, 3.0): 0.00735410364245, (15.0, 8.0): 0.00771145038168, (21.0, 16.0): 0.00575464140986, (18.0, 8.0): 0.00684934796438, (15.0, 5.0): 0.00787652671756, (18.0, 23.0): 0.00731588976063, (16.0, 15.0): 0.00829328055791, (12.0, 10.0): 0.00820243320611, (15.0, 15.0): 0.00575464140986, (13.0, 11.0): 0.00682180343511, (18.0, 13.0): 0.00695357412119, (18.0, 24.0): 0.00760855480162, (8.0, 11.0): 0.00567988997267, (9.0, 10.0): 0.00887088987843, (19.0, 20.0): 0.00771347245783, (17.0, 4.0): 0.00887088987843, (14.0, 11.0): 0.00682180343511, (18.0, 3.0): 0.00567988997267, (17.0, 9.0): 0.00837469783715, (18.0, 14.0): 0.00705782913957, (8.0, 8.0): 0.00688247985581, (9.0, 5.0): 0.00845249151824, (11.0, 6.0): 0.00771145038168, }, ]
+6 Stop [{(16.0, 6.0): 0.0079506043257, (11.0, 11.0): 0.0068458626661, (17.0, 7.0): 0.00821433524173, (16.0, 9.0): 0.00810514631043, (14.0, 4.0): 0.00820777035623, (13.0, 4.0): 0.00819381917939, (20.0, 20.0): 0.0082291028571, (18.0, 4.0): 0.00727196525618, (18.0, 19.0): 0.00701506445748, (8.0, 5.0): 0.00701560889054, (10.0, 8.0): 0.0079506043257, (11.0, 5.0): 0.00794999363868, (10.0, 7.0): 0.00789617366412, (16.0, 3.0): 0.00701560889054, (12.0, 6.0): 0.00770858778626, (16.0, 22.0): 0.00825560837024, (13.0, 7.0): 0.00765404580153, (18.0, 9.0): 0.00695859847682, (15.0, 4.0): 0.00827825249152, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00565794537116, (9.0, 3.0): 0.00727196525618, (19.0, 24.0): 0.00808859909528, (12.0, 11.0): 0.00677449321459, (13.0, 10.0): 0.00819381917939, (18.0, 10.0): 0.0072882104361, (17.0, 18.0): 0.00768215054935, (9.0, 9.0): 0.0084949094395, (16.0, 18.0): 0.00822468527157, (11.0, 8.0): 0.00776374045802, (14.0, 8.0): 0.00770858778626, (21.0, 24.0): 0.00578900040838, (20.0, 16.0): 0.00818235007696, (17.0, 8.0): 0.00824889869593, (18.0, 15.0): 0.00965721008859, (8.0, 9.0): 0.00701560889054, (9.0, 4.0): 0.0088322264042, (10.0, 3.0): 0.00701560889054, (16.0, 7.0): 0.00789617366412, (11.0, 10.0): 0.00827825249152, (17.0, 6.0): 0.00827886317854, (16.0, 10.0): 0.00840859865352, (14.0, 5.0): 0.00788655534351, (13.0, 3.0): 0.00676119831425, (18.0, 5.0): 0.00701560889054, (17.0, 11.0): 0.0072882104361, (18.0, 16.0): 0.00958419721672, (8.0, 6.0): 0.00684834755796, (10.0, 9.0): 0.00814453350297, (9.0, 7.0): 0.00822294290712, (11.0, 4.0): 0.00827825249152, (10.0, 4.0): 0.0084949094395, (16.0, 4.0): 0.0084949094395, (12.0, 7.0): 0.00766244274809, (11.0, 9.0): 0.00794999363868, (15.0, 10.0): 0.00824828800891, (14.0, 6.0): 0.00770858778626, (13.0, 6.0): 0.00770019083969, (18.0, 6.0): 0.0068458626661, (17.0, 22.0): 0.00777602424175, (15.0, 7.0): 0.00771759541985, (18.0, 21.0): 0.00710304803584, (8.0, 3.0): 0.00557034847878, (10.0, 10.0): 0.0084949094395, (11.0, 3.0): 0.0068458626661, (12.0, 4.0): 0.00820777035623, (15.0, 9.0): 0.00793560114504, (14.0, 3.0): 0.00677449321459, (13.0, 9.0): 0.00787815839695, (12.0, 8.0): 0.00770858778626, (18.0, 11.0): 0.0078357273211, (18.0, 22.0): 0.00977657693078, (9.0, 8.0): 0.00827886317854, (14.0, 9.0): 0.00788655534351, (12.0, 9.0): 0.00788655534351, (17.0, 15.0): 0.0077144541435, (21.0, 20.0): 0.00565794537116, (18.0, 12.0): 0.00649454256393, (15.0, 11.0): 0.00680480783503, (8.0, 10.0): 0.00727196525618, (9.0, 11.0): 0.00727196525618, (12.0, 3.0): 0.00677449321459, (17.0, 5.0): 0.0084949094395, (16.0, 11.0): 0.00695859847682, (14.0, 10.0): 0.00819916269084, (17.0, 10.0): 0.00875090454481, (15.0, 3.0): 0.0068458626661, (18.0, 17.0): 0.00699398889321, (8.0, 7.0): 0.00679275789864, (9.0, 6.0): 0.00827886317854, (11.0, 7.0): 0.00771759541985, (10.0, 5.0): 0.00814453350297, (16.0, 5.0): 0.00814453350297, (20.0, 24.0): 0.00848863600666, (19.0, 16.0): 0.00765960245657, (16.0, 8.0): 0.00793621183206, (14.0, 7.0): 0.00766244274809, (13.0, 5.0): 0.00787815839695, (18.0, 20.0): 0.0095822031677, (18.0, 7.0): 0.00678199003393, (15.0, 6.0): 0.00776374045802, (18.0, 18.0): 0.00952072510249, (8.0, 4.0): 0.00727196525618, (10.0, 11.0): 0.00701560889054, (13.0, 8.0): 0.00770019083969, (10.0, 6.0): 0.0079506043257, (15.0, 22.0): 0.00566383548519, (12.0, 5.0): 0.00788655534351, (17.0, 3.0): 0.00727196525618, (15.0, 8.0): 0.00776374045802, (21.0, 16.0): 0.00563585744353, (18.0, 8.0): 0.00680729272689, (15.0, 5.0): 0.00794999363868, (18.0, 23.0): 0.00738138660156, (16.0, 15.0): 0.00820443800459, (12.0, 10.0): 0.00820777035623, (15.0, 15.0): 0.00564174755757, (13.0, 11.0): 0.00675871342239, (18.0, 13.0): 0.00687224637169, (18.0, 24.0): 0.00767445045236, (8.0, 11.0): 0.00557034847878, (9.0, 10.0): 0.0088322264042, (19.0, 20.0): 0.00769982089145, (17.0, 4.0): 0.0088322264042, (14.0, 11.0): 0.00676372534987, (18.0, 3.0): 0.00557034847878, (17.0, 9.0): 0.00840859865352, (18.0, 14.0): 0.00705780459743, (8.0, 8.0): 0.00684834755796, (9.0, 5.0): 0.0084949094395, (11.0, 6.0): 0.00776374045802, }, ]
+7 Stop [{(16.0, 6.0): 0.00800678302587, (11.0, 11.0): 0.00681464169111, (17.0, 7.0): 0.00822315166455, (16.0, 9.0): 0.00815883131891, (14.0, 4.0): 0.00820690277778, (13.0, 4.0): 0.00818780323632, (20.0, 20.0): 0.00813861393509, (18.0, 4.0): 0.00719512164378, (18.0, 19.0): 0.00711408688671, (8.0, 5.0): 0.00698296231407, (10.0, 8.0): 0.00800678302587, (11.0, 5.0): 0.00800461508694, (10.0, 7.0): 0.0079475841285, (16.0, 3.0): 0.0069823410911, (12.0, 6.0): 0.00774430343511, (16.0, 22.0): 0.00817579527992, (13.0, 7.0): 0.00767586259542, (18.0, 9.0): 0.00694524514066, (15.0, 4.0): 0.00829765085171, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00557053444277, (9.0, 3.0): 0.00719512164378, (19.0, 24.0): 0.00808389518477, (12.0, 11.0): 0.00673632139702, (13.0, 10.0): 0.00818546048028, (18.0, 10.0): 0.00727081496747, (17.0, 18.0): 0.0076824598826, (9.0, 9.0): 0.00850400872768, (16.0, 18.0): 0.00813125129255, (11.0, 8.0): 0.0078181043257, (14.0, 8.0): 0.00774430343511, (21.0, 24.0): 0.00572404553974, (20.0, 16.0): 0.00809857956628, (17.0, 8.0): 0.00826343206637, (18.0, 15.0): 0.0097344380733, (8.0, 9.0): 0.00698296231407, (9.0, 4.0): 0.00880039168473, (10.0, 3.0): 0.0069823410911, (16.0, 7.0): 0.00794298409669, (11.0, 10.0): 0.00829765085171, (17.0, 6.0): 0.00829920810362, (16.0, 10.0): 0.00844223712274, (14.0, 5.0): 0.00792621310433, (13.0, 3.0): 0.00671631002173, (18.0, 5.0): 0.0069823410911, (17.0, 11.0): 0.00727081496747, (18.0, 16.0): 0.00969488227625, (8.0, 6.0): 0.00681995122249, (10.0, 9.0): 0.00820699006927, (9.0, 7.0): 0.00823355806033, (11.0, 4.0): 0.00829765085171, (10.0, 4.0): 0.00850388659027, (16.0, 4.0): 0.00850388659027, (12.0, 7.0): 0.0076902519084, (11.0, 9.0): 0.00800461508694, (15.0, 10.0): 0.00825953205842, (14.0, 6.0): 0.00774430343511, (13.0, 6.0): 0.00772991412214, (18.0, 6.0): 0.00681663803335, (17.0, 22.0): 0.00778802177003, (15.0, 7.0): 0.00776073854962, (18.0, 21.0): 0.00720737770324, (8.0, 3.0): 0.00549276545435, (10.0, 10.0): 0.00850388659027, (11.0, 3.0): 0.00681464169111, (12.0, 4.0): 0.00820690277778, (15.0, 9.0): 0.00798786625318, (14.0, 3.0): 0.00673694261998, (13.0, 9.0): 0.00790905582061, (12.0, 8.0): 0.00774430343511, (18.0, 11.0): 0.00776788456964, (18.0, 22.0): 0.00986429719241, (9.0, 8.0): 0.00830155085966, (14.0, 9.0): 0.00792161307252, (12.0, 9.0): 0.00792621310433, (17.0, 15.0): 0.00772059990484, (21.0, 20.0): 0.00557200697128, (18.0, 12.0): 0.00641452814215, (15.0, 11.0): 0.00678144051721, (8.0, 10.0): 0.00719512164378, (9.0, 11.0): 0.00719512164378, (12.0, 3.0): 0.00673694261998, (17.0, 5.0): 0.00850400872768, (16.0, 11.0): 0.00694462391769, (14.0, 10.0): 0.008196496382, (17.0, 10.0): 0.00875772558842, (15.0, 3.0): 0.00681464169111, (18.0, 17.0): 0.00710756021087, (8.0, 7.0): 0.00676695183507, (9.0, 6.0): 0.00830155085966, (11.0, 7.0): 0.00776073854962, (10.0, 5.0): 0.00820699006927, (16.0, 5.0): 0.00820699006927, (20.0, 24.0): 0.00842024523817, (19.0, 16.0): 0.00767670014869, (16.0, 8.0): 0.00799003419211, (14.0, 7.0): 0.0076902519084, (13.0, 5.0): 0.00790905582061, (18.0, 20.0): 0.00966819525352, (18.0, 7.0): 0.00675165340507, (15.0, 6.0): 0.0078181043257, (18.0, 18.0): 0.00961058257564, (8.0, 4.0): 0.00719512164378, (10.0, 11.0): 0.0069823410911, (13.0, 8.0): 0.00772991412214, (10.0, 6.0): 0.00800678302587, (15.0, 22.0): 0.00558378719934, (12.0, 5.0): 0.00792621310433, (17.0, 3.0): 0.00719512164378, (15.0, 8.0): 0.00781234732824, (21.0, 16.0): 0.00554537874742, (18.0, 8.0): 0.00678675004859, (15.0, 5.0): 0.00800461508694, (18.0, 23.0): 0.007462756584, (16.0, 15.0): 0.00812717116148, (12.0, 10.0): 0.00820690277778, (15.0, 15.0): 0.00555568644698, (13.0, 11.0): 0.00671299683259, (18.0, 13.0): 0.00680819784435, (18.0, 24.0): 0.00771481204973, (8.0, 11.0): 0.00549276545435, (9.0, 10.0): 0.00880039168473, (19.0, 20.0): 0.00770519204144, (17.0, 4.0): 0.00880039168473, (14.0, 11.0): 0.00672164418999, (18.0, 3.0): 0.00549276545435, (17.0, 9.0): 0.00844235926014, (18.0, 14.0): 0.00705765284519, (8.0, 8.0): 0.00681995122249, (9.0, 5.0): 0.00850400872768, (11.0, 6.0): 0.0078181043257, }, ]
+8 Stop [{(16.0, 6.0): 0.00805481392423, (11.0, 11.0): 0.00679285621515, (17.0, 7.0): 0.00823366853751, (16.0, 9.0): 0.00820426562942, (14.0, 4.0): 0.00820794964902, (13.0, 4.0): 0.00818121042793, (20.0, 20.0): 0.00806727214448, (18.0, 4.0): 0.00713536583878, (18.0, 19.0): 0.00719105675286, (8.0, 5.0): 0.00695031054062, (10.0, 8.0): 0.0080562024818, (11.0, 5.0): 0.00805071468759, (10.0, 7.0): 0.00799108935804, (16.0, 3.0): 0.00694880342455, (12.0, 6.0): 0.00778175737913, (16.0, 22.0): 0.00811316594965, (13.0, 7.0): 0.0077032389313, (18.0, 9.0): 0.00693917439121, (15.0, 4.0): 0.00830627148412, (1, 1): 0.00763358778626, (15.0, 18.0): 0.0054956843189, (9.0, 3.0): 0.00713536583878, (19.0, 24.0): 0.00807298415756, (12.0, 11.0): 0.00670737053574, (13.0, 10.0): 0.00817783230028, (18.0, 10.0): 0.00724753128712, (17.0, 18.0): 0.00767388270229, (9.0, 9.0): 0.00850832884678, (16.0, 18.0): 0.00805650427977, (11.0, 8.0): 0.00786690888465, (14.0, 8.0): 0.00777968597328, (21.0, 24.0): 0.0056687711826, (20.0, 16.0): 0.00803111594537, (17.0, 8.0): 0.00828048294878, (18.0, 15.0): 0.0097834143374, (8.0, 9.0): 0.00695031054062, (9.0, 4.0): 0.00875921822243, (10.0, 3.0): 0.00694880342455, (16.0, 7.0): 0.00798473830577, (11.0, 10.0): 0.00830627148412, (17.0, 6.0): 0.00831078981268, (16.0, 10.0): 0.00845982119712, (14.0, 5.0): 0.00795821804495, (13.0, 3.0): 0.00668510946269, (18.0, 5.0): 0.00694932693759, (17.0, 11.0): 0.00724737598138, (18.0, 16.0): 0.00978541687391, (8.0, 6.0): 0.00680277651484, (10.0, 9.0): 0.0082452567, (9.0, 7.0): 0.0082485867404, (11.0, 4.0): 0.00830627148412, (10.0, 4.0): 0.00850736911197, (16.0, 4.0): 0.00850736911197, (12.0, 7.0): 0.00772309198473, (11.0, 9.0): 0.00805071468759, (15.0, 10.0): 0.00827258649257, (14.0, 6.0): 0.00778175737913, (13.0, 6.0): 0.00776068788168, (18.0, 6.0): 0.00679749975311, (17.0, 22.0): 0.00778734664809, (15.0, 7.0): 0.00780488524173, (18.0, 21.0): 0.00728558234589, (8.0, 3.0): 0.00542848264001, (10.0, 10.0): 0.00850736911197, (11.0, 3.0): 0.00679301152089, (12.0, 4.0): 0.00820794964902, (15.0, 9.0): 0.00802803800626, (14.0, 3.0): 0.00670835413876, (13.0, 9.0): 0.00793445131997, (12.0, 8.0): 0.00778175737913, (18.0, 11.0): 0.00771555467352, (18.0, 22.0): 0.00995212631719, (9.0, 8.0): 0.00831416794033, (14.0, 9.0): 0.00795186699268, (12.0, 9.0): 0.00795821804495, (17.0, 15.0): 0.00771619987377, (21.0, 20.0): 0.00549887479734, (18.0, 12.0): 0.00634954647124, (15.0, 11.0): 0.00676383356791, (8.0, 10.0): 0.00713552114452, (9.0, 11.0): 0.00713536583878, (12.0, 3.0): 0.00670835413876, (17.0, 5.0): 0.00850770498983, (16.0, 11.0): 0.00693766727514, (14.0, 10.0): 0.00819303144614, (17.0, 10.0): 0.00876387187799, (15.0, 3.0): 0.00679301152089, (18.0, 17.0): 0.00719555294993, (8.0, 7.0): 0.00674842518208, (9.0, 6.0): 0.00831416794033, (11.0, 7.0): 0.00780695664758, (10.0, 5.0): 0.0082452567, (16.0, 5.0): 0.0082452567, (20.0, 24.0): 0.00836340291085, (19.0, 16.0): 0.00768214714072, (16.0, 8.0): 0.00803352580047, (14.0, 7.0): 0.00772309198473, (13.0, 5.0): 0.00793583987754, (18.0, 20.0): 0.00975926769051, (18.0, 7.0): 0.00673339070466, (15.0, 6.0): 0.00786690888465, (18.0, 18.0): 0.0097040146373, (8.0, 4.0): 0.00713552114452, (10.0, 11.0): 0.00694880342455, (13.0, 8.0): 0.00776068788168, (10.0, 6.0): 0.0080562024818, (15.0, 22.0): 0.00551715869298, (12.0, 5.0): 0.00795821804495, (17.0, 3.0): 0.00713536583878, (15.0, 8.0): 0.00785905795165, (21.0, 16.0): 0.0054722158958, (18.0, 8.0): 0.00677359856186, (15.0, 5.0): 0.00805071468759, (18.0, 23.0): 0.00752526384268, (16.0, 15.0): 0.00806043357893, (12.0, 10.0): 0.00820732579207, (15.0, 15.0): 0.00548690027732, (13.0, 11.0): 0.00667983270096, (18.0, 13.0): 0.00676012627723, (18.0, 24.0): 0.00775382127283, (8.0, 11.0): 0.00542848264001, (9.0, 10.0): 0.00875921822243, (19.0, 20.0): 0.00769831747222, (17.0, 4.0): 0.00875921822243, (14.0, 11.0): 0.00669331966135, (18.0, 3.0): 0.00542848264001, (17.0, 9.0): 0.00846078093193, (18.0, 14.0): 0.00705555974817, (8.0, 8.0): 0.00680277651484, (9.0, 5.0): 0.00850832884678, (11.0, 6.0): 0.00786690888465, }, ]
+9 Stop [{(16.0, 6.0): 0.00809250152547, (11.0, 11.0): 0.00677351184068, (17.0, 7.0): 0.00824528359711, (16.0, 9.0): 0.00823728631304, (14.0, 4.0): 0.0082078184559, (13.0, 4.0): 0.00817786728638, (20.0, 20.0): 0.00800463393757, (18.0, 4.0): 0.00708251105192, (18.0, 19.0): 0.00726283949957, (8.0, 5.0): 0.00692381781935, (10.0, 8.0): 0.00809472507296, (11.0, 5.0): 0.00808547396026, (10.0, 7.0): 0.00803180754192, (16.0, 3.0): 0.00692076901845, (12.0, 6.0): 0.00781813283503, (16.0, 22.0): 0.0080587502124, (13.0, 7.0): 0.00773415973282, (18.0, 9.0): 0.00693223224643, (15.0, 4.0): 0.00831271386676, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00543334358604, (9.0, 3.0): 0.00708238017366, (19.0, 24.0): 0.00806340278041, (12.0, 11.0): 0.00668648002137, (13.0, 10.0): 0.00817248634693, (18.0, 10.0): 0.00722833946356, (17.0, 18.0): 0.00766946598668, (9.0, 9.0): 0.00850297197706, (16.0, 18.0): 0.0079913044868, (11.0, 8.0): 0.00791250801615, (14.0, 8.0): 0.00781487815681, (21.0, 24.0): 0.00562218656158, (20.0, 16.0): 0.00797386230993, (17.0, 8.0): 0.0082950912842, (18.0, 15.0): 0.00981612767681, (8.0, 9.0): 0.00692381781935, (9.0, 4.0): 0.00872270498206, (10.0, 3.0): 0.00692076901845, (16.0, 7.0): 0.00802232636194, (11.0, 10.0): 0.00831255026894, (17.0, 6.0): 0.00832077039113, (16.0, 10.0): 0.00847452585821, (14.0, 5.0): 0.00798689592765, (13.0, 3.0): 0.00666169652064, (18.0, 5.0): 0.00692208913034, (17.0, 11.0): 0.00722792385811, (18.0, 16.0): 0.00985144116638, (8.0, 6.0): 0.00678821164745, (10.0, 9.0): 0.00827357436563, (9.0, 7.0): 0.00826070869134, (11.0, 4.0): 0.00831271386676, (10.0, 4.0): 0.00850082395984, (16.0, 4.0): 0.00850082395984, (12.0, 7.0): 0.00775936046438, (11.0, 9.0): 0.00808547396026, (15.0, 10.0): 0.00828165382039, (14.0, 6.0): 0.00781813283503, (13.0, 6.0): 0.00779265628976, (18.0, 6.0): 0.00678221231138, (17.0, 22.0): 0.00778820244521, (15.0, 7.0): 0.00784773647371, (18.0, 21.0): 0.00735637595056, (8.0, 3.0): 0.00537721595916, (10.0, 10.0): 0.00850082395984, (11.0, 3.0): 0.00677379656788, (12.0, 4.0): 0.0082078184559, (15.0, 9.0): 0.00806316301452, (14.0, 3.0): 0.00668820871039, (13.0, 9.0): 0.00795661130791, (12.0, 8.0): 0.00781813283503, (18.0, 11.0): 0.00766913097592, (18.0, 22.0): 0.0100207625249, (9.0, 8.0): 0.00832615133057, (14.0, 9.0): 0.00797741474767, (12.0, 9.0): 0.00798649344474, (17.0, 15.0): 0.00770473140191, (21.0, 20.0): 0.0054385281135, (18.0, 12.0): 0.00629877958454, (15.0, 11.0): 0.00675322242461, (8.0, 10.0): 0.00708279577911, (9.0, 11.0): 0.00708238017366, (12.0, 3.0): 0.00668820871039, (17.0, 5.0): 0.00850192567939, (16.0, 11.0): 0.00692918344553, (14.0, 10.0): 0.00819239336167, (17.0, 10.0): 0.00876062161854, (15.0, 3.0): 0.00677379656788, (18.0, 17.0): 0.00727087552778, (8.0, 7.0): 0.00673821190102, (9.0, 6.0): 0.00832615133057, (11.0, 7.0): 0.00785099115193, (10.0, 5.0): 0.00827357436563, (16.0, 5.0): 0.00827317188273, (20.0, 24.0): 0.00831318128077, (19.0, 16.0): 0.00768410858051, (16.0, 8.0): 0.00807241412722, (14.0, 7.0): 0.00775853190204, (13.0, 5.0): 0.00795883485541, (18.0, 20.0): 0.00983146911295, (18.0, 7.0): 0.00672285596241, (15.0, 6.0): 0.00791181602347, (18.0, 18.0): 0.00977950112769, (8.0, 4.0): 0.00708279577911, (10.0, 11.0): 0.00692073019202, (13.0, 8.0): 0.00779196429707, (10.0, 6.0): 0.00809472507296, (15.0, 22.0): 0.00546296799637, (12.0, 5.0): 0.00798689592765, (17.0, 3.0): 0.00708238017366, (15.0, 8.0): 0.00790103859468, (21.0, 16.0): 0.00541314659636, (18.0, 8.0): 0.00676763750419, (15.0, 5.0): 0.00808547396026, (18.0, 23.0): 0.00758105995114, (16.0, 15.0): 0.00800232795622, (12.0, 10.0): 0.00820677215822, (15.0, 15.0): 0.00543026133163, (13.0, 11.0): 0.00665569718457, (18.0, 13.0): 0.00672174416555, (18.0, 24.0): 0.00778402309102, (8.0, 11.0): 0.00537721595916, (9.0, 10.0): 0.00872270498206, (19.0, 20.0): 0.00769501346153, (17.0, 4.0): 0.00872254138424, (14.0, 11.0): 0.00667285277178, (18.0, 3.0): 0.00537717713273, (17.0, 9.0): 0.00847667387543, (18.0, 14.0): 0.00705108225948, (8.0, 8.0): 0.00678821164745, (9.0, 5.0): 0.00850297197706, (11.0, 6.0): 0.00791250801615, }, ]
+10 Stop [{(16.0, 6.0): 0.00812411723695, (11.0, 11.0): 0.00675769056731, (17.0, 7.0): 0.00825740831748, (16.0, 9.0): 0.00826481263768, (14.0, 4.0): 0.00820911128493, (13.0, 4.0): 0.00817589194088, (20.0, 20.0): 0.00795247985645, (18.0, 4.0): 0.00703805069999, (18.0, 19.0): 0.00732368906002, (8.0, 5.0): 0.00689930070689, (10.0, 8.0): 0.00812775326545, (11.0, 5.0): 0.00811423322729, (10.0, 7.0): 0.00806659150622, (16.0, 3.0): 0.00689440123196, (12.0, 6.0): 0.00785391070659, (16.0, 22.0): 0.00801380155073, (13.0, 7.0): 0.00776733453721, (18.0, 9.0): 0.00692738707863, (15.0, 4.0): 0.00831481519052, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00538043995529, (9.0, 3.0): 0.00703773361416, (19.0, 24.0): 0.0080535357174, (12.0, 11.0): 0.0066702766933, (13.0, 10.0): 0.00816957693109, (18.0, 10.0): 0.00720954999519, (17.0, 18.0): 0.00766513210642, (9.0, 9.0): 0.0084960349859, (16.0, 18.0): 0.00793692861751, (11.0, 8.0): 0.00795236620727, (14.0, 8.0): 0.00784876553965, (21.0, 24.0): 0.00558215370771, (20.0, 16.0): 0.00792589692832, (17.0, 8.0): 0.00830980195284, (18.0, 15.0): 0.0098354967646, (8.0, 9.0): 0.00689930070689, (9.0, 4.0): 0.00868659417198, (10.0, 3.0): 0.00689440123196, (16.0, 7.0): 0.00805605241709, (11.0, 10.0): 0.00831450202962, (17.0, 6.0): 0.00832764931646, (16.0, 10.0): 0.00848311338342, (14.0, 5.0): 0.00801143120685, (13.0, 3.0): 0.00664510194263, (18.0, 5.0): 0.00689708825928, (17.0, 11.0): 0.0072086838936, (18.0, 16.0): 0.00990188691356, (8.0, 6.0): 0.00677779060807, (10.0, 9.0): 0.00829151386715, (9.0, 7.0): 0.00827351675414, (11.0, 4.0): 0.00831481519052, (10.0, 4.0): 0.00849215568947, (16.0, 4.0): 0.00849204247333, (12.0, 7.0): 0.00779615540384, (11.0, 9.0): 0.00811412001115, (15.0, 10.0): 0.00829065281711, (14.0, 6.0): 0.00785360659559, (13.0, 6.0): 0.00782438330961, (18.0, 6.0): 0.00677094342926, (17.0, 22.0): 0.00778750818375, (15.0, 7.0): 0.00788828987117, (18.0, 21.0): 0.0074151832263, (8.0, 3.0): 0.00533369930791, (10.0, 10.0): 0.0084921132633, (11.0, 3.0): 0.00675823634753, (12.0, 4.0): 0.00820911128493, (15.0, 9.0): 0.00809211129806, (14.0, 3.0): 0.00667248914091, (13.0, 9.0): 0.00797699402887, (12.0, 8.0): 0.00785369181148, (18.0, 11.0): 0.00763094176924, (18.0, 22.0): 0.0100804034135, (9.0, 8.0): 0.00833396432625, (14.0, 9.0): 0.00800089211772, (12.0, 9.0): 0.00801069674123, (17.0, 15.0): 0.00768971837191, (21.0, 20.0): 0.00538747536927, (18.0, 12.0): 0.00625745732734, (15.0, 11.0): 0.00674514542456, (8.0, 10.0): 0.00703859971575, (9.0, 11.0): 0.00703772390755, (12.0, 3.0): 0.00667248914091, (17.0, 5.0): 0.00849420415008, (16.0, 11.0): 0.0069224876037, (14.0, 10.0): 0.00819300284828, (17.0, 10.0): 0.00875643010085, (15.0, 3.0): 0.00675823634753, (18.0, 17.0): 0.00733136074944, (8.0, 7.0): 0.00673080053725, (9.0, 6.0): 0.00833396432625, (11.0, 7.0): 0.00789343503811, (10.0, 5.0): 0.00829151386715, (16.0, 5.0): 0.00829077940154, (20.0, 24.0): 0.00826995463452, (19.0, 16.0): 0.00768218392174, (16.0, 8.0): 0.00810563133622, (14.0, 7.0): 0.00779468782008, (13.0, 5.0): 0.00798063005737, (18.0, 20.0): 0.00989594358212, (18.0, 7.0): 0.00671723316392, (15.0, 6.0): 0.00795113216359, (18.0, 18.0): 0.00984593561993, (8.0, 4.0): 0.00703859971575, (10.0, 11.0): 0.00689432034356, (13.0, 8.0): 0.00782314926593, (10.0, 6.0): 0.00812775326545, (15.0, 22.0): 0.00541773406899, (12.0, 5.0): 0.00801143120685, (17.0, 3.0): 0.00703768795245, (15.0, 8.0): 0.00793984607338, (21.0, 16.0): 0.00536452740149, (18.0, 8.0): 0.0067646996851, (15.0, 5.0): 0.00811401433217, (18.0, 23.0): 0.0076268849786, (16.0, 15.0): 0.00795081711853, (12.0, 10.0): 0.00820728044911, (15.0, 15.0): 0.00538257331789, (13.0, 11.0): 0.00663825476382, (18.0, 13.0): 0.00669053533652, (18.0, 24.0): 0.00780949527419, (8.0, 11.0): 0.00533369930791, (9.0, 10.0): 0.00868659417198, (19.0, 20.0): 0.00769108307794, (17.0, 4.0): 0.00868628101109, (14.0, 11.0): 0.00665892176757, (18.0, 3.0): 0.00533361518397, (17.0, 9.0): 0.00848699267985, (18.0, 14.0): 0.00704497406088, (8.0, 8.0): 0.00677779060807, (9.0, 5.0): 0.0084960349859, (11.0, 6.0): 0.00795236620727, }, ]
+11 Stop [{(16.0, 6.0): 0.00814994610712, (11.0, 11.0): 0.00674347230697, (17.0, 7.0): 0.00826949069175, (16.0, 9.0): 0.00828653226704, (14.0, 4.0): 0.00821037220986, (13.0, 4.0): 0.00817622439928, (20.0, 20.0): 0.00790825866276, (18.0, 4.0): 0.00699891267002, (18.0, 19.0): 0.00737669948719, (8.0, 5.0): 0.00687812975486, (10.0, 8.0): 0.00815443783447, (11.0, 5.0): 0.00813687193982, (10.0, 7.0): 0.00809780996587, (16.0, 3.0): 0.00687098987765, (12.0, 6.0): 0.00788764936683, (16.0, 22.0): 0.00797597027932, (13.0, 7.0): 0.00780114206733, (18.0, 9.0): 0.0069228077257, (15.0, 4.0): 0.00831555574307, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00533586285015, (9.0, 3.0): 0.00699825231523, (19.0, 24.0): 0.00804432854204, (12.0, 11.0): 0.00665801159593, (13.0, 10.0): 0.00816893454242, (18.0, 10.0): 0.00719325573094, (17.0, 18.0): 0.00766217081296, (9.0, 9.0): 0.00848644664698, (16.0, 18.0): 0.00789090688562, (11.0, 8.0): 0.00798827326669, (14.0, 8.0): 0.00788146816335, (21.0, 24.0): 0.0055477283987, (20.0, 16.0): 0.00788495731743, (17.0, 8.0): 0.00832314177855, (18.0, 15.0): 0.0098459100638, (8.0, 9.0): 0.00687812975486, (9.0, 4.0): 0.00865404030195, (10.0, 3.0): 0.00687102393631, (16.0, 7.0): 0.00808629983578, (11.0, 10.0): 0.00831502579246, (17.0, 6.0): 0.00833341166151, (16.0, 10.0): 0.00848962368874, (14.0, 5.0): 0.00803375869538, (13.0, 3.0): 0.00663269844429, (18.0, 5.0): 0.00687536142715, (17.0, 11.0): 0.00719181433681, (18.0, 16.0): 0.00993886080993, (8.0, 6.0): 0.0067687658283, (10.0, 9.0): 0.00830430707859, (9.0, 7.0): 0.00828430751688, (11.0, 4.0): 0.00831562216533, (10.0, 4.0): 0.00848061609182, (16.0, 4.0): 0.00848038392329, (12.0, 7.0): 0.00783290549945, (11.0, 9.0): 0.00813663977128, (15.0, 10.0): 0.00829806242551, (14.0, 6.0): 0.00788704821914, (13.0, 6.0): 0.00785597304127, (18.0, 6.0): 0.00676184607641, (17.0, 22.0): 0.0077872040982, (15.0, 7.0): 0.00792600166906, (18.0, 21.0): 0.00746581449101, (8.0, 3.0): 0.00529698310178, (10.0, 10.0): 0.0084805247523, (11.0, 3.0): 0.0067442447182, (12.0, 4.0): 0.00821037220986, (15.0, 9.0): 0.00811766298879, (14.0, 3.0): 0.00666077911475, (13.0, 9.0): 0.00799626181697, (12.0, 8.0): 0.00788721188595, (18.0, 11.0): 0.00759811302362, (18.0, 22.0): 0.0101299596496, (9.0, 8.0): 0.00834070151837, (14.0, 9.0): 0.00802235316651, (12.0, 9.0): 0.00803255660837, (17.0, 15.0): 0.0076723860213, (21.0, 20.0): 0.00534456430345, (18.0, 12.0): 0.00622373299693, (15.0, 11.0): 0.00673976926238, (8.0, 10.0): 0.00699969370936, (9.0, 11.0): 0.00699822966648, (12.0, 3.0): 0.00666077911475, (17.0, 5.0): 0.00848405484065, (16.0, 11.0): 0.00691570190715, (14.0, 10.0): 0.00819555538473, (17.0, 10.0): 0.00874986570502, (15.0, 3.0): 0.0067442447182, (18.0, 17.0): 0.00738074254985, (8.0, 7.0): 0.00672629878917, (9.0, 6.0): 0.00834070151837, (11.0, 7.0): 0.00793218287254, (10.0, 5.0): 0.00830433820705, (16.0, 5.0): 0.00830303151881, (20.0, 24.0): 0.0082322403045, (19.0, 16.0): 0.00767816534508, (16.0, 8.0): 0.00813522888344, (14.0, 7.0): 0.00783053687274, (13.0, 5.0): 0.00800075354431, (18.0, 20.0): 0.00995063768362, (18.0, 7.0): 0.00671470073306, (15.0, 6.0): 0.00798623203989, (18.0, 18.0): 0.00990154454361, (8.0, 4.0): 0.00699969370936, (10.0, 11.0): 0.00687085635726, (13.0, 8.0): 0.00785398703663, (10.0, 6.0): 0.00815443783447, (15.0, 22.0): 0.00538013421807, (12.0, 5.0): 0.00803386329661, (17.0, 3.0): 0.00699815022631, (15.0, 8.0): 0.0079749288237, (21.0, 16.0): 0.00532422934352, (18.0, 8.0): 0.00676429037248, (15.0, 5.0): 0.00813643445893, (18.0, 23.0): 0.00766556093764, (16.0, 15.0): 0.00790479848909, (12.0, 10.0): 0.00820798040354, (15.0, 15.0): 0.00534155903179, (13.0, 11.0): 0.00662577869239, (18.0, 13.0): 0.00666432224158, (18.0, 24.0): 0.00782997199006, (8.0, 11.0): 0.00529698067513, (9.0, 10.0): 0.00865402939006, (19.0, 20.0): 0.00768850687366, (17.0, 4.0): 0.00865344019001, (14.0, 11.0): 0.00664918105864, (18.0, 3.0): 0.0052968063911, (17.0, 9.0): 0.0084954542439, (18.0, 14.0): 0.00703737732362, (8.0, 8.0): 0.0067687658283, (9.0, 5.0): 0.00848644664698, (11.0, 6.0): 0.00798833968894, }, ]
+12 Stop [{(16.0, 6.0): 0.00817178423262, (11.0, 11.0): 0.00673109022353, (17.0, 7.0): 0.00828114397678, (16.0, 9.0): 0.00830490041438, (14.0, 4.0): 0.00821237698821, (13.0, 4.0): 0.00817771908373, (20.0, 20.0): 0.0078712039972, (18.0, 4.0): 0.00696485869266, (18.0, 19.0): 0.00742194538587, (8.0, 5.0): 0.00685893665253, (10.0, 8.0): 0.0081771059328, (11.0, 5.0): 0.00815580705955, (10.0, 7.0): 0.00812463520485, (16.0, 3.0): 0.0068494229902, (12.0, 6.0): 0.00791974617862, (16.0, 22.0): 0.00794445856821, (13.0, 7.0): 0.00783490890348, (18.0, 9.0): 0.00691917930606, (15.0, 4.0): 0.00831461044658, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00529823372028, (9.0, 3.0): 0.0069637881572, (19.0, 24.0): 0.0080355136122, (12.0, 11.0): 0.00664841172953, (13.0, 10.0): 0.00817019110263, (18.0, 10.0): 0.00717851726107, (17.0, 18.0): 0.00765974536876, (9.0, 9.0): 0.00847662936551, (16.0, 18.0): 0.00785229065793, (11.0, 8.0): 0.00801974912619, (14.0, 8.0): 0.00791265481259, (21.0, 24.0): 0.00551794430085, (20.0, 16.0): 0.00784982222593, (17.0, 8.0): 0.00833573571265, (18.0, 15.0): 0.00984944716674, (8.0, 9.0): 0.00685893665253, (9.0, 4.0): 0.0086237071143, (10.0, 3.0): 0.0068495034608, (16.0, 7.0): 0.00811339343743, (11.0, 10.0): 0.00831390222066, (17.0, 6.0): 0.00833784217931, (16.0, 10.0): 0.00849374229405, (14.0, 5.0): 0.00805367342553, (13.0, 3.0): 0.0066238090483, (18.0, 5.0): 0.00685584101153, (17.0, 11.0): 0.0071763804579, (18.0, 16.0): 0.00996582868341, (8.0, 6.0): 0.00676143889676, (10.0, 9.0): 0.00831247121672, (9.0, 7.0): 0.00829427880119, (11.0, 4.0): 0.00831475766092, (10.0, 4.0): 0.00846867933731, (16.0, 4.0): 0.00846822974445, (12.0, 7.0): 0.00786821833842, (11.0, 9.0): 0.00815536050348, (15.0, 10.0): 0.00830512321315, (14.0, 6.0): 0.00791870977369, (13.0, 6.0): 0.00788651324778, (18.0, 6.0): 0.00675465939146, (17.0, 22.0): 0.00778688137157, (15.0, 7.0): 0.00796079984823, (18.0, 21.0): 0.00750875416364, (8.0, 3.0): 0.00526514754008, (10.0, 10.0): 0.008468491492, (11.0, 3.0): 0.00673213637538, (12.0, 4.0): 0.0082124111929, (15.0, 9.0): 0.00813990793431, (14.0, 3.0): 0.00665150501128, (13.0, 9.0): 0.00801481863418, (12.0, 8.0): 0.00791898685942, (18.0, 11.0): 0.00757037343849, (18.0, 22.0): 0.0101720164214, (9.0, 8.0): 0.00834537016042, (14.0, 9.0): 0.00804266030407, (12.0, 9.0): 0.00805213009722, (17.0, 15.0): 0.00765387235275, (21.0, 20.0): 0.00530836837265, (18.0, 12.0): 0.00619554666874, (15.0, 11.0): 0.00673577554215, (8.0, 10.0): 0.00696592196911, (9.0, 11.0): 0.00696373760899, (12.0, 3.0): 0.00665150501128, (17.0, 5.0): 0.00847362799898, (16.0, 11.0): 0.00690974611433, (14.0, 10.0): 0.0081992763685, (17.0, 10.0): 0.00874325624447, (15.0, 3.0): 0.00673211457627, (18.0, 17.0): 0.007420348855, (8.0, 7.0): 0.00672281911482, (9.0, 6.0): 0.00834537016042, (11.0, 7.0): 0.0079679022587, (10.0, 5.0): 0.00831254214403, (16.0, 5.0): 0.00831077016976, (20.0, 24.0): 0.00819938714819, (19.0, 16.0): 0.00767242275665, (16.0, 8.0): 0.0081612263177, (14.0, 7.0): 0.00786523939833, (13.0, 5.0): 0.00802011459537, (18.0, 20.0): 0.00999799970486, (18.0, 7.0): 0.00671410743384, (15.0, 6.0): 0.00801713249883, (18.0, 18.0): 0.00994859041923, (8.0, 4.0): 0.00696592496037, (10.0, 11.0): 0.00684924453314, (13.0, 8.0): 0.00788401419405, (10.0, 6.0): 0.00817712544294, (15.0, 22.0): 0.00534872386881, (12.0, 5.0): 0.00805390207149, (17.0, 3.0): 0.00696357519436, (15.0, 8.0): 0.00800705810567, (21.0, 16.0): 0.00529043377757, (18.0, 8.0): 0.00676507806352, (15.0, 5.0): 0.00815500249122, (18.0, 23.0): 0.00769766755497, (16.0, 15.0): 0.00786317435269, (12.0, 10.0): 0.00820940236834, (15.0, 15.0): 0.00530571234559, (13.0, 11.0): 0.00661702974523, (18.0, 13.0): 0.00664181085404, (18.0, 24.0): 0.00784662048991, (8.0, 11.0): 0.005265141069, (9.0, 10.0): 0.00862368100183, (19.0, 20.0): 0.00768658126638, (17.0, 4.0): 0.00862284151487, (14.0, 11.0): 0.0066427933303, (18.0, 3.0): 0.00526486785445, (17.0, 9.0): 0.00850170073033, (18.0, 14.0): 0.00702871070435, (8.0, 8.0): 0.00676143889676, (9.0, 5.0): 0.00847663777358, (11.0, 6.0): 0.00801989634052, }, ]
+13 Stop [{(16.0, 6.0): 0.00819018450359, (11.0, 11.0): 0.00671996706568, (17.0, 7.0): 0.00829214991969, (16.0, 9.0): 0.00832029553816, (14.0, 4.0): 0.00821455224163, (13.0, 4.0): 0.00818047663412, (20.0, 20.0): 0.00784011260752, (18.0, 4.0): 0.0069346991805, (18.0, 19.0): 0.00746062932631, (8.0, 5.0): 0.00684190268213, (10.0, 8.0): 0.00819586632819, (11.0, 5.0): 0.0081713810553, (10.0, 7.0): 0.00814820952809, (16.0, 3.0): 0.0068299241391, (12.0, 6.0): 0.00794965523536, (16.0, 22.0): 0.00791814191433, (13.0, 7.0): 0.00786777881641, (18.0, 9.0): 0.00691603380373, (15.0, 4.0): 0.00831307257816, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00526654707945, (9.0, 3.0): 0.00693311350738, (19.0, 24.0): 0.0080271737501, (12.0, 11.0): 0.00664101339824, (13.0, 10.0): 0.00817299513104, (18.0, 10.0): 0.0071656687503, (17.0, 18.0): 0.00765782628037, (9.0, 9.0): 0.00846636451203, (16.0, 18.0): 0.00781979553571, (11.0, 8.0): 0.00804782093611, (14.0, 8.0): 0.00794232536294, (21.0, 24.0): 0.00549210119982, (20.0, 16.0): 0.00781929854965, (17.0, 8.0): 0.00834723086337, (18.0, 15.0): 0.0098480133149, (8.0, 9.0): 0.0068419002527, (9.0, 4.0): 0.00859623312443, (10.0, 3.0): 0.00683009286581, (16.0, 7.0): 0.00813766956256, (11.0, 10.0): 0.00831220387278, (17.0, 6.0): 0.0083415445254, (16.0, 10.0): 0.00849684096179, (14.0, 5.0): 0.0080719754548, (13.0, 3.0): 0.00661724858446, (18.0, 5.0): 0.00683856537371, (17.0, 11.0): 0.00716277625157, (18.0, 16.0): 0.00998474283309, (8.0, 6.0): 0.00675487269811, (10.0, 9.0): 0.0083180117021, (9.0, 7.0): 0.00830263564408, (11.0, 4.0): 0.00831336514398, (10.0, 4.0): 0.00845631311651, (16.0, 4.0): 0.00845564612268, (12.0, 7.0): 0.00790195250773, (11.0, 9.0): 0.00817072263285, (15.0, 10.0): 0.00831155384754, (14.0, 6.0): 0.00794825366883, (13.0, 6.0): 0.00791599853979, (18.0, 6.0): 0.00674872039507, (17.0, 22.0): 0.00778678408527, (15.0, 7.0): 0.0079927246577, (18.0, 21.0): 0.0075454220861, (8.0, 3.0): 0.00523747745942, (10.0, 10.0): 0.00845602031953, (11.0, 3.0): 0.00672123774405, (12.0, 4.0): 0.00821463425463, (15.0, 9.0): 0.00815992999432, (14.0, 3.0): 0.00664433255661, (13.0, 9.0): 0.00803276286643, (12.0, 8.0): 0.00794861972306, (18.0, 11.0): 0.00754650001228, (18.0, 22.0): 0.0102074384687, (9.0, 8.0): 0.00834903657617, (14.0, 9.0): 0.00806186361073, (12.0, 9.0): 0.00807013969253, (17.0, 15.0): 0.0076347106935, (21.0, 20.0): 0.00527791885206, (18.0, 12.0): 0.00617171253388, (15.0, 11.0): 0.00673310338932, (8.0, 10.0): 0.00693599787878, (9.0, 11.0): 0.0069330287589, (12.0, 3.0): 0.00664434484732, (17.0, 5.0): 0.00846297662547, (16.0, 11.0): 0.0069042239874, (14.0, 10.0): 0.00820414853024, (17.0, 10.0): 0.00873646428351, (15.0, 3.0): 0.00672118273375, (18.0, 17.0): 0.007452054394, (8.0, 7.0): 0.00672027998732, (9.0, 6.0): 0.00834904215982, (11.0, 7.0): 0.00800008025373, (10.0, 5.0): 0.00831815835148, (16.0, 5.0): 0.00831588292741, (20.0, 24.0): 0.00817060573722, (19.0, 16.0): 0.00766553883171, (16.0, 8.0): 0.00818446279757, (14.0, 7.0): 0.00789846254726, (13.0, 5.0): 0.00803838448478, (18.0, 20.0): 0.0100385935315, (18.0, 7.0): 0.00671469001756, (15.0, 6.0): 0.00804468576892, (18.0, 18.0): 0.00998782747469, (8.0, 4.0): 0.00693600600611, (10.0, 11.0): 0.00682971638981, (13.0, 8.0): 0.00791307668074, (10.0, 6.0): 0.00819591385855, (15.0, 22.0): 0.00532251479047, (12.0, 5.0): 0.00807239621959, (17.0, 3.0): 0.0069327738006, (15.0, 8.0): 0.0080363294037, (21.0, 16.0): 0.00526182429743, (18.0, 8.0): 0.00676673834338, (15.0, 5.0): 0.00817023780638, (18.0, 23.0): 0.00772443345363, (16.0, 15.0): 0.00782520507461, (12.0, 10.0): 0.00821122809015, (15.0, 15.0): 0.00527391429036, (13.0, 11.0): 0.00661109692179, (18.0, 13.0): 0.00662202274238, (18.0, 24.0): 0.00785993388569, (8.0, 11.0): 0.00523746191753, (9.0, 10.0): 0.00859617526639, (19.0, 20.0): 0.00768542834741, (17.0, 4.0): 0.00859504832342, (14.0, 11.0): 0.00663875492812, (18.0, 3.0): 0.00523706442324, (17.0, 9.0): 0.00850691344688, (18.0, 14.0): 0.00701920231115, (8.0, 8.0): 0.00675487269811, (9.0, 5.0): 0.0084663856016, (11.0, 6.0): 0.00804809545607, }, ]
+14 Stop [{(16.0, 6.0): 0.00820599345758, (11.0, 11.0): 0.00671011498799, (17.0, 7.0): 0.0083023914786, (16.0, 9.0): 0.00833368854774, (14.0, 4.0): 0.00821709852089, (13.0, 4.0): 0.00818392166915, (20.0, 20.0): 0.00781413974434, (18.0, 4.0): 0.00690801394432, (18.0, 19.0): 0.00749348169365, (8.0, 5.0): 0.00682647246691, (10.0, 8.0): 0.00821178901414, (11.0, 5.0): 0.00818467924528, (10.0, 7.0): 0.00816854112253, (16.0, 3.0): 0.0068120993929, (12.0, 6.0): 0.00797761959171, (16.0, 22.0): 0.00789623272844, (13.0, 7.0): 0.00789945381839, (18.0, 9.0): 0.00691349291373, (15.0, 4.0): 0.00831099743321, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00523987205163, (9.0, 3.0): 0.00690587403799, (19.0, 24.0): 0.00801923779101, (12.0, 11.0): 0.00663526496446, (13.0, 10.0): 0.00817700115402, (18.0, 10.0): 0.00715434349828, (17.0, 18.0): 0.0076561641407, (9.0, 9.0): 0.00845639267451, (16.0, 18.0): 0.00779248081175, (11.0, 8.0): 0.00807262197479, (14.0, 8.0): 0.00797041152107, (21.0, 24.0): 0.00546958584565, (20.0, 16.0): 0.00779252460917, (17.0, 8.0): 0.00835783599135, (18.0, 15.0): 0.00984282670521, (8.0, 9.0): 0.0068264656098, (9.0, 4.0): 0.00857106624688, (10.0, 3.0): 0.00681237365261, (16.0, 7.0): 0.00815943828822, (11.0, 10.0): 0.00831002674948, (17.0, 6.0): 0.0083445512136, (16.0, 10.0): 0.00849908692305, (14.0, 5.0): 0.00808868073129, (13.0, 3.0): 0.00661257682392, (18.0, 5.0): 0.00682309156241, (17.0, 11.0): 0.00715066791952, (18.0, 16.0): 0.0099973867789, (8.0, 6.0): 0.00674907227385, (10.0, 9.0): 0.00832139709894, (9.0, 7.0): 0.00830985477846, (11.0, 4.0): 0.0083114481501, (10.0, 4.0): 0.00844433716373, (16.0, 4.0): 0.00844341102511, (12.0, 7.0): 0.00793361730726, (11.0, 9.0): 0.00818377976728, (15.0, 10.0): 0.00831777051411, (14.0, 6.0): 0.00797587519592, (13.0, 6.0): 0.00794401414903, (18.0, 6.0): 0.00674380285166, (17.0, 22.0): 0.00778683495038, (15.0, 7.0): 0.00802197438803, (18.0, 21.0): 0.00757664869543, (8.0, 3.0): 0.00521310569818, (10.0, 10.0): 0.00844391132961, (11.0, 3.0): 0.00671159189309, (12.0, 4.0): 0.00821726066229, (15.0, 9.0): 0.00817799447889, (14.0, 3.0): 0.00663860141703, (13.0, 9.0): 0.00805016759629, (12.0, 8.0): 0.00797632190803, (18.0, 11.0): 0.00752597376483, (18.0, 22.0): 0.0102374061588, (9.0, 8.0): 0.00835149878662, (14.0, 9.0): 0.00808020607293, (12.0, 9.0): 0.008086694601, (17.0, 15.0): 0.00761530858476, (21.0, 20.0): 0.0052523302952, (18.0, 12.0): 0.00615120342849, (15.0, 11.0): 0.00673133134572, (8.0, 10.0): 0.00690953022532, (9.0, 11.0): 0.00690574197963, (12.0, 3.0): 0.00663863464489, (17.0, 5.0): 0.00845273182377, (16.0, 11.0): 0.00689939409943, (14.0, 10.0): 0.00820980095594, (17.0, 10.0): 0.0087301549889, (15.0, 3.0): 0.006711474373, (18.0, 17.0): 0.00747716070828, (8.0, 7.0): 0.0067180334747, (9.0, 6.0): 0.00835151362734, (11.0, 7.0): 0.00802923173635, (10.0, 5.0): 0.00832163039669, (16.0, 5.0): 0.00831898559711, (20.0, 24.0): 0.00814531042902, (19.0, 16.0): 0.00765779816873, (16.0, 8.0): 0.00820519763307, (14.0, 7.0): 0.00792990901063, (13.0, 5.0): 0.00805584626662, (18.0, 20.0): 0.0100734749695, (18.0, 7.0): 0.00671596717294, (15.0, 6.0): 0.00806921728108, (18.0, 18.0): 0.0100204602022, (8.0, 4.0): 0.00690954961675, (10.0, 11.0): 0.0068118821175, (13.0, 8.0): 0.00794091268992, (10.0, 6.0): 0.0082118838708, (15.0, 22.0): 0.00530063803335, (12.0, 5.0): 0.00808929024993, (17.0, 3.0): 0.00690537229069, (15.0, 8.0): 0.00806315444324, (21.0, 16.0): 0.0052373449986, (18.0, 8.0): 0.00676881171384, (15.0, 5.0): 0.00818317090713, (18.0, 23.0): 0.00774664873029, (16.0, 15.0): 0.00779026240122, (12.0, 10.0): 0.00821356670686, (15.0, 15.0): 0.00524535883672, (13.0, 11.0): 0.00660731533824, (18.0, 13.0): 0.00660431252914, (18.0, 24.0): 0.00787051369648, (8.0, 11.0): 0.00521307729859, (9.0, 10.0): 0.00857096867901, (19.0, 20.0): 0.00768482870119, (17.0, 4.0): 0.00856960245959, (14.0, 11.0): 0.00663656851586, (18.0, 3.0): 0.00521255638635, (17.0, 9.0): 0.00851118927732, (18.0, 14.0): 0.00700907834657, (8.0, 8.0): 0.00674907054977, (9.0, 5.0): 0.008456439518, (11.0, 6.0): 0.0080730251718, }, ]
+15 Stop [{(16.0, 6.0): 0.00821963716752, (11.0, 11.0): 0.00670132086738, (17.0, 7.0): 0.00831183518759, (16.0, 9.0): 0.00834543137201, (14.0, 4.0): 0.00821979002517, (13.0, 4.0): 0.00818796962977, (20.0, 20.0): 0.00779248796277, (18.0, 4.0): 0.00688421566405, (18.0, 19.0): 0.00752131102415, (8.0, 5.0): 0.00681256149298, (10.0, 8.0): 0.0082251695994, (11.0, 5.0): 0.00819601464276, (10.0, 7.0): 0.00818626010446, (16.0, 3.0): 0.00679591871917, (12.0, 6.0): 0.00800351329395, (16.0, 22.0): 0.00787800824295, (13.0, 7.0): 0.00792958139504, (18.0, 9.0): 0.00691139988693, (15.0, 4.0): 0.00830880417052, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00521742962973, (9.0, 3.0): 0.00688147707142, (19.0, 24.0): 0.0080116873055, (12.0, 11.0): 0.00663088716404, (13.0, 10.0): 0.00818193611718, (18.0, 10.0): 0.00714448354199, (17.0, 18.0): 0.00765466336804, (9.0, 9.0): 0.00844666785027, (16.0, 18.0): 0.0077694843433, (11.0, 8.0): 0.00809474888012, (14.0, 8.0): 0.00799691874756, (21.0, 24.0): 0.00544989639916, (20.0, 16.0): 0.00776878009193, (17.0, 8.0): 0.00836752580453, (18.0, 15.0): 0.00983484901481, (8.0, 9.0): 0.00681254513113, (9.0, 4.0): 0.00854822449941, (10.0, 3.0): 0.00679632732867, (16.0, 7.0): 0.0081789990491, (11.0, 10.0): 0.00830778565764, (17.0, 6.0): 0.00834708430762, (16.0, 10.0): 0.00850098871962, (14.0, 5.0): 0.00810413432437, (13.0, 3.0): 0.00660923755529, (18.0, 5.0): 0.00680927345435, (17.0, 11.0): 0.00714003994373, (18.0, 16.0): 0.0100050396634, (8.0, 6.0): 0.00674369727933, (10.0, 9.0): 0.0083234539769, (9.0, 7.0): 0.00831579003167, (11.0, 4.0): 0.00830944301755, (10.0, 4.0): 0.00843278980463, (16.0, 4.0): 0.00843162415123, (12.0, 7.0): 0.00796324887235, (11.0, 9.0): 0.0081949040383, (15.0, 10.0): 0.00832376341083, (14.0, 6.0): 0.00800153927359, (13.0, 6.0): 0.00797056180433, (18.0, 6.0): 0.00673962563947, (17.0, 22.0): 0.00778704076598, (15.0, 7.0): 0.00804873868224, (18.0, 21.0): 0.00760326984723, (8.0, 3.0): 0.00519155781308, (10.0, 10.0): 0.00843223130078, (11.0, 3.0): 0.00670293967767, (12.0, 4.0): 0.00822004280752, (15.0, 9.0): 0.00819456281138, (14.0, 3.0): 0.00663408285767, (13.0, 9.0): 0.00806699642283, (12.0, 8.0): 0.0080020336962, (18.0, 11.0): 0.00750814743849, (18.0, 22.0): 0.0102627289984, (9.0, 8.0): 0.00835317468819, (14.0, 9.0): 0.00809771612503, (12.0, 9.0): 0.00810210611589, (17.0, 15.0): 0.00759589700496, (21.0, 20.0): 0.00523087839571, (18.0, 12.0): 0.00613333209375, (15.0, 11.0): 0.00673037759307, (8.0, 10.0): 0.00688588512745, (9.0, 11.0): 0.00688129219295, (12.0, 3.0): 0.00663415297293, (17.0, 5.0): 0.00844294710942, (16.0, 11.0): 0.00689516572578, (14.0, 10.0): 0.00821609786836, (17.0, 10.0): 0.0087243390923, (15.0, 3.0): 0.00670274328237, (18.0, 17.0): 0.00749684864804, (8.0, 7.0): 0.00671601503027, (9.0, 6.0): 0.00835320642738, (11.0, 7.0): 0.00805540746255, (10.0, 5.0): 0.0083237940389, (16.0, 5.0): 0.00832085856214, (20.0, 24.0): 0.00812297566283, (19.0, 16.0): 0.00764945428736, (16.0, 8.0): 0.00822386298073, (14.0, 7.0): 0.00795952478681, (13.0, 5.0): 0.0080723506132, (18.0, 20.0): 0.0101033551058, (18.0, 7.0): 0.00671762373033, (15.0, 6.0): 0.00809124624595, (18.0, 18.0): 0.0100473838981, (8.0, 4.0): 0.00688592066968, (10.0, 11.0): 0.0067957170372, (13.0, 8.0): 0.00796745350674, (10.0, 6.0): 0.00822531883783, (15.0, 22.0): 0.00528239659282, (12.0, 5.0): 0.00810493920317, (17.0, 3.0): 0.00688080720827, (15.0, 8.0): 0.00808774649286, (21.0, 16.0): 0.00521618070235, (18.0, 8.0): 0.0067711351484, (15.0, 5.0): 0.00819421038996, (18.0, 23.0): 0.00776507234863, (16.0, 15.0): 0.00775786974702, (12.0, 10.0): 0.00821627408339, (15.0, 15.0): 0.0052194335521, (13.0, 11.0): 0.00660518743544, (18.0, 13.0): 0.0065881981014, (18.0, 24.0): 0.00787880007259, (8.0, 11.0): 0.0051915104841, (9.0, 10.0): 0.00854807258787, (19.0, 20.0): 0.00768469155754, (17.0, 4.0): 0.00854649562044, (14.0, 11.0): 0.00663576399114, (18.0, 3.0): 0.0051908653542, (17.0, 9.0): 0.00851494698949, (18.0, 14.0): 0.00699850363487, (8.0, 8.0): 0.00674369216589, (9.0, 5.0): 0.00844674807451, (11.0, 6.0): 0.00809528792319, }, ]
+16 Stop [{(16.0, 6.0): 0.00823156506646, (11.0, 11.0): 0.00669353839869, (17.0, 7.0): 0.00832049480235, (16.0, 9.0): 0.00835595857465, (14.0, 4.0): 0.00822266034438, (13.0, 4.0): 0.00819234000395, (20.0, 20.0): 0.00777449903796, (18.0, 4.0): 0.00686295985509, (18.0, 19.0): 0.00754478842569, (8.0, 5.0): 0.0067998944754, (10.0, 8.0): 0.00823656144981, (11.0, 5.0): 0.00820589576511, (10.0, 7.0): 0.00820158920718, (16.0, 3.0): 0.0067811921327, (12.0, 6.0): 0.0080275102194, (16.0, 22.0): 0.00786288129939, (13.0, 7.0): 0.00795807407305, (18.0, 9.0): 0.00690974404223, (15.0, 4.0): 0.00830657156797, (1, 1): 0.00763358778626, (15.0, 18.0): 0.0051985429293, (9.0, 3.0): 0.00685961527093, (19.0, 24.0): 0.00800448768031, (12.0, 11.0): 0.0066276036834, (13.0, 10.0): 0.00818755775721, (18.0, 10.0): 0.00713587553531, (17.0, 18.0): 0.00765322854497, (9.0, 9.0): 0.00843741010343, (16.0, 18.0): 0.00775009738531, (11.0, 8.0): 0.00811445273531, (14.0, 8.0): 0.0080218719318, (21.0, 24.0): 0.00543260675386, (20.0, 16.0): 0.00774750181094, (17.0, 8.0): 0.00837641797957, (18.0, 15.0): 0.00982477238282, (8.0, 9.0): 0.00679986417617, (9.0, 4.0): 0.00852740191099, (10.0, 3.0): 0.00678174398037, (16.0, 7.0): 0.00819661461343, (11.0, 10.0): 0.00830556923287, (17.0, 6.0): 0.0083492071643, (16.0, 10.0): 0.0085026959504, (14.0, 5.0): 0.00811840492526, (13.0, 3.0): 0.00660696227243, (18.0, 5.0): 0.00679686811135, (17.0, 11.0): 0.00713070609546, (18.0, 16.0): 0.0100087398147, (8.0, 6.0): 0.00673870973612, (10.0, 9.0): 0.00832448535313, (9.0, 7.0): 0.00832069000791, (11.0, 4.0): 0.00830739297391, (10.0, 4.0): 0.00842193210427, (16.0, 4.0): 0.00842053618066, (12.0, 7.0): 0.00799075694402, (11.0, 9.0): 0.00820459973377, (15.0, 10.0): 0.00832967696031, (14.0, 6.0): 0.00802540128701, (13.0, 6.0): 0.00799550927602, (18.0, 6.0): 0.00673604756756, (17.0, 22.0): 0.00778736525258, (15.0, 7.0): 0.00807325105139, (18.0, 21.0): 0.00762594430846, (8.0, 3.0): 0.0051723687063, (10.0, 10.0): 0.00842123796394, (11.0, 3.0): 0.00669524359833, (12.0, 4.0): 0.00822301717483, (15.0, 9.0): 0.00820984404242, (14.0, 3.0): 0.00663047392887, (13.0, 9.0): 0.00808324165753, (12.0, 8.0): 0.00802591821426, (18.0, 11.0): 0.0074926117623, (18.0, 22.0): 0.0102841432369, (9.0, 8.0): 0.00835408347538, (14.0, 9.0): 0.00811445839503, (12.0, 9.0): 0.00811646287132, (17.0, 15.0): 0.00757663450436, (21.0, 20.0): 0.00521293518545, (18.0, 12.0): 0.00611754692467, (15.0, 11.0): 0.00673007950966, (8.0, 10.0): 0.00686472557692, (9.0, 11.0): 0.00685937031981, (12.0, 3.0): 0.00663059111298, (17.0, 5.0): 0.00843379548351, (16.0, 11.0): 0.00689159355957, (14.0, 10.0): 0.00822284370207, (17.0, 10.0): 0.00871918583171, (15.0, 3.0): 0.00669494704891, (18.0, 17.0): 0.00751205543971, (8.0, 7.0): 0.00671400912521, (9.0, 6.0): 0.00835413699411, (11.0, 7.0): 0.00807899064853, (10.0, 5.0): 0.00832493307973, (16.0, 5.0): 0.00832185547605, (20.0, 24.0): 0.00810316918903, (19.0, 16.0): 0.0076406713756, (16.0, 8.0): 0.00824071313985, (14.0, 7.0): 0.00798726057705, (13.0, 5.0): 0.00808799111497, (18.0, 20.0): 0.0101289295861, (18.0, 7.0): 0.00671946316707, (15.0, 6.0): 0.00811107435185, (18.0, 18.0): 0.0100694536546, (8.0, 4.0): 0.00686478471157, (10.0, 11.0): 0.00678102878454, (13.0, 8.0): 0.00799259675368, (10.0, 6.0): 0.00823677346635, (15.0, 22.0): 0.00526720104406, (12.0, 5.0): 0.00811937211212, (17.0, 3.0): 0.00685876905735, (15.0, 8.0): 0.00811036594295, (21.0, 16.0): 0.00519768371515, (18.0, 8.0): 0.00677354485232, (15.0, 5.0): 0.00820385073859, (18.0, 23.0): 0.00778030639001, (16.0, 15.0): 0.00772763902671, (12.0, 10.0): 0.00821934218583, (15.0, 15.0): 0.00519567335839, (13.0, 11.0): 0.00660434687109, (18.0, 13.0): 0.00657334461001, (18.0, 24.0): 0.00788518657557, (8.0, 11.0): 0.0051722978248, (9.0, 10.0): 0.00852718867788, (19.0, 20.0): 0.00768489861655, (17.0, 4.0): 0.0085254690943, (14.0, 11.0): 0.00663605182859, (18.0, 3.0): 0.00517154416948, (17.0, 9.0): 0.0085182986234, (18.0, 14.0): 0.00698761283246, (8.0, 8.0): 0.00673869801946, (9.0, 5.0): 0.00843753498128, (11.0, 6.0): 0.00811510843205, }, ]
+17 Stop [{(16.0, 6.0): 0.00824206333442, (11.0, 11.0): 0.00668665656323, (17.0, 7.0): 0.0083284127037, (16.0, 9.0): 0.00836550206614, (14.0, 4.0): 0.00822561385053, (13.0, 4.0): 0.00819694229573, (20.0, 20.0): 0.00775960014423, (18.0, 4.0): 0.00684389886696, (18.0, 19.0): 0.0075645252854, (8.0, 5.0): 0.00678835422703, (10.0, 8.0): 0.00824623444416, (11.0, 5.0): 0.00821454047258, (10.0, 7.0): 0.00821492095596, (16.0, 3.0): 0.00676783429587, (12.0, 6.0): 0.00804965139672, (16.0, 22.0): 0.00785034937268, (13.0, 7.0): 0.00798483952476, (18.0, 9.0): 0.00690845083214, (15.0, 4.0): 0.00830446052855, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00518263725975, (9.0, 3.0): 0.00683994309712, (19.0, 24.0): 0.00799761448164, (12.0, 11.0): 0.00662524067546, (13.0, 10.0): 0.0081936837783, (18.0, 10.0): 0.0071283950013, (17.0, 18.0): 0.00765180539041, (9.0, 9.0): 0.00842859956601, (16.0, 18.0): 0.00773371344141, (11.0, 8.0): 0.00813210455634, (14.0, 8.0): 0.0080453107201, (21.0, 24.0): 0.00541735977327, (20.0, 16.0): 0.00772823291976, (17.0, 8.0): 0.00838457112211, (18.0, 15.0): 0.00981312716165, (8.0, 9.0): 0.00678830396382, (9.0, 4.0): 0.00850847379493, (10.0, 3.0): 0.00676853713326, (16.0, 7.0): 0.0082125277347, (11.0, 10.0): 0.00830353442295, (17.0, 6.0): 0.00835102439522, (16.0, 10.0): 0.00850440185331, (14.0, 5.0): 0.00813166168204, (13.0, 3.0): 0.00660547482936, (18.0, 5.0): 0.0067857279802, (17.0, 11.0): 0.00712256502067, (18.0, 16.0): 0.0100092869878, (8.0, 6.0): 0.006733980733, (10.0, 9.0): 0.00832485892082, (9.0, 7.0): 0.00832460221822, (11.0, 4.0): 0.00830545850321, (10.0, 4.0): 0.00841176800887, (16.0, 4.0): 0.00841018449697, (12.0, 7.0): 0.00801625001985, (11.0, 9.0): 0.00821311398528, (15.0, 10.0): 0.00833553200845, (14.0, 6.0): 0.00804753008344, (13.0, 6.0): 0.00801889719409, (18.0, 6.0): 0.00673293614436, (17.0, 22.0): 0.00778778465988, (15.0, 7.0): 0.00809571330734, (18.0, 21.0): 0.0076452496419, (8.0, 3.0): 0.00515522289773, (10.0, 10.0): 0.0084109534417, (11.0, 3.0): 0.0066883732677, (12.0, 4.0): 0.00822607223121, (15.0, 9.0): 0.00822406078307, (14.0, 3.0): 0.00662762788143, (13.0, 9.0): 0.00809886348696, (12.0, 8.0): 0.00804803750372, (18.0, 11.0): 0.00747898065649, (18.0, 22.0): 0.0103022411262, (9.0, 8.0): 0.00835442351217, (14.0, 9.0): 0.00813045194577, (12.0, 9.0): 0.00812991293254, (17.0, 15.0): 0.00755761760606, (21.0, 20.0): 0.00519796727204, (18.0, 12.0): 0.0061034501188, (15.0, 11.0): 0.00673036661652, (8.0, 10.0): 0.00684568444878, (9.0, 11.0): 0.0068396367866, (12.0, 3.0): 0.0066278026809, (17.0, 5.0): 0.00842528247147, (16.0, 11.0): 0.00688863398125, (14.0, 10.0): 0.00822992032007, (17.0, 10.0): 0.00871468148879, (15.0, 3.0): 0.00668796759121, (18.0, 17.0): 0.00752356684722, (8.0, 7.0): 0.00671199222178, (9.0, 6.0): 0.00835450452396, (11.0, 7.0): 0.00810017959342, (10.0, 5.0): 0.00832541387935, (16.0, 5.0): 0.00832232058905, (20.0, 24.0): 0.00808552233337, (19.0, 16.0): 0.00763157601585, (16.0, 8.0): 0.00825601405009, (14.0, 7.0): 0.00801317178406, (13.0, 5.0): 0.00810272348646, (18.0, 20.0): 0.0101507761801, (18.0, 7.0): 0.00672136285721, (15.0, 6.0): 0.00812902849906, (18.0, 18.0): 0.0100873875504, (8.0, 4.0): 0.00684577308104, (10.0, 11.0): 0.00676773196855, (13.0, 8.0): 0.00801634052606, (10.0, 6.0): 0.00824650823588, (15.0, 22.0): 0.00525456095516, (12.0, 5.0): 0.00813275727729, (17.0, 3.0): 0.00683893217286, (15.0, 8.0): 0.00813120922168, (21.0, 16.0): 0.00518134246122, (18.0, 8.0): 0.00677597161132, (15.0, 5.0): 0.00821235141194, (18.0, 23.0): 0.00779286679775, (16.0, 15.0): 0.00769926118955, (12.0, 10.0): 0.0082226873303, (15.0, 15.0): 0.00517371635476, (13.0, 11.0): 0.00660451214721, (18.0, 13.0): 0.00655950145571, (18.0, 24.0): 0.00788999354863, (8.0, 11.0): 0.00515512324912, (9.0, 10.0): 0.00850819132323, (19.0, 20.0): 0.00768536494803, (17.0, 4.0): 0.0085063923798, (14.0, 11.0): 0.00663718829275, (18.0, 3.0): 0.0051542802846, (17.0, 9.0): 0.00852140821242, (18.0, 14.0): 0.00697651224319, (8.0, 8.0): 0.00673395952529, (9.0, 5.0): 0.00842877501207, (11.0, 6.0): 0.00813285570629, }, ]
+18 Stop [{(16.0, 6.0): 0.00825139291049, (11.0, 11.0): 0.0066806141864, (17.0, 7.0): 0.00833564790545, (16.0, 9.0): 0.00837427739301, (14.0, 4.0): 0.00822864264173, (13.0, 4.0): 0.00820163908013, (20.0, 20.0): 0.00774730533344, (18.0, 4.0): 0.00682677861595, (18.0, 19.0): 0.0075810493611, (8.0, 5.0): 0.00677778201268, (10.0, 8.0): 0.00825450847789, (11.0, 5.0): 0.00822220516774, (10.0, 7.0): 0.00822648908953, (16.0, 3.0): 0.00675572041438, (12.0, 6.0): 0.00807008231885, (16.0, 22.0): 0.00783999182177, (13.0, 7.0): 0.00800989980977, (18.0, 9.0): 0.00690748600368, (15.0, 4.0): 0.0083025139554, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00516922311035, (9.0, 3.0): 0.00682222244916, (19.0, 24.0): 0.00799104345455, (12.0, 11.0): 0.00662363981253, (13.0, 10.0): 0.00820015901993, (18.0, 10.0): 0.00712189292024, (17.0, 18.0): 0.00765035316488, (9.0, 9.0): 0.0084202906554, (16.0, 18.0): 0.00771982490715, (11.0, 8.0): 0.00814793401658, (14.0, 8.0): 0.00806729683954, (21.0, 24.0): 0.00540385399776, (20.0, 16.0): 0.00771060754248, (17.0, 8.0): 0.00839207412049, (18.0, 15.0): 0.00980031348712, (8.0, 9.0): 0.00677770689767, (9.0, 4.0): 0.00849123240772, (10.0, 3.0): 0.00675656697629, (16.0, 7.0): 0.00822694622605, (11.0, 10.0): 0.00830172197685, (17.0, 6.0): 0.00835259061705, (16.0, 10.0): 0.00850618197865, (14.0, 5.0): 0.00814397610288, (13.0, 3.0): 0.00660461480707, (18.0, 5.0): 0.00677569724217, (17.0, 11.0): 0.00711548121236, (18.0, 16.0): 0.0100073178251, (8.0, 6.0): 0.00672948270025, (10.0, 9.0): 0.00832475207159, (9.0, 7.0): 0.00832768829751, (11.0, 4.0): 0.0083036611601, (10.0, 4.0): 0.00840235712059, (16.0, 4.0): 0.00840063017284, (12.0, 7.0): 0.00803979160769, (11.0, 9.0): 0.00822070496359, (15.0, 10.0): 0.00834137464711, (14.0, 6.0): 0.00806805784854, (13.0, 6.0): 0.0080407283371, (18.0, 6.0): 0.00673021162449, (17.0, 22.0): 0.00778827162575, (15.0, 7.0): 0.00811633010937, (18.0, 21.0): 0.00766167087388, (8.0, 3.0): 0.00513983667712, (10.0, 10.0): 0.00840144061388, (11.0, 3.0): 0.00668226997111, (12.0, 4.0): 0.00822919673171, (15.0, 9.0): 0.00823735120502, (14.0, 3.0): 0.0066253903456, (13.0, 9.0): 0.00811385053393, (12.0, 8.0): 0.0080685291077, (18.0, 11.0): 0.00746696854255, (18.0, 22.0): 0.0103175273147, (9.0, 8.0): 0.00835426182943, (14.0, 9.0): 0.00814572145119, (12.0, 9.0): 0.00814252304776, (17.0, 15.0): 0.00753890805562, (21.0, 20.0): 0.00518551701743, (18.0, 12.0): 0.00609072902229, (15.0, 11.0): 0.00673115362432, (8.0, 10.0): 0.00682850978417, (9.0, 11.0): 0.00682185486981, (12.0, 3.0): 0.00662562714073, (17.0, 5.0): 0.00841743596216, (16.0, 11.0): 0.00688627177527, (14.0, 10.0): 0.00823721468371, (17.0, 10.0): 0.0087108383164, (15.0, 3.0): 0.00668174954784, (18.0, 17.0): 0.0075320242503, (8.0, 7.0): 0.00670990356366, (9.0, 6.0): 0.00835437318128, (11.0, 7.0): 0.00811926216637, (10.0, 5.0): 0.00832540112175, (16.0, 5.0): 0.00832244046077, (20.0, 24.0): 0.00806972549164, (19.0, 16.0): 0.00762225805882, (16.0, 8.0): 0.00826996483894, (14.0, 7.0): 0.00803731308394, (13.0, 5.0): 0.00811659638712, (18.0, 20.0): 0.0101694073368, (18.0, 7.0): 0.00672325019396, (15.0, 6.0): 0.00814533732724, (18.0, 18.0): 0.0101018127286, (8.0, 4.0): 0.00682863421858, (10.0, 11.0): 0.00675569701793, (13.0, 8.0): 0.00803867835232, (10.0, 6.0): 0.00825484066029, (15.0, 22.0): 0.00524406360181, (12.0, 5.0): 0.00814514897285, (17.0, 3.0): 0.00682106352134, (15.0, 8.0): 0.00815046161646, (21.0, 16.0): 0.00516674887053, (18.0, 8.0): 0.00677836054959, (15.0, 5.0): 0.00821996454213, (18.0, 23.0): 0.00780318039702, (16.0, 15.0): 0.00767248444259, (12.0, 10.0): 0.00822627386168, (15.0, 15.0): 0.0051532785739, (13.0, 11.0): 0.00660547203452, (18.0, 13.0): 0.00654648793924, (18.0, 24.0): 0.00789349160934, (8.0, 11.0): 0.00513970472522, (9.0, 10.0): 0.00849087917503, (19.0, 20.0): 0.00768601574244, (17.0, 4.0): 0.0084890796296, (14.0, 11.0): 0.00663900082813, (18.0, 3.0): 0.00513880118816, (17.0, 9.0): 0.00852434528593, (18.0, 14.0): 0.00696528635671, (8.0, 8.0): 0.00672944863016, (9.0, 5.0): 0.00842052199882, (11.0, 6.0): 0.00814874708098, }, ]
+19 Stop [{(16.0, 6.0): 0.00825974150832, (11.0, 11.0): 0.00667533214959, (17.0, 7.0): 0.0083422643223, (16.0, 9.0): 0.00838242414031, (14.0, 4.0): 0.00823170194243, (13.0, 4.0): 0.0082063686699, (20.0, 20.0): 0.00773719886734, (18.0, 4.0): 0.00681136861984, (18.0, 19.0): 0.00759482147006, (8.0, 5.0): 0.00676807913264, (10.0, 8.0): 0.008261589097, (11.0, 5.0): 0.00822903270068, (10.0, 7.0): 0.00823655773832, (16.0, 3.0): 0.00674475940546, (12.0, 6.0): 0.00808889966349, (16.0, 22.0): 0.00783145295007, (13.0, 7.0): 0.00803328223816, (18.0, 9.0): 0.00690680392556, (15.0, 4.0): 0.00830078764938, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00515788652422, (9.0, 3.0): 0.00680622273028, (19.0, 24.0): 0.00798475351851, (12.0, 11.0): 0.0066226862807, (13.0, 10.0): 0.00820686762848, (18.0, 10.0): 0.0071162545299, (17.0, 18.0): 0.00764884587283, (9.0, 9.0): 0.00841246347071, (16.0, 18.0): 0.00770800424585, (11.0, 8.0): 0.00816218774643, (14.0, 8.0): 0.00808789426869, (21.0, 24.0): 0.00539183549609, (20.0, 16.0): 0.0076943296357, (17.0, 8.0): 0.00839899656756, (18.0, 15.0): 0.00978663929882, (8.0, 9.0): 0.00676797445908, (9.0, 4.0): 0.00847553647236, (10.0, 3.0): 0.00674573627326, (16.0, 7.0): 0.00824005639806, (11.0, 10.0): 0.0083001818298, (17.0, 6.0): 0.00835396638515, (16.0, 10.0): 0.00850810241085, (14.0, 5.0): 0.00815544750448, (13.0, 3.0): 0.00660423588938, (18.0, 5.0): 0.00676665906309, (17.0, 11.0): 0.00710934804583, (18.0, 16.0): 0.0100033352644, (8.0, 6.0): 0.0067251667054, (10.0, 9.0): 0.00832433935647, (9.0, 7.0): 0.00833003837046, (11.0, 4.0): 0.0083020515288, (10.0, 4.0): 0.0083936721061, (16.0, 4.0): 0.00839186294732, (12.0, 7.0): 0.00806151300208, (11.0, 9.0): 0.00822752721527, (15.0, 10.0): 0.00834721290898, (14.0, 6.0): 0.00808708253994, (13.0, 6.0): 0.00806107294027, (18.0, 6.0): 0.00672780788856, (17.0, 22.0): 0.00778880297785, (15.0, 7.0): 0.00813527767261, (18.0, 21.0): 0.00767562395418, (8.0, 3.0): 0.00512599305931, (10.0, 10.0): 0.00839268302195, (11.0, 3.0): 0.00667684825405, (12.0, 4.0): 0.00823233597414, (15.0, 9.0): 0.00824983726256, (14.0, 3.0): 0.00662366720347, (13.0, 9.0): 0.00812818648103, (12.0, 8.0): 0.00808749122641, (18.0, 11.0): 0.00745632867622, (18.0, 22.0): 0.0103304227942, (9.0, 8.0): 0.00835371200958, (14.0, 9.0): 0.00816028694268, (12.0, 9.0): 0.00815437630293, (17.0, 15.0): 0.00752054253785, (21.0, 20.0): 0.00517519361986, (18.0, 12.0): 0.00607914778948, (15.0, 11.0): 0.00673238148635, (8.0, 10.0): 0.00681296491387, (9.0, 11.0): 0.00680579871535, (12.0, 3.0): 0.00662396732607, (17.0, 5.0): 0.00841023364446, (16.0, 11.0): 0.00688446304872, (14.0, 10.0): 0.00824464416742, (17.0, 10.0): 0.00870761664935, (15.0, 3.0): 0.00667621786803, (18.0, 17.0): 0.00753795738852, (8.0, 7.0): 0.00670774638302, (9.0, 6.0): 0.00835385550264, (11.0, 7.0): 0.00813644479223, (10.0, 5.0): 0.00832506521384, (16.0, 5.0): 0.00832237280968, (20.0, 24.0): 0.00805551664761, (19.0, 16.0): 0.0076127846567, (16.0, 8.0): 0.00828274483899, (14.0, 7.0): 0.00805977953823, (13.0, 5.0): 0.00812961777602, (18.0, 20.0): 0.0101852638267, (18.0, 7.0): 0.0067250851731, (15.0, 6.0): 0.00816021654755, (18.0, 18.0): 0.0101132621076, (8.0, 4.0): 0.00681312943173, (10.0, 11.0): 0.00674482964131, (13.0, 8.0): 0.00805965092865, (10.0, 6.0): 0.00826197022676, (15.0, 22.0): 0.00523536240816, (12.0, 5.0): 0.00815664591566, (17.0, 3.0): 0.00680494563924, (15.0, 8.0): 0.00816828092187, (21.0, 16.0): 0.00515357694943, (18.0, 8.0): 0.0067806890109, (15.0, 5.0): 0.00822684647768, (18.0, 23.0): 0.0078116058308, (16.0, 15.0): 0.00764710345302, (12.0, 10.0): 0.00823004553438, (15.0, 15.0): 0.00513413410114, (13.0, 11.0): 0.00660705997278, (18.0, 13.0): 0.00653416777275, (18.0, 24.0): 0.00789590515364, (8.0, 11.0): 0.0051258260719, (9.0, 10.0): 0.00847511325236, (19.0, 20.0): 0.00768679219283, (17.0, 4.0): 0.00847338968725, (14.0, 11.0): 0.00664134955848, (18.0, 3.0): 0.00512489426371, (17.0, 9.0): 0.00852717852408, (18.0, 14.0): 0.0069540031371, (8.0, 8.0): 0.00672511713876, (9.0, 5.0): 0.00841275124508, (11.0, 6.0): 0.00816302747885, }, ]
+20 Stop [{(16.0, 6.0): 0.00826727072975, (11.0, 11.0): 0.00667074838386, (17.0, 7.0): 0.00834832802789, (16.0, 9.0): 0.00839005743536, (14.0, 4.0): 0.00823477795411, (13.0, 4.0): 0.00821106384484, (20.0, 20.0): 0.00772892716332, (18.0, 4.0): 0.00679748294608, (18.0, 19.0): 0.00760623864025, (8.0, 5.0): 0.00675914406646, (10.0, 8.0): 0.00826767718956, (11.0, 5.0): 0.00823516456757, (10.0, 7.0): 0.00824532004496, (16.0, 3.0): 0.00673485331765, (12.0, 6.0): 0.00810623180007, (16.0, 22.0): 0.00782443318006, (13.0, 7.0): 0.00805505972948, (18.0, 9.0): 0.00690637257141, (15.0, 4.0): 0.00829929427037, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00514827801073, (9.0, 3.0): 0.00679176139846, (19.0, 24.0): 0.00797872510658, (12.0, 11.0): 0.00662227870764, (13.0, 10.0): 0.00821371375546, (18.0, 10.0): 0.00711137011279, (17.0, 18.0): 0.00764726556646, (9.0, 9.0): 0.00840511923259, (16.0, 18.0): 0.00769789330167, (11.0, 8.0): 0.00817504801547, (14.0, 8.0): 0.00810717852002, (21.0, 24.0): 0.00538108996392, (20.0, 16.0): 0.00767915990551, (17.0, 8.0): 0.00840540910331, (18.0, 15.0): 0.00977234219912, (8.0, 9.0): 0.00675900682207, (9.0, 4.0): 0.00846123000521, (10.0, 3.0): 0.00673593623562, (16.0, 7.0): 0.00825201694806, (11.0, 10.0): 0.00829892055768, (17.0, 6.0): 0.00835519314419, (16.0, 10.0): 0.00851018698408, (14.0, 5.0): 0.00816613924811, (13.0, 3.0): 0.00660424133871, (18.0, 5.0): 0.00675850562176, (17.0, 11.0): 0.00710405827256, (18.0, 16.0): 0.00999774098922, (8.0, 6.0): 0.00672101915579, (10.0, 9.0): 0.00832372043228, (9.0, 7.0): 0.00833176931995, (11.0, 4.0): 0.00830063052546, (10.0, 4.0): 0.00838569913253, (16.0, 4.0): 0.00838387247009, (12.0, 7.0): 0.00808152618448, (11.0, 9.0): 0.00823372249018, (15.0, 10.0): 0.00835305472155, (14.0, 6.0): 0.0081047198141, (13.0, 6.0): 0.00807999103158, (18.0, 6.0): 0.00672568130822, (17.0, 22.0): 0.00778935767453, (15.0, 7.0): 0.00815272221566, (18.0, 21.0): 0.00768746297329, (8.0, 3.0): 0.0051135023936, (10.0, 10.0): 0.00838467090244, (11.0, 3.0): 0.00667204826911, (12.0, 4.0): 0.00823547224922, (15.0, 9.0): 0.00826160843528, (14.0, 3.0): 0.00662237062871, (13.0, 9.0): 0.00814187365675, (12.0, 8.0): 0.0081050438413, (18.0, 11.0): 0.00744686540948, (18.0, 22.0): 0.0103412832862, (9.0, 8.0): 0.00835283987424, (14.0, 9.0): 0.00817416982447, (12.0, 9.0): 0.008165525352, (17.0, 15.0): 0.00750254182166, (21.0, 20.0): 0.00516666309905, (18.0, 12.0): 0.0060685206898, (15.0, 11.0): 0.00673399110519, (8.0, 10.0): 0.00679886618434, (9.0, 11.0): 0.00679128843027, (12.0, 3.0): 0.0066227300622, (17.0, 5.0): 0.00840365727108, (16.0, 11.0): 0.0068831686274, (14.0, 10.0): 0.00825213971913, (17.0, 10.0): 0.00870498016079, (15.0, 3.0): 0.00667131864912, (18.0, 17.0): 0.00754180180584, (8.0, 7.0): 0.00670551523089, (9.0, 6.0): 0.00835301474534, (11.0, 7.0): 0.00815194615158, (10.0, 5.0): 0.0083244982985, (16.0, 5.0): 0.00832221147749, (20.0, 24.0): 0.00804267447009, (19.0, 16.0): 0.0076032052469, (16.0, 8.0): 0.00829450057336, (14.0, 7.0): 0.00808066325153, (13.0, 5.0): 0.00814183056127, (18.0, 20.0): 0.0101987284957, (18.0, 7.0): 0.0067268483826, (15.0, 6.0): 0.00817383294922, (18.0, 18.0): 0.0101221904374, (8.0, 4.0): 0.00679907378867, (10.0, 11.0): 0.00673502673095, (13.0, 8.0): 0.00807930102859, (10.0, 6.0): 0.00826809523208, (15.0, 22.0): 0.00522816552077, (12.0, 5.0): 0.008167306406, (17.0, 3.0): 0.00679040228653, (15.0, 8.0): 0.00818480699294, (21.0, 16.0): 0.00514156501995, (18.0, 8.0): 0.0067829438409, (15.0, 5.0): 0.00823313419775, (18.0, 23.0): 0.00781844269337, (16.0, 15.0): 0.00762294904753, (12.0, 10.0): 0.00823396582929, (15.0, 15.0): 0.00511610153491, (13.0, 11.0): 0.00660914747869, (18.0, 13.0): 0.00652243956644, (18.0, 24.0): 0.00789742150098, (8.0, 11.0): 0.00511329959794, (9.0, 10.0): 0.00846074285631, (19.0, 20.0): 0.00768764631006, (17.0, 4.0): 0.00845917582057, (14.0, 11.0): 0.00664412658789, (18.0, 3.0): 0.00511237665267, (17.0, 9.0): 0.00852994415765, (18.0, 14.0): 0.00694271679465, (8.0, 8.0): 0.00672095189713, (9.0, 5.0): 0.00840546146994, (11.0, 6.0): 0.0081758749724, }, ]
+21 Stop [{(16.0, 6.0): 0.00827410504974, (11.0, 11.0): 0.00666679756715, (17.0, 7.0): 0.00835390154034, (16.0, 9.0): 0.00839725951714, (14.0, 4.0): 0.00823784772066, (13.0, 4.0): 0.00821568925656, (20.0, 20.0): 0.00772218937398, (18.0, 4.0): 0.00678495785697, (18.0, 19.0): 0.00761564261335, (8.0, 5.0): 0.00675090154672, (10.0, 8.0): 0.0082729211113, (11.0, 5.0): 0.00824069495398, (10.0, 7.0): 0.00825296158763, (16.0, 3.0): 0.00672591805734, (12.0, 6.0): 0.0081221860789, (16.0, 22.0): 0.00781867971192, (13.0, 7.0): 0.00807530824513, (18.0, 9.0): 0.00690616046281, (15.0, 4.0): 0.00829804544074, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00514010343926, (9.0, 3.0): 0.00677867120743, (19.0, 24.0): 0.00797294035922, (12.0, 11.0): 0.00662233680841, (13.0, 10.0): 0.0082206254618, (18.0, 10.0): 0.00710714805558, (17.0, 18.0): 0.00764560056539, (9.0, 9.0): 0.0083982361846, (16.0, 18.0): 0.00768919196141, (11.0, 8.0): 0.00818668753762, (14.0, 8.0): 0.00812522392351, (21.0, 24.0): 0.00537143647199, (20.0, 16.0): 0.00766490422744, (17.0, 8.0): 0.00841137233267, (18.0, 15.0): 0.00975760700252, (8.0, 9.0): 0.0067507300724, (9.0, 4.0): 0.00844818691832, (10.0, 3.0): 0.0067270763023, (16.0, 7.0): 0.00826296769894, (11.0, 10.0): 0.00829794305188, (17.0, 6.0): 0.00835631016164, (16.0, 10.0): 0.0085124480172, (14.0, 5.0): 0.00817612035507, (13.0, 3.0): 0.00660454827637, (18.0, 5.0): 0.00675114892323, (17.0, 11.0): 0.00709951910952, (18.0, 16.0): 0.00999085648133, (8.0, 6.0): 0.00671702256235, (10.0, 9.0): 0.00832298204941, (9.0, 7.0): 0.00833296760462, (11.0, 4.0): 0.00829940536223, (10.0, 4.0): 0.00837839565124, (16.0, 4.0): 0.00837662413712, (12.0, 7.0): 0.00809996154138, (11.0, 9.0): 0.00823938736952, (15.0, 10.0): 0.0083588957483, (14.0, 6.0): 0.00812106925891, (13.0, 6.0): 0.0080975665873, (18.0, 6.0): 0.00672379745698, (17.0, 22.0): 0.00778991777307, (15.0, 7.0): 0.00816880847148, (18.0, 21.0): 0.00769749060323, (8.0, 3.0): 0.00510220959465, (10.0, 10.0): 0.00837736763248, (11.0, 3.0): 0.00666780474682, (12.0, 4.0): 0.00823857712065, (15.0, 9.0): 0.00827273948192, (14.0, 3.0): 0.00662143824495, (13.0, 9.0): 0.00815491672346, (12.0, 8.0): 0.00812128888437, (18.0, 11.0): 0.00743841367864, (18.0, 22.0): 0.0103504086019, (9.0, 8.0): 0.00835171909755, (14.0, 9.0): 0.00818739403113, (12.0, 9.0): 0.00817602623391, (17.0, 15.0): 0.00748491583951, (21.0, 20.0): 0.00515964060396, (18.0, 12.0): 0.00605870310445, (15.0, 11.0): 0.00673593252443, (8.0, 10.0): 0.00678605002218, (9.0, 11.0): 0.00677816056088, (12.0, 3.0): 0.00662184936735, (17.0, 5.0): 0.00839767394811, (16.0, 11.0): 0.0068823418981, (14.0, 10.0): 0.0082596472511, (17.0, 10.0): 0.00870287935684, (15.0, 3.0): 0.00666699450294, (18.0, 17.0): 0.00754391679193, (8.0, 7.0): 0.00670322543494, (9.0, 6.0): 0.00835192294241, (11.0, 7.0): 0.00816594307378, (10.0, 5.0): 0.00832378374012, (16.0, 5.0): 0.00832202922923, (20.0, 24.0): 0.00803101150752, (19.0, 16.0): 0.00759355696478, (16.0, 8.0): 0.00830535821061, (14.0, 7.0): 0.00810006870616, (13.0, 5.0): 0.00815326621836, (18.0, 20.0): 0.0102101314318, (18.0, 7.0): 0.00672853398851, (15.0, 6.0): 0.0081863359813, (18.0, 18.0): 0.0101289829469, (8.0, 4.0): 0.00678630126269, (10.0, 11.0): 0.00672620006676, (13.0, 8.0): 0.00809769135523, (10.0, 6.0): 0.00827336065866, (15.0, 22.0): 0.00522222715374, (12.0, 5.0): 0.00817720111682, (17.0, 3.0): 0.00677727461605, (15.0, 8.0): 0.00820016334745, (21.0, 16.0): 0.00513050247848, (18.0, 8.0): 0.00678512301939, (15.0, 5.0): 0.00823892242859, (18.0, 23.0): 0.00782394221966, (16.0, 15.0): 0.00759988105718, (12.0, 10.0): 0.0082379947758, (15.0, 15.0): 0.0050990337833, (13.0, 11.0): 0.00661163094465, (18.0, 13.0): 0.00651122568363, (18.0, 24.0): 0.00789819643365, (8.0, 11.0): 0.00510197185297, (9.0, 10.0): 0.00844764525192, (19.0, 20.0): 0.00768853994838, (17.0, 4.0): 0.0084463124205, (14.0, 11.0): 0.00664724423677, (18.0, 3.0): 0.00510109685904, (17.0, 9.0): 0.00853267131427, (18.0, 14.0): 0.00693147100348, (8.0, 8.0): 0.00671693646237, (9.0, 5.0): 0.00839862692041, (11.0, 6.0): 0.00818746254474, }, ]
+22 Stop [{(16.0, 6.0): 0.00828034962417, (11.0, 11.0): 0.00666342222096, (17.0, 7.0): 0.00835904384384, (16.0, 9.0): 0.00840409530823, (14.0, 4.0): 0.00824090011585, (13.0, 4.0): 0.00822021313234, (20.0, 20.0): 0.0077167300761, (18.0, 4.0): 0.00677365479883, (18.0, 19.0): 0.00762332613244, (8.0, 5.0): 0.00674328172702, (10.0, 8.0): 0.0082774542767, (11.0, 5.0): 0.00824570954358, (10.0, 7.0): 0.0082596308072, (16.0, 3.0): 0.00671787162151, (12.0, 6.0): 0.00813687557383, (16.0, 22.0): 0.0078139794052, (13.0, 7.0): 0.00809411928704, (18.0, 9.0): 0.0069061421473, (15.0, 4.0): 0.00829703657116, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00513311570676, (9.0, 3.0): 0.00676681079265, (19.0, 24.0): 0.00796738276679, (12.0, 11.0): 0.00662279028521, (13.0, 10.0): 0.00822754457859, (18.0, 10.0): 0.00710350642062, (17.0, 18.0): 0.00764384324565, (9.0, 9.0): 0.0083917990348, (16.0, 18.0): 0.00768164922856, (11.0, 8.0): 0.00819724559532, (14.0, 8.0): 0.0081421082727, (21.0, 24.0): 0.00536272207183, (20.0, 16.0): 0.00765140496998, (17.0, 8.0): 0.00841694143443, (18.0, 15.0): 0.00974257815196, (8.0, 9.0): 0.00674307637616, (9.0, 4.0): 0.00843628501553, (10.0, 3.0): 0.00671906719439, (16.0, 7.0): 0.00827302819422, (11.0, 10.0): 0.00829723795772, (17.0, 6.0): 0.00835734750421, (16.0, 10.0): 0.00851488200242, (14.0, 5.0): 0.00818544519632, (13.0, 3.0): 0.00660509682348, (18.0, 5.0): 0.00674451084892, (17.0, 11.0): 0.00709564454293, (18.0, 16.0): 0.00998294045653, (8.0, 6.0): 0.00671317197449, (10.0, 9.0): 0.00832217886946, (9.0, 7.0): 0.00833372060518, (11.0, 4.0): 0.00829836580433, (10.0, 4.0): 0.00837172340996, (16.0, 4.0): 0.00837008175985, (12.0, 7.0): 0.00811693756471, (11.0, 9.0): 0.00824460524847, (15.0, 10.0): 0.00836472923081, (14.0, 6.0): 0.00813623217774, (13.0, 6.0): 0.00811387927772, (18.0, 6.0): 0.00672213212451, (17.0, 22.0): 0.00779046797881, (15.0, 7.0): 0.00818366884107, (18.0, 21.0): 0.00770596520951, (8.0, 3.0): 0.00509197964908, (10.0, 10.0): 0.00837073761383, (11.0, 3.0): 0.00666406367656, (12.0, 4.0): 0.00824163691309, (15.0, 9.0): 0.00828329042519, (14.0, 3.0): 0.0066208148002, (13.0, 9.0): 0.0081673307611, (12.0, 8.0): 0.0081363311105, (18.0, 11.0): 0.00743083791242, (18.0, 22.0): 0.0103580523491, (9.0, 8.0): 0.00835040291521, (14.0, 9.0): 0.00819998428222, (12.0, 9.0): 0.00818592279741, (17.0, 15.0): 0.00746766738286, (21.0, 20.0): 0.00515388342664, (18.0, 12.0): 0.00604957968235, (15.0, 11.0): 0.00673815881448, (8.0, 10.0): 0.00677438135835, (9.0, 11.0): 0.00676627649162, (12.0, 3.0): 0.00662126602177, (17.0, 5.0): 0.0083922523827, (16.0, 11.0): 0.00688193798645, (14.0, 10.0): 0.00826712355766, (17.0, 10.0): 0.00870126652894, (15.0, 3.0): 0.00666319678946, (18.0, 17.0): 0.00754459878769, (8.0, 7.0): 0.00670088963584, (9.0, 6.0): 0.00835063126581, (11.0, 7.0): 0.00817860325703, (10.0, 5.0): 0.00832297238488, (16.0, 5.0): 0.00832187095856, (20.0, 24.0): 0.00802036885824, (19.0, 16.0): 0.00758386785107, (16.0, 8.0): 0.00831542422136, (14.0, 7.0): 0.00811809572104, (13.0, 5.0): 0.00816396870682, (18.0, 20.0): 0.0102197572463, (18.0, 7.0): 0.00673014392429, (15.0, 6.0): 0.008197848238, (18.0, 18.0): 0.0101339657269, (8.0, 4.0): 0.00677467461757, (10.0, 11.0): 0.00671826307519, (13.0, 8.0): 0.00811488582634, (10.0, 6.0): 0.00827789829471, (15.0, 22.0): 0.00521734014751, (12.0, 5.0): 0.00818638509775, (17.0, 3.0): 0.00676542627213, (15.0, 8.0): 0.00821445868699, (21.0, 16.0): 0.00512021931505, (18.0, 8.0): 0.00678722883421, (15.0, 5.0): 0.00824429068699, (18.0, 23.0): 0.00782831503492, (16.0, 15.0): 0.00757778252388, (12.0, 10.0): 0.00824210210678, (15.0, 15.0): 0.00508281057738, (13.0, 11.0): 0.00661442808981, (18.0, 13.0): 0.00650046659719, (18.0, 24.0): 0.00789835967084, (8.0, 11.0): 0.00509170993009, (9.0, 10.0): 0.00843570245957, (19.0, 20.0): 0.00768944263207, (17.0, 4.0): 0.0084346802194, (14.0, 11.0): 0.00665063137668, (18.0, 3.0): 0.00509092373794, (17.0, 9.0): 0.00853537661989, (18.0, 14.0): 0.00692030064633, (8.0, 8.0): 0.00671306681194, (9.0, 5.0): 0.00839222949093, (11.0, 6.0): 0.00819792946201, }, ]
+23 Stop [{(16.0, 6.0): 0.00828608890383, (11.0, 11.0): 0.00666056648688, (17.0, 7.0): 0.00836380817641, (16.0, 9.0): 0.00841061371542, (14.0, 4.0): 0.00824392270318, (13.0, 4.0): 0.00822461797949, (20.0, 20.0): 0.00771233261605, (18.0, 4.0): 0.00676345203513, (18.0, 19.0): 0.00762953945411, (8.0, 5.0): 0.00673622797796, (10.0, 8.0): 0.00828138249278, (11.0, 5.0): 0.00825027245851, (10.0, 7.0): 0.00826546144816, (16.0, 3.0): 0.00671064002274, (12.0, 6.0): 0.0081504013952, (16.0, 22.0): 0.00781015253509, (13.0, 7.0): 0.00811158353537, (18.0, 9.0): 0.00690629467451, (15.0, 4.0): 0.00829626102413, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00512710759624, (9.0, 3.0): 0.00675605304956, (19.0, 24.0): 0.00796203709862, (12.0, 11.0): 0.00662358057035, (13.0, 10.0): 0.00823442722328, (18.0, 10.0): 0.00710037492587, (17.0, 18.0): 0.00764198892314, (9.0, 9.0): 0.00838578574985, (16.0, 18.0): 0.00767505534478, (11.0, 8.0): 0.0082068478976, (14.0, 8.0): 0.00815790655786, (21.0, 24.0): 0.005354817322, (20.0, 16.0): 0.00763853393121, (17.0, 8.0): 0.00842216443246, (18.0, 15.0): 0.00972736899519, (8.0, 9.0): 0.00673599094357, (9.0, 4.0): 0.00842541893584, (10.0, 3.0): 0.00671183009789, (16.0, 7.0): 0.00828230294493, (11.0, 10.0): 0.0082967921406, (17.0, 6.0): 0.00835833170211, (16.0, 10.0): 0.00851747911069, (14.0, 5.0): 0.00819416737674, (13.0, 3.0): 0.00660583703783, (18.0, 5.0): 0.0067385249196, (17.0, 11.0): 0.00709235841624, (18.0, 16.0): 0.00997420186504, (8.0, 6.0): 0.0067094620875, (10.0, 9.0): 0.00832135500865, (9.0, 7.0): 0.00833409952764, (11.0, 4.0): 0.00829750305333, (10.0, 4.0): 0.00836563612154, (16.0, 4.0): 0.00836420180717, (12.0, 7.0): 0.00813257335862, (11.0, 9.0): 0.00824943809368, (15.0, 10.0): 0.00837054474684, (14.0, 6.0): 0.00815030012216, (13.0, 6.0): 0.00812901500463, (18.0, 6.0): 0.00672066622527, (17.0, 22.0): 0.00779099554862, (15.0, 7.0): 0.00819741993626, (18.0, 21.0): 0.00771310746869, (8.0, 3.0): 0.00508269790225, (10.0, 10.0): 0.00836473714892, (11.0, 3.0): 0.00666077238405, (12.0, 4.0): 0.00824463669494, (15.0, 9.0): 0.00829331158669, (14.0, 3.0): 0.00662045712645, (13.0, 9.0): 0.00817913364913, (12.0, 8.0): 0.00815026457886, (18.0, 11.0): 0.00742402377978, (18.0, 22.0): 0.0103644291617, (9.0, 8.0): 0.00834894206936, (14.0, 9.0): 0.00821196745977, (12.0, 9.0): 0.00819525840485, (17.0, 15.0): 0.0074507945069, (21.0, 20.0): 0.00514918507202, (18.0, 12.0): 0.00604105823795, (15.0, 11.0): 0.00674062789057, (8.0, 10.0): 0.0067637415689, (9.0, 11.0): 0.00675551202698, (12.0, 3.0): 0.00662093401307, (17.0, 5.0): 0.0083873579252, (16.0, 11.0): 0.00688191173645, (14.0, 10.0): 0.00827453417403, (17.0, 10.0): 0.00870009277114, (15.0, 3.0): 0.00665987811702, (18.0, 17.0): 0.00754409280843, (8.0, 7.0): 0.0066985262266, (9.0, 6.0): 0.00834918892495, (11.0, 7.0): 0.00819006933725, (10.0, 5.0): 0.00832210662481, (16.0, 5.0): 0.00832176908245, (20.0, 24.0): 0.00801061157759, (19.0, 16.0): 0.00757415938782, (16.0, 8.0): 0.00832478956905, (14.0, 7.0): 0.00813484485992, (13.0, 5.0): 0.00817397828219, (18.0, 20.0): 0.0102278506362, (18.0, 7.0): 0.00673168498952, (15.0, 6.0): 0.00820847791359, (18.0, 18.0): 0.0101374141537, (8.0, 4.0): 0.00676407263895, (10.0, 11.0): 0.00671113796971, (13.0, 8.0): 0.00813095505154, (10.0, 6.0): 0.00828181244292, (15.0, 22.0): 0.00521332987549, (12.0, 5.0): 0.00819491516701, (17.0, 3.0): 0.0067547350966, (15.0, 8.0): 0.00822779008946, (21.0, 16.0): 0.00511057798085, (18.0, 8.0): 0.00678926701333, (15.0, 5.0): 0.0082492983302, (18.0, 23.0): 0.0078317379892, (16.0, 15.0): 0.0075565552576, (12.0, 10.0): 0.0082462590594, (15.0, 15.0): 0.00506733279665, (13.0, 11.0): 0.00661747135364, (18.0, 13.0): 0.00649011564196, (18.0, 24.0): 0.00789801915752, (8.0, 11.0): 0.00508240110585, (9.0, 10.0): 0.00842481228413, (19.0, 20.0): 0.00769033021429, (17.0, 4.0): 0.00842417314013, (14.0, 11.0): 0.00665422928178, (18.0, 3.0): 0.00508174484705, (17.0, 9.0): 0.00853807151512, (18.0, 14.0): 0.00690923361916, (8.0, 8.0): 0.00670933878903, (9.0, 5.0): 0.00838624406319, (11.0, 6.0): 0.00820740322623, }, ]
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+28 Stop [{(16.0, 6.0): 0.0083093228017, (11.0, 11.0): 0.00665243108263, (17.0, 7.0): 0.00838346105257, (16.0, 9.0): 0.0084395353925, (14.0, 4.0): 0.00825837182319, (13.0, 4.0): 0.008244566844, (20.0, 20.0): 0.00770075458869, (18.0, 4.0): 0.00672563927861, (18.0, 19.0): 0.00764500566671, (8.0, 5.0): 0.00670789111588, (10.0, 8.0): 0.0082946903069, (11.0, 5.0): 0.00826798127577, (10.0, 7.0): 0.00828547205306, (16.0, 3.0): 0.0066844600799, (12.0, 6.0): 0.00820374473138, (16.0, 22.0): 0.00779958014661, (13.0, 7.0): 0.00818185103991, (18.0, 9.0): 0.00690900928907, (15.0, 4.0): 0.00829541214975, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00510659202339, (9.0, 3.0): 0.00671532142558, (19.0, 24.0): 0.00793803096305, (12.0, 11.0): 0.00663101748761, (13.0, 10.0): 0.00826733422655, (18.0, 10.0): 0.00709046982912, (17.0, 18.0): 0.0076312144023, (9.0, 9.0): 0.00836137689572, (16.0, 18.0): 0.00765111498635, (11.0, 8.0): 0.00824382278365, (14.0, 8.0): 0.00822307416454, (21.0, 24.0): 0.00532412701379, (20.0, 16.0): 0.00758055665432, (17.0, 8.0): 0.00844438309289, (18.0, 15.0): 0.00965115856717, (8.0, 9.0): 0.00670759366976, (9.0, 4.0): 0.00838355936441, (10.0, 3.0): 0.00668487294502, (16.0, 7.0): 0.00831966414616, (11.0, 10.0): 0.00829778237136, (17.0, 6.0): 0.00836308254134, (16.0, 10.0): 0.00853231823777, (14.0, 5.0): 0.00823025929212, (13.0, 3.0): 0.00661129102275, (18.0, 5.0): 0.00671655017823, (17.0, 11.0): 0.00708259769427, (18.0, 16.0): 0.00992311523443, (8.0, 6.0): 0.00669299535928, (10.0, 9.0): 0.00831771640718, (9.0, 7.0): 0.00833237430182, (11.0, 4.0): 0.00829535043859, (10.0, 4.0): 0.00834248704725, (16.0, 4.0): 0.00834323098842, (12.0, 7.0): 0.00819425829789, (11.0, 9.0): 0.00826934705236, (15.0, 10.0): 0.00839895068113, (14.0, 6.0): 0.00820706782472, (13.0, 6.0): 0.00818982419382, (18.0, 6.0): 0.00671590323505, (17.0, 22.0): 0.00779299050811, (15.0, 7.0): 0.00825285386723, (18.0, 21.0): 0.00773454160327, (8.0, 3.0): 0.00504741478713, (10.0, 10.0): 0.00834270038629, (11.0, 3.0): 0.00664957674002, (12.0, 4.0): 0.00825850145885, (15.0, 9.0): 0.00833677532403, (14.0, 3.0): 0.00662152928163, (13.0, 9.0): 0.00822983749522, (12.0, 8.0): 0.00820622948934, (18.0, 11.0): 0.00739847656665, (18.0, 22.0): 0.0103827954451, (9.0, 8.0): 0.00834069575029, (14.0, 9.0): 0.00826373733991, (12.0, 9.0): 0.00823476710794, (17.0, 15.0): 0.00737183234833, (21.0, 20.0): 0.00513628650575, (18.0, 12.0): 0.00600527694154, (15.0, 11.0): 0.00675538851763, (8.0, 10.0): 0.00672282907849, (9.0, 11.0): 0.00671506899915, (12.0, 3.0): 0.00662185199792, (17.0, 5.0): 0.00836966929306, (16.0, 11.0): 0.00688603573782, (14.0, 10.0): 0.00830983386226, (17.0, 10.0): 0.00869929661376, (15.0, 3.0): 0.00664908502389, (18.0, 17.0): 0.00752974565398, (8.0, 7.0): 0.006686855173, (9.0, 6.0): 0.00834090214095, (11.0, 7.0): 0.00823351837381, (10.0, 5.0): 0.00831773483193, (16.0, 5.0): 0.00832257804435, (20.0, 24.0): 0.00797162961461, (19.0, 16.0): 0.0075258036408, (16.0, 8.0): 0.00836348435485, (14.0, 7.0): 0.00820258154658, (13.0, 5.0): 0.00821511228073, (18.0, 20.0): 0.0102517175657, (18.0, 7.0): 0.00673868929647, (15.0, 6.0): 0.00825124721726, (18.0, 18.0): 0.0101386126156, (8.0, 4.0): 0.00672320574078, (10.0, 11.0): 0.00668533870659, (13.0, 8.0): 0.00819683077531, (10.0, 6.0): 0.00829476270634, (15.0, 22.0): 0.00520201423336, (12.0, 5.0): 0.00822939842165, (17.0, 3.0): 0.00671523119854, (15.0, 8.0): 0.00828270511853, (21.0, 16.0): 0.00506875083934, (18.0, 8.0): 0.00679869491257, (15.0, 5.0): 0.00827035840918, (18.0, 23.0): 0.00783905453812, (16.0, 15.0): 0.00746097772565, (12.0, 10.0): 0.00826707866893, (15.0, 15.0): 0.00499866686588, (13.0, 11.0): 0.00663474778239, (18.0, 13.0): 0.00644340144226, (18.0, 24.0): 0.00789142804661, (8.0, 11.0): 0.00504712086769, (9.0, 10.0): 0.00838317423961, (19.0, 20.0): 0.00769400045957, (17.0, 4.0): 0.00838539623721, (14.0, 11.0): 0.0066739137823, (18.0, 3.0): 0.0050477039463, (17.0, 9.0): 0.00855151654796, (18.0, 14.0): 0.00685607114256, (8.0, 8.0): 0.00669283090078, (9.0, 5.0): 0.00836172438756, (11.0, 6.0): 0.00824325538906, }, ]
+29 Stop [{(16.0, 6.0): 0.00831317895016, (11.0, 11.0): 0.00665175329348, (17.0, 7.0): 0.00838679049071, (16.0, 9.0): 0.00844472597142, (14.0, 4.0): 0.00826110434222, (13.0, 4.0): 0.00824813323704, (20.0, 20.0): 0.00769972826896, (18.0, 4.0): 0.00672019459375, (18.0, 19.0): 0.00764591776755, (8.0, 5.0): 0.0067033679315, (10.0, 8.0): 0.00829647946022, (11.0, 5.0): 0.0082707440714, (10.0, 7.0): 0.00828816354839, (16.0, 3.0): 0.00668084027327, (12.0, 6.0): 0.00821209620676, (16.0, 22.0): 0.00779853066825, (13.0, 7.0): 0.0081930691707, (18.0, 9.0): 0.00690984681728, (15.0, 4.0): 0.00829574593008, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00510366767381, (9.0, 3.0): 0.00670923206124, (19.0, 24.0): 0.00793369620809, (12.0, 11.0): 0.00663296482194, (13.0, 10.0): 0.00827350379619, (18.0, 10.0): 0.00708934824396, (17.0, 18.0): 0.00762876295011, (9.0, 9.0): 0.008357491076, (16.0, 18.0): 0.00764740580791, (11.0, 8.0): 0.00824952160121, (14.0, 8.0): 0.00823378578897, (21.0, 24.0): 0.00531927337843, (20.0, 16.0): 0.00756982885138, (17.0, 8.0): 0.0084482427378, (18.0, 15.0): 0.00963620294736, (8.0, 9.0): 0.0067030887914, (9.0, 4.0): 0.00837718595143, (10.0, 3.0): 0.00668094018711, (16.0, 7.0): 0.0083257572445, (11.0, 10.0): 0.00829848946645, (17.0, 6.0): 0.00836408294649, (16.0, 10.0): 0.00853552911949, (14.0, 5.0): 0.00823623392599, (13.0, 3.0): 0.00661258144438, (18.0, 5.0): 0.00671345703158, (17.0, 11.0): 0.00708163682244, (18.0, 16.0): 0.00991208488199, (8.0, 6.0): 0.00669011584023, (10.0, 9.0): 0.00831716620969, (9.0, 7.0): 0.00833160264247, (11.0, 4.0): 0.0082952582291, (10.0, 4.0): 0.00833904457269, (16.0, 4.0): 0.00834043850392, (12.0, 7.0): 0.00820392038646, (11.0, 9.0): 0.0082726871445, (15.0, 10.0): 0.00840442275044, (14.0, 6.0): 0.0082161960149, (13.0, 6.0): 0.00819952001411, (18.0, 6.0): 0.0067154021857, (17.0, 22.0): 0.00779322241284, (15.0, 7.0): 0.00826181037915, (18.0, 21.0): 0.0077368087871, (8.0, 3.0): 0.0050421033873, (10.0, 10.0): 0.00833960935754, (11.0, 3.0): 0.00664814550846, (12.0, 4.0): 0.0082610264321, (15.0, 9.0): 0.00834434077122, (14.0, 3.0): 0.00662215069671, (13.0, 9.0): 0.00823850138899, (12.0, 8.0): 0.00821518169083, (18.0, 11.0): 0.00739464500302, (18.0, 22.0): 0.0103845610778, (9.0, 8.0): 0.00833903517614, (14.0, 9.0): 0.00827265163719, (12.0, 9.0): 0.00824145196276, (17.0, 15.0): 0.00735705966645, (21.0, 20.0): 0.0051350614491, (18.0, 12.0): 0.00599917860293, (15.0, 11.0): 0.00675862464566, (8.0, 10.0): 0.00671661415754, (9.0, 11.0): 0.00670911039692, (12.0, 3.0): 0.00662238023194, (17.0, 5.0): 0.00836728276775, (16.0, 11.0): 0.00688746913498, (14.0, 10.0): 0.00831644966755, (17.0, 10.0): 0.00869989316074, (15.0, 3.0): 0.00664785102631, (18.0, 17.0): 0.0075253471805, (8.0, 7.0): 0.00668464521863, (9.0, 6.0): 0.00833920154715, (11.0, 7.0): 0.00824006537949, (10.0, 5.0): 0.00831693804977, (16.0, 5.0): 0.00832303190734, (20.0, 24.0): 0.00796528369945, (19.0, 16.0): 0.00751623224031, (16.0, 8.0): 0.00836995442099, (14.0, 7.0): 0.0082134856886, (13.0, 5.0): 0.00822183220646, (18.0, 20.0): 0.0102541119679, (18.0, 7.0): 0.00674001407154, (15.0, 6.0): 0.00825817002402, (18.0, 18.0): 0.0101366417282, (8.0, 4.0): 0.00671695768276, (10.0, 11.0): 0.00668174977435, (13.0, 8.0): 0.00820756459286, (10.0, 6.0): 0.00829642542427, (15.0, 22.0): 0.00520086716555, (12.0, 5.0): 0.00823494763368, (17.0, 3.0): 0.00670957004915, (15.0, 8.0): 0.00829177856584, (21.0, 16.0): 0.00506122763778, (18.0, 8.0): 0.00680047499311, (15.0, 5.0): 0.00827397102253, (18.0, 23.0): 0.00783919305618, (16.0, 15.0): 0.0074436034576, (12.0, 10.0): 0.00827114684686, (15.0, 15.0): 0.00498632600816, (13.0, 11.0): 0.00663838660838, (18.0, 13.0): 0.00643491650879, (18.0, 24.0): 0.00788950451593, (8.0, 11.0): 0.00504184814197, (9.0, 10.0): 0.00837692482373, (19.0, 20.0): 0.0076945144075, (17.0, 4.0): 0.00837987692302, (14.0, 11.0): 0.00667797929303, (18.0, 3.0): 0.00504278560139, (17.0, 9.0): 0.00855419865169, (18.0, 14.0): 0.00684594717006, (8.0, 8.0): 0.00668995908594, (9.0, 5.0): 0.00835775692394, (11.0, 6.0): 0.00824865249527, }, ]
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+150 Stop [{(16.0, 6.0): 0.00852103287234, (11.0, 11.0): 0.0067665681387, (17.0, 7.0): 0.00858098182398, (16.0, 9.0): 0.00865340719507, (14.0, 4.0): 0.00844867681546, (13.0, 4.0): 0.0084328302692, (20.0, 20.0): 0.00742224360948, (18.0, 4.0): 0.00679664923463, (18.0, 19.0): 0.00726042479162, (8.0, 5.0): 0.00671146228371, (10.0, 8.0): 0.00841990542359, (11.0, 5.0): 0.00841041630712, (10.0, 7.0): 0.00841234330923, (16.0, 3.0): 0.00677661908917, (12.0, 6.0): 0.00843433407698, (16.0, 22.0): 0.007571746438, (13.0, 7.0): 0.00847029246883, (18.0, 9.0): 0.00700338117153, (15.0, 4.0): 0.00846437202878, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00478928191945, (9.0, 3.0): 0.00671057807652, (19.0, 24.0): 0.00762386822499, (12.0, 11.0): 0.00679107232544, (13.0, 10.0): 0.00852083648739, (18.0, 10.0): 0.00711379718536, (17.0, 18.0): 0.00715881392723, (9.0, 9.0): 0.00841494145616, (16.0, 18.0): 0.00717672774975, (11.0, 8.0): 0.00843671388472, (14.0, 8.0): 0.00852241165596, (21.0, 24.0): 0.00509496465412, (20.0, 16.0): 0.00682218164429, (17.0, 8.0): 0.00863760880359, (18.0, 15.0): 0.0087363019475, (8.0, 9.0): 0.00672760736167, (9.0, 4.0): 0.0083892959895, (10.0, 3.0): 0.00671540452621, (16.0, 7.0): 0.00855637995056, (11.0, 10.0): 0.00845356611043, (17.0, 6.0): 0.00853854422614, (16.0, 10.0): 0.00870819795945, (14.0, 5.0): 0.00845973570807, (13.0, 3.0): 0.0067432125459, (18.0, 5.0): 0.00681299559267, (17.0, 11.0): 0.0071128324495, (18.0, 16.0): 0.00903457140872, (8.0, 6.0): 0.00671484213822, (10.0, 9.0): 0.00842703968555, (9.0, 7.0): 0.00840291771443, (11.0, 4.0): 0.0084054679537, (10.0, 4.0): 0.00839569684314, (16.0, 4.0): 0.00847844715227, (12.0, 7.0): 0.00844638123248, (11.0, 9.0): 0.00844602448031, (15.0, 10.0): 0.00863148154141, (14.0, 6.0): 0.0084763880927, (13.0, 6.0): 0.0084541966299, (18.0, 6.0): 0.00683912571007, (17.0, 22.0): 0.00755755871169, (15.0, 7.0): 0.00852733407085, (18.0, 21.0): 0.00746222845009, (8.0, 3.0): 0.00503141634126, (10.0, 10.0): 0.0084328218403, (11.0, 3.0): 0.00672276455852, (12.0, 4.0): 0.00841809368414, (15.0, 9.0): 0.00859733067412, (14.0, 3.0): 0.0067549211587, (13.0, 9.0): 0.00850600229536, (12.0, 8.0): 0.00845953805259, (18.0, 11.0): 0.00729823466786, (18.0, 22.0): 0.0100463515507, (9.0, 8.0): 0.00840910961908, (14.0, 9.0): 0.00854772347272, (12.0, 9.0): 0.00847221631742, (17.0, 15.0): 0.00657787464453, (21.0, 20.0): 0.00495244273676, (18.0, 12.0): 0.00574985590811, (15.0, 11.0): 0.00692343318221, (8.0, 10.0): 0.006731024783, (9.0, 11.0): 0.00673822685181, (12.0, 3.0): 0.0067322333154, (17.0, 5.0): 0.00850842804161, (16.0, 11.0): 0.00700046662233, (14.0, 10.0): 0.00856979944971, (17.0, 10.0): 0.00880011295345, (15.0, 3.0): 0.006766409964, (18.0, 17.0): 0.0069582019193, (8.0, 7.0): 0.00671899779493, (9.0, 6.0): 0.00839711577123, (11.0, 7.0): 0.0084269104361, (10.0, 5.0): 0.00839951453848, (16.0, 5.0): 0.0084951238639, (20.0, 24.0): 0.00763715093892, (19.0, 16.0): 0.00680423949066, (16.0, 8.0): 0.00860100531269, (14.0, 7.0): 0.00849778487842, (13.0, 5.0): 0.00844140207286, (18.0, 20.0): 0.00984072140173, (18.0, 7.0): 0.00687679817422, (15.0, 6.0): 0.00849938636459, (18.0, 18.0): 0.00950715415576, (8.0, 4.0): 0.00670926978574, (10.0, 11.0): 0.00674918515296, (13.0, 8.0): 0.00848825262273, (10.0, 6.0): 0.00840527583107, (15.0, 22.0): 0.00505160539977, (12.0, 5.0): 0.00842461906256, (17.0, 3.0): 0.00678449358805, (15.0, 8.0): 0.0085608614646, (21.0, 16.0): 0.00455294262511, (18.0, 8.0): 0.00692932933674, (15.0, 5.0): 0.00847822808194, (18.0, 23.0): 0.00757286421643, (16.0, 15.0): 0.00659416244493, (12.0, 10.0): 0.0084825750348, (15.0, 15.0): 0.00440049020691, (13.0, 11.0): 0.00682384449216, (18.0, 13.0): 0.00602302288257, (18.0, 24.0): 0.00760249383278, (8.0, 11.0): 0.00504996623414, (9.0, 10.0): 0.0084196921509, (19.0, 20.0): 0.00740619751194, (17.0, 4.0): 0.00848937138425, (14.0, 11.0): 0.00686681430059, (18.0, 3.0): 0.00509186703196, (17.0, 9.0): 0.0087106252386, (18.0, 14.0): 0.00629112799686, (8.0, 8.0): 0.00672343088345, (9.0, 5.0): 0.00839238963496, (11.0, 6.0): 0.00841781245927, }, ]
+151 None [{(16.0, 6.0): 0.00852103287234, (11.0, 11.0): 0.0067665681387, (17.0, 7.0): 0.00858098182398, (16.0, 9.0): 0.00865340719507, (14.0, 4.0): 0.00844867681546, (13.0, 4.0): 0.0084328302692, (20.0, 20.0): 0.00742224360948, (18.0, 4.0): 0.00679664923463, (18.0, 19.0): 0.00726042479162, (8.0, 5.0): 0.00671146228371, (10.0, 8.0): 0.00841990542359, (11.0, 5.0): 0.00841041630712, (10.0, 7.0): 0.00841234330923, (16.0, 3.0): 0.00677661908917, (12.0, 6.0): 0.00843433407698, (16.0, 22.0): 0.007571746438, (13.0, 7.0): 0.00847029246883, (18.0, 9.0): 0.00700338117153, (15.0, 4.0): 0.00846437202878, (1, 1): 0.00763358778626, (15.0, 18.0): 0.00478928191945, (9.0, 3.0): 0.00671057807652, (19.0, 24.0): 0.00762386822499, (12.0, 11.0): 0.00679107232544, (13.0, 10.0): 0.00852083648739, (18.0, 10.0): 0.00711379718536, (17.0, 18.0): 0.00715881392723, (9.0, 9.0): 0.00841494145616, (16.0, 18.0): 0.00717672774975, (11.0, 8.0): 0.00843671388472, (14.0, 8.0): 0.00852241165596, (21.0, 24.0): 0.00509496465412, (20.0, 16.0): 0.00682218164429, (17.0, 8.0): 0.00863760880359, (18.0, 15.0): 0.0087363019475, (8.0, 9.0): 0.00672760736167, (9.0, 4.0): 0.0083892959895, (10.0, 3.0): 0.00671540452621, (16.0, 7.0): 0.00855637995056, (11.0, 10.0): 0.00845356611043, (17.0, 6.0): 0.00853854422614, (16.0, 10.0): 0.00870819795945, (14.0, 5.0): 0.00845973570807, (13.0, 3.0): 0.0067432125459, (18.0, 5.0): 0.00681299559267, (17.0, 11.0): 0.0071128324495, (18.0, 16.0): 0.00903457140872, (8.0, 6.0): 0.00671484213822, (10.0, 9.0): 0.00842703968555, (9.0, 7.0): 0.00840291771443, (11.0, 4.0): 0.0084054679537, (10.0, 4.0): 0.00839569684314, (16.0, 4.0): 0.00847844715227, (12.0, 7.0): 0.00844638123248, (11.0, 9.0): 0.00844602448031, (15.0, 10.0): 0.00863148154141, (14.0, 6.0): 0.0084763880927, (13.0, 6.0): 0.0084541966299, (18.0, 6.0): 0.00683912571007, (17.0, 22.0): 0.00755755871169, (15.0, 7.0): 0.00852733407085, (18.0, 21.0): 0.00746222845009, (8.0, 3.0): 0.00503141634126, (10.0, 10.0): 0.0084328218403, (11.0, 3.0): 0.00672276455852, (12.0, 4.0): 0.00841809368414, (15.0, 9.0): 0.00859733067412, (14.0, 3.0): 0.0067549211587, (13.0, 9.0): 0.00850600229536, (12.0, 8.0): 0.00845953805259, (18.0, 11.0): 0.00729823466786, (18.0, 22.0): 0.0100463515507, (9.0, 8.0): 0.00840910961908, (14.0, 9.0): 0.00854772347272, (12.0, 9.0): 0.00847221631742, (17.0, 15.0): 0.00657787464453, (21.0, 20.0): 0.00495244273676, (18.0, 12.0): 0.00574985590811, (15.0, 11.0): 0.00692343318221, (8.0, 10.0): 0.006731024783, (9.0, 11.0): 0.00673822685181, (12.0, 3.0): 0.0067322333154, (17.0, 5.0): 0.00850842804161, (16.0, 11.0): 0.00700046662233, (14.0, 10.0): 0.00856979944971, (17.0, 10.0): 0.00880011295345, (15.0, 3.0): 0.006766409964, (18.0, 17.0): 0.0069582019193, (8.0, 7.0): 0.00671899779493, (9.0, 6.0): 0.00839711577123, (11.0, 7.0): 0.0084269104361, (10.0, 5.0): 0.00839951453848, (16.0, 5.0): 0.0084951238639, (20.0, 24.0): 0.00763715093892, (19.0, 16.0): 0.00680423949066, (16.0, 8.0): 0.00860100531269, (14.0, 7.0): 0.00849778487842, (13.0, 5.0): 0.00844140207286, (18.0, 20.0): 0.00984072140173, (18.0, 7.0): 0.00687679817422, (15.0, 6.0): 0.00849938636459, (18.0, 18.0): 0.00950715415576, (8.0, 4.0): 0.00670926978574, (10.0, 11.0): 0.00674918515296, (13.0, 8.0): 0.00848825262273, (10.0, 6.0): 0.00840527583107, (15.0, 22.0): 0.00505160539977, (12.0, 5.0): 0.00842461906256, (17.0, 3.0): 0.00678449358805, (15.0, 8.0): 0.0085608614646, (21.0, 16.0): 0.00455294262511, (18.0, 8.0): 0.00692932933674, (15.0, 5.0): 0.00847822808194, (18.0, 23.0): 0.00757286421643, (16.0, 15.0): 0.00659416244493, (12.0, 10.0): 0.0084825750348, (15.0, 15.0): 0.00440049020691, (13.0, 11.0): 0.00682384449216, (18.0, 13.0): 0.00602302288257, (18.0, 24.0): 0.00760249383278, (8.0, 11.0): 0.00504996623414, (9.0, 10.0): 0.0084196921509, (19.0, 20.0): 0.00740619751194, (17.0, 4.0): 0.00848937138425, (14.0, 11.0): 0.00686681430059, (18.0, 3.0): 0.00509186703196, (17.0, 9.0): 0.0087106252386, (18.0, 14.0): 0.00629112799686, (8.0, 8.0): 0.00672343088345, (9.0, 5.0): 0.00839238963496, (11.0, 6.0): 0.00841781245927, }, ]
+"""

tracking/test_cases/q11/4-ParticlePredict.test

@@ -0,0 +1,46 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%    %
+%%%%%%%%%%%%%%%%%% %%%%
+%%%%%%%%%%%%%%%    %%%%
+%%%%%%%%%%%%%%%%%% %%%%
+%%%%%%%%%%%%%%%%%%    %
+%%%%%%%%%%%%%%%%%% %%%%
+%%%%%%%%%%%%%%%    %%%%
+%%%%%%%%%%%%%%%%%% %%%%
+%%%%%%%%%%%%%%%%%%    %
+%%%%%%%%%%%%%%%    %%%%
+%%%%%%%%%%%%%%%%%% %%%%
+%%%%%%%%%%%%%%%%%% %%%%
+%%%%%%%%%%%%%%%%%% %%%%
+%%%%%%%%           %%%%
+%%%%%%%%           %%%%
+%%%%%%%%           %%%%
+%%%%%%%%           %%%%
+%%%%%%%%   G       %%%%
+%%%%%%%%           %%%%
+%%P%%%%%           %%%%
+%%%%%%%%           %%%%
+%%%%%%%%           %%%%
+%%%%%%%%%%%%%%%%%%%%%%%
+% %%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%
+"""
+
+observe: "False"
+
+elapse: "True"
+
+checkUniform: "False"
+maxMoves: "150"
+numParticles: "5000"
+numGhosts: "1"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+L2Tolerance: "0.05"
+errorMsg: "Particle filter full test: %d inference errors."
+

tracking/test_cases/q11/5-ParticlePredict.test

@@ -0,0 +1,37 @@
+class: "DoubleInferenceAgentTest"
+
+seed: "188"
+
+layout: """
+%%%%%%%%%%%%%%%
+%      G      %
+% %%%%%%%%%%% %
+% %         % %
+% %         % %
+% %         % %
+% %         % %
+% %    P    % %
+% %         % %
+% %         % %
+% %         % %
+% %         % %
+% %%%%%%%%%%% %
+%      G      %
+%%%%%%%%%%%%%%%
+% % %%%%%%%%%%%
+%%%%%%%%%%%%%%%
+"""
+
+observe: "True"
+
+elapse: "True"
+
+checkUniform: "False"
+maxMoves: "100"
+numParticles: "5000"
+numGhosts: "2"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+L2Tolerance: "0.05"
+errorMsg: "Particle filter full test: %d inference errors."
+

tracking/test_cases/q11/6-ParticlePredict.solution

@@ -0,0 +1 @@
+# You must win at least 4/10 games with at least 300 points

tracking/test_cases/q11/6-ParticlePredict.test

@@ -0,0 +1,26 @@
+class: "GameScoreTest"
+
+numRuns: "5"
+numWinsForCredit: "4"
+layout_name: "smallHunt"
+min_score: "300"
+
+layout_str: """
+%%%%%%%%%%%%%%%%%%%%
+%P        G        %
+% %%%%% %%%%%% % % %
+%     G            %
+%                  %
+%%%%%%%%%%%%%%%%%%%%
+% % %%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%
+"""
+
+observe: "True"
+elapse: "True"
+maxMoves: "100"
+ghost: "SeededRandomGhostAgent"
+inference: "ParticleFilter"
+numGhosts: "2"
+
+

tracking/test_cases/q11/CONFIG

@@ -0,0 +1,3 @@
+max_points: "2"
+class: "PassAllTestsQuestion"
+

tracking/test_cases/q2/1-product-rule.solution

@@ -0,0 +1,18 @@
+# This is the solution file for test_cases/q3/1-product-rule.test.
+variableDomainsDict: """
+D : wet dry
+W : sun rain
+"""
+
+unconditionedVariables: "D W"
+
+conditionedVariables: ""
+
+FactorTable: """
+D : wet, W : sun = 0.08
+D : dry, W : sun = 0.72
+D : wet, W : rain = 0.14
+D : dry, W : rain = 0.06
+"""
+
+

tracking/test_cases/q2/1-product-rule.test

@@ -0,0 +1,36 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "joinFactors"
+factorArgs: "A B"
+seed: "cs188"
+constructRandomly: "False"
+
+
+variableDomainsDict: """
+W : sun rain
+D : wet dry
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "D"
+
+AconditionedVariables: "W"
+
+AFactorTable: """
+D : wet, W : sun = 0.1
+D : dry, W : sun = 0.9
+D : wet, W : rain = 0.7
+D : dry, W : rain = 0.3
+"""
+
+
+BunconditionedVariables: "W"
+
+BconditionedVariables: ""
+
+BFactorTable: """
+W : sun = 0.8
+W : rain = 0.2
+"""
+

tracking/test_cases/q2/2-product-rule-extended.solution

@@ -0,0 +1,23 @@
+# This is the solution file for test_cases/q3/2-product-rule-extended.test.
+variableDomainsDict: """
+D : wet dry
+W : sun rain
+T : cold hot
+"""
+
+unconditionedVariables: "D W T"
+
+conditionedVariables: ""
+
+FactorTable: """
+D : wet, W : sun, T : cold = 0.02
+D : dry, W : sun, T : cold = 0.18
+D : wet, W : rain, T : cold = 0.126
+D : dry, W : rain, T : cold = 0.054
+D : wet, W : sun, T : hot = 0.06
+D : dry, W : sun, T : hot = 0.54
+D : wet, W : rain, T : hot = 0.014
+D : dry, W : rain, T : hot = 0.006
+"""
+
+

tracking/test_cases/q2/2-product-rule-extended.test

@@ -0,0 +1,49 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "joinFactors"
+factorArgs: "A B C"
+seed: "cs188"
+constructRandomly: "False"
+
+
+variableDomainsDict: """
+W : sun rain
+D : wet dry
+T : cold hot
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "D"
+
+AconditionedVariables: "W"
+
+AFactorTable: """
+D : wet, W : sun = 0.1
+D : dry, W : sun = 0.9
+D : wet, W : rain = 0.7
+D : dry, W : rain = 0.3
+"""
+
+
+CunconditionedVariables: "T"
+
+CconditionedVariables: "W"
+
+CFactorTable: """
+T : cold, W : sun = 0.25
+T : hot, W : sun = 0.75
+T : cold, W : rain = 0.9
+T : hot, W : rain = 0.1
+"""
+
+
+BunconditionedVariables: "W"
+
+BconditionedVariables: ""
+
+BFactorTable: """
+W : sun = 0.8
+W : rain = 0.2
+"""
+

tracking/test_cases/q2/3-disjoint-right.solution

@@ -0,0 +1,113 @@
+# This is the solution file for test_cases/q3/3-disjoint-right.test.
+variableDomainsDict: """
+Q : singleEntryForThisVariable
+M : l m n o
+L : 0 1 2
+O : help! me! I'mTrapped InaBayesNet
+N : -10.5 1.34
+"""
+
+unconditionedVariables: "M L N"
+
+conditionedVariables: "Q O"
+
+FactorTable: """
+M : l, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.258330124403
+M : m, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.104734692091
+M : n, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.0377614688681
+M : o, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.0
+M : l, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.172735794206
+M : m, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.0125717596017
+M : n, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.0638957615356
+M : o, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.0667905852913
+M : l, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.0
+M : m, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.0391473894072
+M : n, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.191710903684
+M : o, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : help! = 0.0523215209113
+M : l, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : m, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : n, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : o, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : l, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : m, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : n, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : o, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : l, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : m, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : n, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : o, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : help! = 0.0
+M : l, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.145578766133
+M : m, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0590219482964
+M : n, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0212800116049
+M : o, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0
+M : l, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0973431335025
+M : m, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.00708466058755
+M : n, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0360076709868
+M : o, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0376390133302
+M : l, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0
+M : m, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0220610300885
+M : n, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.108036323201
+M : o, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : me! = 0.0294851499571
+M : l, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.11275135827
+M : m, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0457127437946
+M : n, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0164814572632
+M : o, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0
+M : l, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0753926607035
+M : m, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.00548709901415
+M : n, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0278880905488
+M : o, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0291515719611
+M : l, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0
+M : m, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0170863593187
+M : n, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0836745804831
+M : o, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : me! = 0.0228363709542
+M : l, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.195574976701
+M : m, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0792918944811
+M : n, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0285882198645
+M : o, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0
+M : l, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.130773749307
+M : m, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0095177501922
+M : n, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0483738089101
+M : o, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0505654042182
+M : l, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0
+M : m, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0296374640352
+M : n, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.145139308116
+M : o, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0396112542307
+M : l, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0627551477024
+M : m, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0254427976099
+M : n, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.00917324900359
+M : o, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0
+M : l, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0419620448987
+M : m, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0030540094095
+M : n, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0155219526255
+M : o, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0162251810731
+M : l, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0
+M : m, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.009509925372
+M : n, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0465715955685
+M : o, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : I'mTrapped = 0.0127102666806
+M : l, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.135144264696
+M : m, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0547914920242
+M : n, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0197547458154
+M : o, L : 0, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0
+M : l, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0903659685399
+M : m, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.00657686056258
+M : n, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0334267857065
+M : o, L : 1, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0349411999808
+M : l, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0
+M : m, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.02047978403
+M : n, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.100292713335
+M : o, L : 2, N : -10.5, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0273717727954
+M : l, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.123185859707
+M : m, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0499432000668
+M : n, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0180067230527
+M : o, L : 0, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0
+M : l, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0823698256661
+M : m, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.00599489903912
+M : n, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0304689758291
+M : o, L : 1, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0318493853105
+M : l, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0
+M : m, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0186676053772
+M : n, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0914181903486
+M : o, L : 2, N : 1.34, Q : singleEntryForThisVariable, O : InaBayesNet = 0.0249497481159
+"""
+
+

tracking/test_cases/q2/3-disjoint-right.test

@@ -0,0 +1,53 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "joinFactors"
+factorArgs: "A B"
+seed: "cs188"
+constructRandomly: "False"
+
+
+variableDomainsDict: """
+L : 0 1 2
+M : l m n o
+N : -10.5 1.34
+O : help! me! I'mTrapped InaBayesNet
+Q : singleEntryForThisVariable
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "M L"
+
+AconditionedVariables: "Q"
+
+AFactorTable: """
+L : 0, M : l, Q : singleEntryForThisVariable = 0.258330124403
+L : 1, M : l, Q : singleEntryForThisVariable = 0.172735794206
+L : 2, M : l, Q : singleEntryForThisVariable = 0.0
+L : 0, M : m, Q : singleEntryForThisVariable = 0.104734692091
+L : 1, M : m, Q : singleEntryForThisVariable = 0.0125717596017
+L : 2, M : m, Q : singleEntryForThisVariable = 0.0391473894072
+L : 0, M : n, Q : singleEntryForThisVariable = 0.0377614688681
+L : 1, M : n, Q : singleEntryForThisVariable = 0.0638957615356
+L : 2, M : n, Q : singleEntryForThisVariable = 0.191710903684
+L : 0, M : o, Q : singleEntryForThisVariable = 0.0
+L : 1, M : o, Q : singleEntryForThisVariable = 0.0667905852913
+L : 2, M : o, Q : singleEntryForThisVariable = 0.0523215209113
+"""
+
+
+BunconditionedVariables: "N"
+
+BconditionedVariables: "O"
+
+BFactorTable: """
+N : -10.5, O : help! = 1.0
+N : 1.34, O : help! = 0.0
+N : -10.5, O : me! = 0.563537707688
+N : 1.34, O : me! = 0.436462292312
+N : -10.5, O : I'mTrapped = 0.757073830056
+N : 1.34, O : I'mTrapped = 0.242926169944
+N : -10.5, O : InaBayesNet = 0.523145587487
+N : 1.34, O : InaBayesNet = 0.476854412513
+"""
+

tracking/test_cases/q2/4-common-right.solution

@@ -0,0 +1,113 @@
+# This is the solution file for test_cases/q3/4-common-right.test.
+variableDomainsDict: """
+Q1 : myNameHasMoreThanOneLetter
+N1 : -10.5 1.34
+M1 : l m n o
+O1 : help! me! I'mTrapped InaBayesNet
+L1 : 0 1 2
+"""
+
+unconditionedVariables: "Q1 N1 M1 L1"
+
+conditionedVariables: "O1"
+
+FactorTable: """
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 0, O1 : help! = 0.0396781584597
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 0, O1 : help! = 0.218651965943
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 0, O1 : help! = 0.0160867019231
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 0, O1 : help! = 0.0886479901679
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 0, O1 : help! = 0.00579996447911
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 0, O1 : help! = 0.031961504389
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 0, O1 : help! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 0, O1 : help! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 1, O1 : help! = 0.0265313161986
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 1, O1 : help! = 0.146204478007
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 1, O1 : help! = 0.00193095664219
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 1, O1 : help! = 0.0106408029595
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 1, O1 : help! = 0.0098140553951
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 1, O1 : help! = 0.0540817061405
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 1, O1 : help! = 0.0102586852111
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 1, O1 : help! = 0.0565319000802
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 2, O1 : help! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 2, O1 : help! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 2, O1 : help! = 0.00601283463852
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 2, O1 : help! = 0.0331345547687
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 2, O1 : help! = 0.0294457939523
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 2, O1 : help! = 0.162265109732
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 2, O1 : help! = 0.00803631246015
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 2, O1 : help! = 0.0442852084511
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 0, O1 : me! = 0.159661284861
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 0, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 0, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 0, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 0, O1 : me! = 0.062484977125
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 0, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 0, O1 : me! = 0.048394873988
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 0, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 1, O1 : me! = 0.110637840267
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 1, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 1, O1 : me! = 0.0355009323002
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 1, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 1, O1 : me! = 0.144114059443
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 1, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 1, O1 : me! = 0.131361951232
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 1, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 2, O1 : me! = 0.062692626321
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 2, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 2, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 2, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 2, O1 : me! = 0.130648517392
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 2, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 2, O1 : me! = 0.11450293707
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 2, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 0, O1 : I'mTrapped = 0.158525600787
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 0, O1 : I'mTrapped = 0.106254735008
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 0, O1 : I'mTrapped = 0.100800788457
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 0, O1 : I'mTrapped = 0.0675636049505
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 0, O1 : I'mTrapped = 0.0987735573681
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 0, O1 : I'mTrapped = 0.0662048155749
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 0, O1 : I'mTrapped = 0.0263411253275
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 0, O1 : I'mTrapped = 0.017655629612
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 1, O1 : I'mTrapped = 0.0097691504527
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 1, O1 : I'mTrapped = 0.0065479549515
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 1, O1 : I'mTrapped = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 1, O1 : I'mTrapped = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 1, O1 : I'mTrapped = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 1, O1 : I'mTrapped = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 1, O1 : I'mTrapped = 0.0442852566048
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 1, O1 : I'mTrapped = 0.029683017645
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 2, O1 : I'mTrapped = 0.0445155280707
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 2, O1 : I'mTrapped = 0.0298373613816
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 2, O1 : I'mTrapped = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 2, O1 : I'mTrapped = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 2, O1 : I'mTrapped = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 2, O1 : I'mTrapped = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 2, O1 : I'mTrapped = 0.115695087593
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 2, O1 : I'mTrapped = 0.077546786216
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 0, O1 : InaBayesNet = 0.134402798029
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 0, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 0, O1 : InaBayesNet = 0.118127348229
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 0, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 0, O1 : InaBayesNet = 0.036009670999
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 0, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 0, O1 : InaBayesNet = 0.112024611449
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 0, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 1, O1 : InaBayesNet = 0.0773037189897
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 1, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 1, O1 : InaBayesNet = 0.143223151678
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 1, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 1, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 1, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 1, O1 : InaBayesNet = 0.0865755172268
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 1, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : l, L1 : 2, O1 : InaBayesNet = 0.143496852294
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : l, L1 : 2, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : m, L1 : 2, O1 : InaBayesNet = 0.0132562954964
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : m, L1 : 2, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : n, L1 : 2, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : n, L1 : 2, O1 : InaBayesNet = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, M1 : o, L1 : 2, O1 : InaBayesNet = 0.135580035609
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, M1 : o, L1 : 2, O1 : InaBayesNet = 0.0
+"""
+
+

tracking/test_cases/q2/4-common-right.test

@@ -0,0 +1,162 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "joinFactors"
+factorArgs: "A B"
+seed: "cs188"
+constructRandomly: "False"
+
+
+variableDomainsDict: """
+L1 : 0 1 2
+M1 : l m n o
+N1 : -10.5 1.34
+O1 : help! me! I'mTrapped InaBayesNet
+Q1 : myNameHasMoreThanOneLetter
+"""
+
+# endO1fN1onFactors
+
+AunconditionedVariables: "L1 M1"
+
+AconditionedVariables: "O1"
+
+AFactorTable: """
+L1 : 0, M1 : l, O1 : help! = 0.258330124403
+L1 : 1, M1 : l, O1 : help! = 0.172735794206
+L1 : 2, M1 : l, O1 : help! = 0.0
+L1 : 0, M1 : m, O1 : help! = 0.104734692091
+L1 : 1, M1 : m, O1 : help! = 0.0125717596017
+L1 : 2, M1 : m, O1 : help! = 0.0391473894072
+L1 : 0, M1 : n, O1 : help! = 0.0377614688681
+L1 : 1, M1 : n, O1 : help! = 0.0638957615356
+L1 : 2, M1 : n, O1 : help! = 0.191710903684
+L1 : 0, M1 : o, O1 : help! = 0.0
+L1 : 1, M1 : o, O1 : help! = 0.0667905852913
+L1 : 2, M1 : o, O1 : help! = 0.0523215209113
+L1 : 0, M1 : l, O1 : me! = 0.159661284861
+L1 : 1, M1 : l, O1 : me! = 0.110637840267
+L1 : 2, M1 : l, O1 : me! = 0.062692626321
+L1 : 0, M1 : m, O1 : me! = 0.0
+L1 : 1, M1 : m, O1 : me! = 0.0355009323002
+L1 : 2, M1 : m, O1 : me! = 0.0
+L1 : 0, M1 : n, O1 : me! = 0.062484977125
+L1 : 1, M1 : n, O1 : me! = 0.144114059443
+L1 : 2, M1 : n, O1 : me! = 0.130648517392
+L1 : 0, M1 : o, O1 : me! = 0.048394873988
+L1 : 1, M1 : o, O1 : me! = 0.131361951232
+L1 : 2, M1 : o, O1 : me! = 0.11450293707
+L1 : 0, M1 : l, O1 : I'mTrapped = 0.264780335795
+L1 : 1, M1 : l, O1 : I'mTrapped = 0.0163171054042
+L1 : 2, M1 : l, O1 : I'mTrapped = 0.0743528894523
+L1 : 0, M1 : m, O1 : I'mTrapped = 0.168364393407
+L1 : 1, M1 : m, O1 : I'mTrapped = 0.0
+L1 : 2, M1 : m, O1 : I'mTrapped = 0.0
+L1 : 0, M1 : n, O1 : I'mTrapped = 0.164978372943
+L1 : 1, M1 : n, O1 : I'mTrapped = 0.0
+L1 : 2, M1 : n, O1 : I'mTrapped = 0.0
+L1 : 0, M1 : o, O1 : I'mTrapped = 0.0439967549395
+L1 : 1, M1 : o, O1 : I'mTrapped = 0.0739682742498
+L1 : 2, M1 : o, O1 : I'mTrapped = 0.193241873809
+L1 : 0, M1 : l, O1 : InaBayesNet = 0.134402798029
+L1 : 1, M1 : l, O1 : InaBayesNet = 0.0773037189897
+L1 : 2, M1 : l, O1 : InaBayesNet = 0.143496852294
+L1 : 0, M1 : m, O1 : InaBayesNet = 0.118127348229
+L1 : 1, M1 : m, O1 : InaBayesNet = 0.143223151678
+L1 : 2, M1 : m, O1 : InaBayesNet = 0.0132562954964
+L1 : 0, M1 : n, O1 : InaBayesNet = 0.036009670999
+L1 : 1, M1 : n, O1 : InaBayesNet = 0.0
+L1 : 2, M1 : n, O1 : InaBayesNet = 0.0
+L1 : 0, M1 : o, O1 : InaBayesNet = 0.112024611449
+L1 : 1, M1 : o, O1 : InaBayesNet = 0.0865755172268
+L1 : 2, M1 : o, O1 : InaBayesNet = 0.135580035609
+"""
+
+
+BunconditionedVariables: "Q1 N1"
+
+BconditionedVariables: "O1"
+
+BFactorTable: """
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, O1 : help! = 0.15359477936
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, O1 : help! = 0.84640522064
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, O1 : me! = 1.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, O1 : I'mTrapped = 0.59870609466
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, O1 : I'mTrapped = 0.40129390534
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, O1 : InaBayesNet = 1.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, O1 : InaBayesNet = 0.0
+"""
+
+
+
+AunconditionedVariables: "M1 L1"
+
+AconditionedVariables: "O1"
+
+AFactorTable: """
+L1 : 0, M1 : l, O1 : help! = 0.258330124403
+L1 : 1, M1 : l, O1 : help! = 0.172735794206
+L1 : 2, M1 : l, O1 : help! = 0.0
+L1 : 0, M1 : m, O1 : help! = 0.104734692091
+L1 : 1, M1 : m, O1 : help! = 0.0125717596017
+L1 : 2, M1 : m, O1 : help! = 0.0391473894072
+L1 : 0, M1 : n, O1 : help! = 0.0377614688681
+L1 : 1, M1 : n, O1 : help! = 0.0638957615356
+L1 : 2, M1 : n, O1 : help! = 0.191710903684
+L1 : 0, M1 : o, O1 : help! = 0.0
+L1 : 1, M1 : o, O1 : help! = 0.0667905852913
+L1 : 2, M1 : o, O1 : help! = 0.0523215209113
+L1 : 0, M1 : l, O1 : me! = 0.159661284861
+L1 : 1, M1 : l, O1 : me! = 0.110637840267
+L1 : 2, M1 : l, O1 : me! = 0.062692626321
+L1 : 0, M1 : m, O1 : me! = 0.0
+L1 : 1, M1 : m, O1 : me! = 0.0355009323002
+L1 : 2, M1 : m, O1 : me! = 0.0
+L1 : 0, M1 : n, O1 : me! = 0.062484977125
+L1 : 1, M1 : n, O1 : me! = 0.144114059443
+L1 : 2, M1 : n, O1 : me! = 0.130648517392
+L1 : 0, M1 : o, O1 : me! = 0.048394873988
+L1 : 1, M1 : o, O1 : me! = 0.131361951232
+L1 : 2, M1 : o, O1 : me! = 0.11450293707
+L1 : 0, M1 : l, O1 : I'mTrapped = 0.264780335795
+L1 : 1, M1 : l, O1 : I'mTrapped = 0.0163171054042
+L1 : 2, M1 : l, O1 : I'mTrapped = 0.0743528894523
+L1 : 0, M1 : m, O1 : I'mTrapped = 0.168364393407
+L1 : 1, M1 : m, O1 : I'mTrapped = 0.0
+L1 : 2, M1 : m, O1 : I'mTrapped = 0.0
+L1 : 0, M1 : n, O1 : I'mTrapped = 0.164978372943
+L1 : 1, M1 : n, O1 : I'mTrapped = 0.0
+L1 : 2, M1 : n, O1 : I'mTrapped = 0.0
+L1 : 0, M1 : o, O1 : I'mTrapped = 0.0439967549395
+L1 : 1, M1 : o, O1 : I'mTrapped = 0.0739682742498
+L1 : 2, M1 : o, O1 : I'mTrapped = 0.193241873809
+L1 : 0, M1 : l, O1 : InaBayesNet = 0.134402798029
+L1 : 1, M1 : l, O1 : InaBayesNet = 0.0773037189897
+L1 : 2, M1 : l, O1 : InaBayesNet = 0.143496852294
+L1 : 0, M1 : m, O1 : InaBayesNet = 0.118127348229
+L1 : 1, M1 : m, O1 : InaBayesNet = 0.143223151678
+L1 : 2, M1 : m, O1 : InaBayesNet = 0.0132562954964
+L1 : 0, M1 : n, O1 : InaBayesNet = 0.036009670999
+L1 : 1, M1 : n, O1 : InaBayesNet = 0.0
+L1 : 2, M1 : n, O1 : InaBayesNet = 0.0
+L1 : 0, M1 : o, O1 : InaBayesNet = 0.112024611449
+L1 : 1, M1 : o, O1 : InaBayesNet = 0.0865755172268
+L1 : 2, M1 : o, O1 : InaBayesNet = 0.135580035609
+"""
+
+
+BunconditionedVariables: "Q1 N1"
+
+BconditionedVariables: "O1"
+
+BFactorTable: """
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, O1 : help! = 0.15359477936
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, O1 : help! = 0.84640522064
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, O1 : me! = 1.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, O1 : me! = 0.0
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, O1 : I'mTrapped = 0.59870609466
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, O1 : I'mTrapped = 0.40129390534
+Q1 : myNameHasMoreThanOneLetter, N1 : -10.5, O1 : InaBayesNet = 1.0
+Q1 : myNameHasMoreThanOneLetter, N1 : 1.34, O1 : InaBayesNet = 0.0
+"""
+

tracking/test_cases/q2/5-grade-join.solution

@@ -0,0 +1,113 @@
+# This is the solution file for test_cases/q3/5-grade-join.test.
+variableDomainsDict: """
+Q : singleEntryForThisVariable
+M : l m n o
+L : 0 1 2
+O : help! me! I'mTrapped InaBayesNet
+N : -10.5 1.34
+"""
+
+unconditionedVariables: "M L O N"
+
+conditionedVariables: "Q"
+
+FactorTable: """
+M : l, L : 0, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : m, L : 0, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : n, L : 0, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : o, L : 0, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : l, L : 1, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : m, L : 1, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.00639608303801
+M : n, L : 1, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0176575414931
+M : o, L : 1, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.00296557788646
+M : l, L : 2, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0135133823856
+M : m, L : 2, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0307889341341
+M : n, L : 2, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0264097152778
+M : o, L : 2, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0490236571006
+M : l, L : 0, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.007715630127
+M : m, L : 0, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.00217909337527
+M : n, L : 0, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.00678551130624
+M : o, L : 0, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : l, L : 1, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0107669094141
+M : m, L : 1, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : n, L : 1, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0305996504641
+M : o, L : 1, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : l, L : 2, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : m, L : 2, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0322681088037
+M : n, L : 2, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : o, L : 2, O : me!, N : -10.5, Q : singleEntryForThisVariable = 5.28535267952e-05
+M : l, L : 0, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.00654528636072
+M : m, L : 0, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : n, L : 0, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0332297127389
+M : o, L : 0, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0189507724124
+M : l, L : 1, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0437076697087
+M : m, L : 1, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0242173380091
+M : n, L : 1, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0243844055398
+M : o, L : 1, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : l, L : 2, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : m, L : 2, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.00685766769793
+M : n, L : 2, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : o, L : 2, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : l, L : 0, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : m, L : 0, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.00646248211554
+M : n, L : 0, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0
+M : o, L : 0, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0020683816771
+M : l, L : 1, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0208613118848
+M : m, L : 1, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0347270528183
+M : n, L : 1, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.00282752151891
+M : o, L : 1, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.013443479893
+M : l, L : 2, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0242099453272
+M : m, L : 2, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0382716139813
+M : n, L : 2, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.00599572506557
+M : o, L : 2, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0335505363489
+M : l, L : 0, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.00746874052442
+M : m, L : 0, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.024671121071
+M : n, L : 0, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : o, L : 0, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0242600514229
+M : l, L : 1, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0170706440325
+M : m, L : 1, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.00415780764838
+M : n, L : 1, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0274809888983
+M : o, L : 1, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : l, L : 2, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : m, L : 2, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : n, L : 2, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : o, L : 2, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : l, L : 0, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0236650880181
+M : m, L : 0, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : n, L : 0, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : o, L : 0, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : l, L : 1, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : m, L : 1, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : n, L : 1, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : o, L : 1, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : l, L : 2, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.00282788161527
+M : m, L : 2, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.00355557777586
+M : n, L : 2, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.025527974888
+M : o, L : 2, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0488393325071
+M : l, L : 0, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : m, L : 0, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : n, L : 0, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : o, L : 0, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : l, L : 1, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : m, L : 1, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.00217872982558
+M : n, L : 1, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : o, L : 1, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0161232920219
+M : l, L : 2, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0208377342581
+M : m, L : 2, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0212182372623
+M : n, L : 2, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0082921520866
+M : o, L : 2, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : l, L : 0, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : m, L : 0, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : n, L : 0, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : o, L : 0, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.035358138402
+M : l, L : 1, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0048170873596
+M : m, L : 1, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : n, L : 1, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0313228366687
+M : o, L : 1, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : l, L : 2, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0168964348
+M : m, L : 2, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+M : n, L : 2, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.039032224516
+M : o, L : 2, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0123590383624
+"""
+
+

tracking/test_cases/q2/5-grade-join.test

@@ -0,0 +1,225 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "joinFactors"
+factorArgs: "A B"
+seed: "cs188"
+constructRandomly: "False"
+
+
+variableDomainsDict: """
+L : 0 1 2
+M : l m n o
+N : -10.5 1.34
+O : help! me! I'mTrapped InaBayesNet
+Q : singleEntryForThisVariable
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "M L O"
+
+AconditionedVariables: "Q N"
+
+AFactorTable: """
+L : 0, M : l, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0447769827421
+L : 1, M : l, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0335548259369
+L : 2, M : l, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0135133823856
+L : 0, M : m, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0299407113048
+L : 1, M : m, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0190137493524
+L : 2, M : m, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0459168029028
+L : 0, M : n, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 1, M : n, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0481229169727
+L : 2, M : n, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0264097152778
+L : 0, M : o, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0484229139817
+L : 1, M : o, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.00296557788646
+L : 2, M : o, O : help!, N : -10.5, Q : singleEntryForThisVariable = 0.0490236571006
+L : 0, M : l, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0181539164706
+L : 1, M : l, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0107669094141
+L : 2, M : l, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 0, M : m, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.00217909337527
+L : 1, M : m, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 2, M : m, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0403565271376
+L : 0, M : n, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.00678551130624
+L : 1, M : n, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0305996504641
+L : 2, M : n, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0489301511809
+L : 0, M : o, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 1, M : o, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 2, M : o, O : me!, N : -10.5, Q : singleEntryForThisVariable = 0.0045288246707
+L : 0, M : l, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.00654528636072
+L : 1, M : l, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0437076697087
+L : 2, M : l, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 0, M : m, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0110752062624
+L : 1, M : m, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0396237693127
+L : 2, M : m, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0123021915473
+L : 0, M : n, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0332297127389
+L : 1, M : n, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0299842528698
+L : 2, M : n, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 0, M : o, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0189507724124
+L : 1, M : o, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 2, M : o, O : I'mTrapped, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 0, M : l, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0
+L : 1, M : l, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.039840143279
+L : 2, M : l, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0351210471164
+L : 0, M : m, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0115769730372
+L : 1, M : m, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0347270528183
+L : 2, M : m, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0382716139813
+L : 0, M : n, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0090690152544
+L : 1, M : n, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.00799625915824
+L : 2, M : n, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0295772931739
+L : 0, M : o, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0146774518464
+L : 1, M : o, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.013443479893
+L : 2, M : o, O : InaBayesNet, N : -10.5, Q : singleEntryForThisVariable = 0.0463189893654
+L : 0, M : l, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.00746874052442
+L : 1, M : l, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0170706440325
+L : 2, M : l, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0125889576289
+L : 0, M : m, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.024671121071
+L : 1, M : m, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.00626546229009
+L : 2, M : m, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 0, M : n, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0246502130155
+L : 1, M : n, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0434087985544
+L : 2, M : n, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0128190936074
+L : 0, M : o, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0242600514229
+L : 1, M : o, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.0038706658879
+L : 2, M : o, O : help!, N : 1.34, Q : singleEntryForThisVariable = 0.000564853924528
+L : 0, M : l, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0411575250006
+L : 1, M : l, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 2, M : l, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0293529082434
+L : 0, M : m, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0165362781706
+L : 1, M : m, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.026525369977
+L : 2, M : m, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0177402756729
+L : 0, M : n, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0488609903771
+L : 1, M : n, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0072654289765
+L : 2, M : n, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.025527974888
+L : 0, M : o, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0391811589986
+L : 1, M : o, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 2, M : o, O : me!, N : 1.34, Q : singleEntryForThisVariable = 0.0494160410886
+L : 0, M : l, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0552756677955
+L : 1, M : l, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 2, M : l, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0321280167988
+L : 0, M : m, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 1, M : m, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.005603470804
+L : 2, M : m, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0479437369214
+L : 0, M : n, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 1, M : n, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 2, M : n, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0302095703475
+L : 0, M : o, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0227207214722
+L : 1, M : o, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0161232920219
+L : 2, M : o, O : I'mTrapped, N : 1.34, Q : singleEntryForThisVariable = 0.0151621127433
+L : 0, M : l, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 1, M : l, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0101119740519
+L : 2, M : l, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0543868524166
+L : 0, M : m, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 1, M : m, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0379246424001
+L : 2, M : m, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.00980121226356
+L : 0, M : n, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 1, M : n, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0484577738418
+L : 2, M : n, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0489563519456
+L : 0, M : o, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0411582588411
+L : 1, M : o, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.0
+L : 2, M : o, O : InaBayesNet, N : 1.34, Q : singleEntryForThisVariable = 0.044833791982
+"""
+
+
+BunconditionedVariables: "N"
+
+BconditionedVariables: "Q M L O"
+
+BFactorTable: """
+N : -10.5, L : 0, M : l, O : help!, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 0, M : l, O : help!, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 1, M : l, O : help!, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 1, M : l, O : help!, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 2, M : l, O : help!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 2, M : l, O : help!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 0, M : m, O : help!, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 0, M : m, O : help!, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 1, M : m, O : help!, Q : singleEntryForThisVariable = 0.33639251888
+N : 1.34, L : 1, M : m, O : help!, Q : singleEntryForThisVariable = 0.66360748112
+N : -10.5, L : 2, M : m, O : help!, Q : singleEntryForThisVariable = 0.670537410876
+N : 1.34, L : 2, M : m, O : help!, Q : singleEntryForThisVariable = 0.329462589124
+N : -10.5, L : 0, M : n, O : help!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 0, M : n, O : help!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 1, M : n, O : help!, Q : singleEntryForThisVariable = 0.366925835005
+N : 1.34, L : 1, M : n, O : help!, Q : singleEntryForThisVariable = 0.633074164995
+N : -10.5, L : 2, M : n, O : help!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 2, M : n, O : help!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 0, M : o, O : help!, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 0, M : o, O : help!, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 1, M : o, O : help!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 1, M : o, O : help!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 2, M : o, O : help!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 2, M : o, O : help!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 0, M : l, O : me!, Q : singleEntryForThisVariable = 0.425011877712
+N : 1.34, L : 0, M : l, O : me!, Q : singleEntryForThisVariable = 0.574988122288
+N : -10.5, L : 1, M : l, O : me!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 1, M : l, O : me!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 2, M : l, O : me!, Q : singleEntryForThisVariable = 0.903659235677
+N : 1.34, L : 2, M : l, O : me!, Q : singleEntryForThisVariable = 0.0963407643231
+N : -10.5, L : 0, M : m, O : me!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 0, M : m, O : me!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 1, M : m, O : me!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 1, M : m, O : me!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 2, M : m, O : me!, Q : singleEntryForThisVariable = 0.799575956912
+N : 1.34, L : 2, M : m, O : me!, Q : singleEntryForThisVariable = 0.200424043088
+N : -10.5, L : 0, M : n, O : me!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 0, M : n, O : me!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 1, M : n, O : me!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 1, M : n, O : me!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 2, M : n, O : me!, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 2, M : n, O : me!, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 0, M : o, O : me!, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 0, M : o, O : me!, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 1, M : o, O : me!, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 1, M : o, O : me!, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 2, M : o, O : me!, Q : singleEntryForThisVariable = 0.0116704731665
+N : 1.34, L : 2, M : o, O : me!, Q : singleEntryForThisVariable = 0.988329526833
+N : -10.5, L : 0, M : l, O : I'mTrapped, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 0, M : l, O : I'mTrapped, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 1, M : l, O : I'mTrapped, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 1, M : l, O : I'mTrapped, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 2, M : l, O : I'mTrapped, Q : singleEntryForThisVariable = 0.351415482985
+N : 1.34, L : 2, M : l, O : I'mTrapped, Q : singleEntryForThisVariable = 0.648584517015
+N : -10.5, L : 0, M : m, O : I'mTrapped, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 0, M : m, O : I'mTrapped, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 1, M : m, O : I'mTrapped, Q : singleEntryForThisVariable = 0.611182086641
+N : 1.34, L : 1, M : m, O : I'mTrapped, Q : singleEntryForThisVariable = 0.388817913359
+N : -10.5, L : 2, M : m, O : I'mTrapped, Q : singleEntryForThisVariable = 0.5574346385
+N : 1.34, L : 2, M : m, O : I'mTrapped, Q : singleEntryForThisVariable = 0.4425653615
+N : -10.5, L : 0, M : n, O : I'mTrapped, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 0, M : n, O : I'mTrapped, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 1, M : n, O : I'mTrapped, Q : singleEntryForThisVariable = 0.813240391404
+N : 1.34, L : 1, M : n, O : I'mTrapped, Q : singleEntryForThisVariable = 0.186759608596
+N : -10.5, L : 2, M : n, O : I'mTrapped, Q : singleEntryForThisVariable = 0.725512412417
+N : 1.34, L : 2, M : n, O : I'mTrapped, Q : singleEntryForThisVariable = 0.274487587583
+N : -10.5, L : 0, M : o, O : I'mTrapped, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 0, M : o, O : I'mTrapped, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 1, M : o, O : I'mTrapped, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 1, M : o, O : I'mTrapped, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 2, M : o, O : I'mTrapped, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 2, M : o, O : I'mTrapped, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 0, M : l, O : InaBayesNet, Q : singleEntryForThisVariable = 0.493094921153
+N : 1.34, L : 0, M : l, O : InaBayesNet, Q : singleEntryForThisVariable = 0.506905078847
+N : -10.5, L : 1, M : l, O : InaBayesNet, Q : singleEntryForThisVariable = 0.523625423199
+N : 1.34, L : 1, M : l, O : InaBayesNet, Q : singleEntryForThisVariable = 0.476374576801
+N : -10.5, L : 2, M : l, O : InaBayesNet, Q : singleEntryForThisVariable = 0.689328687923
+N : 1.34, L : 2, M : l, O : InaBayesNet, Q : singleEntryForThisVariable = 0.310671312077
+N : -10.5, L : 0, M : m, O : InaBayesNet, Q : singleEntryForThisVariable = 0.558218637529
+N : 1.34, L : 0, M : m, O : InaBayesNet, Q : singleEntryForThisVariable = 0.441781362471
+N : -10.5, L : 1, M : m, O : InaBayesNet, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 1, M : m, O : InaBayesNet, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 2, M : m, O : InaBayesNet, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 2, M : m, O : InaBayesNet, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 0, M : n, O : InaBayesNet, Q : singleEntryForThisVariable = 0.0
+N : 1.34, L : 0, M : n, O : InaBayesNet, Q : singleEntryForThisVariable = 1.0
+N : -10.5, L : 1, M : n, O : InaBayesNet, Q : singleEntryForThisVariable = 0.353605537659
+N : 1.34, L : 1, M : n, O : InaBayesNet, Q : singleEntryForThisVariable = 0.646394462341
+N : -10.5, L : 2, M : n, O : InaBayesNet, Q : singleEntryForThisVariable = 0.202713785549
+N : 1.34, L : 2, M : n, O : InaBayesNet, Q : singleEntryForThisVariable = 0.797286214451
+N : -10.5, L : 0, M : o, O : InaBayesNet, Q : singleEntryForThisVariable = 0.140922395709
+N : 1.34, L : 0, M : o, O : InaBayesNet, Q : singleEntryForThisVariable = 0.859077604291
+N : -10.5, L : 1, M : o, O : InaBayesNet, Q : singleEntryForThisVariable = 1.0
+N : 1.34, L : 1, M : o, O : InaBayesNet, Q : singleEntryForThisVariable = 0.0
+N : -10.5, L : 2, M : o, O : InaBayesNet, Q : singleEntryForThisVariable = 0.724336536883
+N : 1.34, L : 2, M : o, O : InaBayesNet, Q : singleEntryForThisVariable = 0.275663463117
+"""
+

tracking/test_cases/q2/6-product-rule-nonsingleton-var.solution

@@ -0,0 +1,18 @@
+# This is the solution file for test_cases/q3/6-product-rule-nonsingleton-var.test.
+variableDomainsDict: """
+D0 : wet dry
+W0 : sun rain
+"""
+
+unconditionedVariables: "D0 W0"
+
+conditionedVariables: ""
+
+FactorTable: """
+D0 : wet, W0 : sun = 0.08
+D0 : dry, W0 : sun = 0.72
+D0 : wet, W0 : rain = 0.14
+D0 : dry, W0 : rain = 0.06
+"""
+
+

tracking/test_cases/q2/6-product-rule-nonsingleton-var.test

@@ -0,0 +1,36 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "joinFactors"
+factorArgs: "A B"
+seed: "cs188"
+constructRandomly: "False"
+
+
+variableDomainsDict: """
+W0 : sun rain
+D0 : wet dry
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "D0"
+
+AconditionedVariables: "W0"
+
+AFactorTable: """
+D0 : wet, W0 : sun = 0.1
+D0 : dry, W0 : sun = 0.9
+D0 : wet, W0 : rain = 0.7
+D0 : dry, W0 : rain = 0.3
+"""
+
+
+BunconditionedVariables: "W0"
+
+BconditionedVariables: ""
+
+BFactorTable: """
+W0 : sun = 0.8
+W0 : rain = 0.2
+"""
+

tracking/test_cases/q2/CONFIG

@@ -0,0 +1,2 @@
+max_points: "3"
+class: "PassAllTestsQuestion"

tracking/test_cases/q3/1-simple-eliminate.solution

@@ -0,0 +1,16 @@
+# This is the solution file for test_cases/q4/1-simple-eliminate.test.
+variableDomainsDict: """
+D : wet dry
+W : sun rain
+"""
+
+unconditionedVariables: "D"
+
+conditionedVariables: ""
+
+FactorTable: """
+D : wet = 0.22
+D : dry = 0.78
+"""
+
+

tracking/test_cases/q3/1-simple-eliminate.test

@@ -0,0 +1,26 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "eliminate"
+factorArgs: "A"
+eliminateVariable: "W"
+seed: "cs188"
+constructRandomly: "False"
+
+variableDomainsDict: """
+W : sun rain
+D : wet dry
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "D W"
+
+AconditionedVariables: ""
+
+AFactorTable: """
+D : wet, W : sun = 0.08
+D : dry, W : sun = 0.72
+D : wet, W : rain = 0.14
+D : dry, W : rain = 0.06
+"""
+

tracking/test_cases/q3/2-simple-eliminate-extended.solution

@@ -0,0 +1,19 @@
+# This is the solution file for test_cases/q4/2-simple-eliminate-extended.test.
+variableDomainsDict: """
+D : wet dry
+W : sun rain
+T : hot cold
+"""
+
+unconditionedVariables: "D W"
+
+conditionedVariables: ""
+
+FactorTable: """
+D : wet, W : sun = 0.08
+D : dry, W : sun = 0.72
+D : wet, W : rain = 0.14
+D : dry, W : rain = 0.06
+"""
+
+

tracking/test_cases/q3/2-simple-eliminate-extended.test

@@ -0,0 +1,31 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "eliminate"
+factorArgs: "A"
+eliminateVariable: "T"
+seed: "cs188"
+constructRandomly: "False"
+
+variableDomainsDict: """
+W : sun rain
+D : wet dry
+T : hot cold
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "D W T"
+
+AconditionedVariables: ""
+
+AFactorTable: """
+D : wet, W : sun, T : hot = 0.06
+D : dry, W : sun, T : hot = 0.54
+D : wet, W : rain, T : hot = 0.014
+D : dry, W : rain, T : hot = 0.006
+D : wet, W : sun, T : cold = 0.02
+D : dry, W : sun, T : cold = 0.18
+D : wet, W : rain, T : cold = 0.126
+D : dry, W : rain, T : cold = 0.054
+"""
+

tracking/test_cases/q3/3-eliminate-conditioned.solution

@@ -0,0 +1,24 @@
+# This is the solution file for test_cases/q4/3-eliminate-conditioned.test.
+variableDomainsDict: """
+B : wind breeze still
+D : wet dry
+W : sun rain
+T : hot cold
+"""
+
+unconditionedVariables: "T W"
+
+conditionedVariables: "D"
+
+FactorTable: """
+T : hot, W : sun, D : wet = 0.431065918609
+T : cold, W : sun, D : wet = 0.1564538411
+T : hot, W : rain, D : wet = 0.293368134088
+T : cold, W : rain, D : wet = 0.119112106203
+T : hot, W : sun, D : dry = 0.332991751449
+T : cold, W : sun, D : dry = 0.0355009323002
+T : hot, W : rain, D : dry = 0.33724755396
+T : cold, W : rain, D : dry = 0.29425976229
+"""
+
+

tracking/test_cases/q3/3-eliminate-conditioned.test

@@ -0,0 +1,48 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "eliminate"
+factorArgs: "A"
+eliminateVariable: "B"
+seed: "cs188"
+constructRandomly: "False"
+
+variableDomainsDict: """
+W : sun rain
+D : wet dry
+T : hot cold
+B : wind breeze still
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "B T W"
+
+AconditionedVariables: "D"
+
+AFactorTable: """
+B : wind, W : sun, T : hot, D : wet = 0.258330124403
+B : breeze, W : sun, T : hot, D : wet = 0.172735794206
+B : still, W : sun, T : hot, D : wet = 0.0
+B : wind, W : rain, T : hot, D : wet = 0.0377614688681
+B : breeze, W : rain, T : hot, D : wet = 0.0638957615356
+B : still, W : rain, T : hot, D : wet = 0.191710903684
+B : wind, W : sun, T : cold, D : wet = 0.104734692091
+B : breeze, W : sun, T : cold, D : wet = 0.0125717596017
+B : still, W : sun, T : cold, D : wet = 0.0391473894072
+B : wind, W : rain, T : cold, D : wet = 0.0
+B : breeze, W : rain, T : cold, D : wet = 0.0667905852913
+B : still, W : rain, T : cold, D : wet = 0.0523215209113
+B : wind, W : sun, T : hot, D : dry = 0.159661284861
+B : breeze, W : sun, T : hot, D : dry = 0.110637840267
+B : still, W : sun, T : hot, D : dry = 0.062692626321
+B : wind, W : rain, T : hot, D : dry = 0.062484977125
+B : breeze, W : rain, T : hot, D : dry = 0.144114059443
+B : still, W : rain, T : hot, D : dry = 0.130648517392
+B : wind, W : sun, T : cold, D : dry = 0.0
+B : breeze, W : sun, T : cold, D : dry = 0.0355009323002
+B : still, W : sun, T : cold, D : dry = 0.0
+B : wind, W : rain, T : cold, D : dry = 0.048394873988
+B : breeze, W : rain, T : cold, D : dry = 0.131361951232
+B : still, W : rain, T : cold, D : dry = 0.11450293707
+"""
+

tracking/test_cases/q3/4-grade-eliminate.solution

@@ -0,0 +1,49 @@
+# This is the solution file for test_cases/q4/4-grade-eliminate.test.
+variableDomainsDict: """
+Q1 : myNameHasMoreThanOneLetter
+N1 : -10.5 1.34
+M1 : l m n o
+O1 : help! me! I'mTrapped InaBayesNet
+L1 : 0 1 2
+"""
+
+unconditionedVariables: "M1 O1"
+
+conditionedVariables: "Q1 N1"
+
+FactorTable: """
+M1 : l, O1 : help!, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0918451910646
+M1 : m, O1 : help!, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.09487126356
+M1 : n, O1 : help!, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0745326322505
+M1 : o, O1 : help!, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.100412148969
+M1 : l, O1 : me!, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0289208258847
+M1 : m, O1 : me!, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0425356205129
+M1 : n, O1 : me!, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0863153129512
+M1 : o, O1 : me!, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0045288246707
+M1 : l, O1 : I'mTrapped, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0502529560694
+M1 : m, O1 : I'mTrapped, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0630011671224
+M1 : n, O1 : I'mTrapped, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0632139656087
+M1 : o, O1 : I'mTrapped, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0189507724124
+M1 : l, O1 : InaBayesNet, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0749611903954
+M1 : m, O1 : InaBayesNet, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0845756398368
+M1 : n, O1 : InaBayesNet, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0466425675865
+M1 : o, O1 : InaBayesNet, Q1 : myNameHasMoreThanOneLetter, N1 : -10.5 = 0.0744399211048
+M1 : l, O1 : help!, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0371283421858
+M1 : m, O1 : help!, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0309365833611
+M1 : n, O1 : help!, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0808781051773
+M1 : o, O1 : help!, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0286955712353
+M1 : l, O1 : me!, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.070510433244
+M1 : m, O1 : me!, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0608019238205
+M1 : n, O1 : me!, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0816543942416
+M1 : o, O1 : me!, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0885972000872
+M1 : l, O1 : I'mTrapped, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0874036845943
+M1 : m, O1 : I'mTrapped, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0535472077254
+M1 : n, O1 : I'mTrapped, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0302095703475
+M1 : o, O1 : I'mTrapped, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0540061262374
+M1 : l, O1 : InaBayesNet, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0644988264685
+M1 : m, O1 : InaBayesNet, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0477258546637
+M1 : n, O1 : InaBayesNet, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0974141257874
+M1 : o, O1 : InaBayesNet, Q1 : myNameHasMoreThanOneLetter, N1 : 1.34 = 0.0859920508231
+"""
+
+

tracking/test_cases/q3/4-grade-eliminate.test

@@ -0,0 +1,122 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "eliminate"
+factorArgs: "A"
+eliminateVariable: "L1"
+seed: "cs188"
+constructRandomly: "False"
+
+
+variableDomainsDict: """
+L1 : 0 1 2
+M1 : l m n o
+N1 : -10.5 1.34
+O1 : help! me! I'mTrapped InaBayesNet
+Q1 : myNameHasMoreThanOneLetter
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "M1 O1 L1"
+
+AconditionedVariables: "Q1 N1"
+
+AFactorTable: """
+L1 : 0, M1 : l, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0447769827421
+L1 : 1, M1 : l, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0335548259369
+L1 : 2, M1 : l, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0135133823856
+L1 : 0, M1 : m, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0299407113048
+L1 : 1, M1 : m, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0190137493524
+L1 : 2, M1 : m, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0459168029028
+L1 : 0, M1 : n, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 1, M1 : n, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0481229169727
+L1 : 2, M1 : n, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0264097152778
+L1 : 0, M1 : o, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0484229139817
+L1 : 1, M1 : o, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.00296557788646
+L1 : 2, M1 : o, O1 : help!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0490236571006
+L1 : 0, M1 : l, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0181539164706
+L1 : 1, M1 : l, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0107669094141
+L1 : 2, M1 : l, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 0, M1 : m, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.00217909337527
+L1 : 1, M1 : m, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 2, M1 : m, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0403565271376
+L1 : 0, M1 : n, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.00678551130624
+L1 : 1, M1 : n, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0305996504641
+L1 : 2, M1 : n, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0489301511809
+L1 : 0, M1 : o, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 1, M1 : o, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 2, M1 : o, O1 : me!, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0045288246707
+L1 : 0, M1 : l, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.00654528636072
+L1 : 1, M1 : l, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0437076697087
+L1 : 2, M1 : l, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 0, M1 : m, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0110752062624
+L1 : 1, M1 : m, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0396237693127
+L1 : 2, M1 : m, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0123021915473
+L1 : 0, M1 : n, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0332297127389
+L1 : 1, M1 : n, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0299842528698
+L1 : 2, M1 : n, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 0, M1 : o, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0189507724124
+L1 : 1, M1 : o, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 2, M1 : o, O1 : I'mTrapped, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 0, M1 : l, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 1, M1 : l, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.039840143279
+L1 : 2, M1 : l, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0351210471164
+L1 : 0, M1 : m, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0115769730372
+L1 : 1, M1 : m, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0347270528183
+L1 : 2, M1 : m, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0382716139813
+L1 : 0, M1 : n, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0090690152544
+L1 : 1, M1 : n, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.00799625915824
+L1 : 2, M1 : n, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0295772931739
+L1 : 0, M1 : o, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0146774518464
+L1 : 1, M1 : o, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.013443479893
+L1 : 2, M1 : o, O1 : InaBayesNet, N1 : -10.5, Q1 : myNameHasMoreThanOneLetter = 0.0463189893654
+L1 : 0, M1 : l, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.00746874052442
+L1 : 1, M1 : l, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0170706440325
+L1 : 2, M1 : l, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0125889576289
+L1 : 0, M1 : m, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.024671121071
+L1 : 1, M1 : m, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.00626546229009
+L1 : 2, M1 : m, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 0, M1 : n, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0246502130155
+L1 : 1, M1 : n, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0434087985544
+L1 : 2, M1 : n, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0128190936074
+L1 : 0, M1 : o, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0242600514229
+L1 : 1, M1 : o, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0038706658879
+L1 : 2, M1 : o, O1 : help!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.000564853924528
+L1 : 0, M1 : l, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0411575250006
+L1 : 1, M1 : l, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 2, M1 : l, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0293529082434
+L1 : 0, M1 : m, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0165362781706
+L1 : 1, M1 : m, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.026525369977
+L1 : 2, M1 : m, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0177402756729
+L1 : 0, M1 : n, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0488609903771
+L1 : 1, M1 : n, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0072654289765
+L1 : 2, M1 : n, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.025527974888
+L1 : 0, M1 : o, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0391811589986
+L1 : 1, M1 : o, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 2, M1 : o, O1 : me!, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0494160410886
+L1 : 0, M1 : l, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0552756677955
+L1 : 1, M1 : l, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 2, M1 : l, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0321280167988
+L1 : 0, M1 : m, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 1, M1 : m, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.005603470804
+L1 : 2, M1 : m, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0479437369214
+L1 : 0, M1 : n, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 1, M1 : n, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 2, M1 : n, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0302095703475
+L1 : 0, M1 : o, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0227207214722
+L1 : 1, M1 : o, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0161232920219
+L1 : 2, M1 : o, O1 : I'mTrapped, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0151621127433
+L1 : 0, M1 : l, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 1, M1 : l, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0101119740519
+L1 : 2, M1 : l, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0543868524166
+L1 : 0, M1 : m, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 1, M1 : m, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0379246424001
+L1 : 2, M1 : m, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.00980121226356
+L1 : 0, M1 : n, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 1, M1 : n, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0484577738418
+L1 : 2, M1 : n, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0489563519456
+L1 : 0, M1 : o, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0411582588411
+L1 : 1, M1 : o, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.0
+L1 : 2, M1 : o, O1 : InaBayesNet, N1 : 1.34, Q1 : myNameHasMoreThanOneLetter = 0.044833791982
+"""
+

tracking/test_cases/q3/5-simple-eliminate-nonsingleton-var.solution

@@ -0,0 +1,16 @@
+# This is the solution file for test_cases/q4/5-simple-eliminate-nonsingleton-var.test.
+variableDomainsDict: """
+D0 : wet dry
+W0 : sun rain
+"""
+
+unconditionedVariables: "D0"
+
+conditionedVariables: ""
+
+FactorTable: """
+D0 : wet = 0.22
+D0 : dry = 0.78
+"""
+
+

tracking/test_cases/q3/5-simple-eliminate-nonsingleton-var.test

@@ -0,0 +1,26 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "eliminate"
+factorArgs: "A"
+eliminateVariable: "W0"
+seed: "cs188"
+constructRandomly: "False"
+
+variableDomainsDict: """
+W0 : sun rain
+D0 : wet dry
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "D0 W0"
+
+AconditionedVariables: ""
+
+AFactorTable: """
+D0 : wet, W0 : sun = 0.08
+D0 : dry, W0 : sun = 0.72
+D0 : wet, W0 : rain = 0.14
+D0 : dry, W0 : rain = 0.06
+"""
+

tracking/test_cases/q3/6-simple-eliminate-int.solution

@@ -0,0 +1,16 @@
+# This is the solution file for test_cases/q4/6-simple-eliminate-int.test.
+variableDomainsDict: """
+D : wet dry
+W : sun rain
+"""
+
+unconditionedVariables: "D"
+
+conditionedVariables: ""
+
+FactorTable: """
+D : wet = 2.0
+D : dry = 0.0
+"""
+
+

tracking/test_cases/q3/6-simple-eliminate-int.test

@@ -0,0 +1,26 @@
+class: "FactorInputFactorEqualityTest"
+max_points: "4"
+alg: "eliminate"
+factorArgs: "A"
+eliminateVariable: "W"
+seed: "cs188"
+constructRandomly: "False"
+
+variableDomainsDict: """
+W : sun rain
+D : wet dry
+"""
+
+# endOfNonFactors
+
+AunconditionedVariables: "D W"
+
+AconditionedVariables: ""
+
+AFactorTable: """
+D : wet, W : sun = 1.0
+D : dry, W : sun = 0.0
+D : wet, W : rain = 1.0
+D : dry, W : rain = 0.0
+"""
+

tracking/test_cases/q3/CONFIG

@@ -0,0 +1,2 @@
+max_points: "2"
+class: "PassAllTestsQuestion"

tracking/test_cases/q4/1-disconnected-eliminate.solution

@@ -0,0 +1,18 @@
+# This is the solution file for test_cases/q6/1-disconnected-eliminate.test.
+variableDomainsDict: """
+D : dry wet
+W : sun
+T : hot cold
+"""
+
+unconditionedVariables: "D"
+
+conditionedVariables: "W"
+
+FactorTable: """
+D : dry, W : sun = 0.9
+D : wet, W : sun = 0.1
+"""
+
+
+callTrackingList: "[('join', 'T')]"

tracking/test_cases/q4/1-disconnected-eliminate.test

@@ -0,0 +1,66 @@
+class: "BayesNetInputFactorEqualityTest"
+max_points: "4"
+alg: "inferenceByVariableElimination"
+seed: "cs188"
+constructRandomly: "False"
+
+variables: """
+W
+D
+T
+"""
+
+edges: """
+W D
+W T
+"""
+
+variableDomainsDict: """
+W : sun rain
+D : dry wet
+T : hot cold
+"""
+
+queryVariables: "D"
+
+evidenceDict: """
+W : sun
+"""
+
+variableEliminationOrder: "T"
+
+# endOfNonFactors
+
+DunconditionedVariables: "D"
+
+DconditionedVariables: "W"
+
+DFactorTable: """
+D : dry, W : sun = 0.9
+D : wet, W : sun = 0.1
+D : dry, W : rain = 0.3
+D : wet, W : rain = 0.7
+"""
+
+
+WunconditionedVariables: "W"
+
+WconditionedVariables: ""
+
+WFactorTable: """
+W : sun = 0.8
+W : rain = 0.2
+"""
+
+
+TunconditionedVariables: "T"
+
+TconditionedVariables: "W"
+
+TFactorTable: """
+T : hot, W : sun = 0.75
+T : cold, W : sun = 0.25
+T : hot, W : rain = 0.1
+T : cold, W : rain = 0.9
+"""
+

tracking/test_cases/q4/2-independent-eliminate.solution

@@ -0,0 +1,18 @@
+# This is the solution file for test_cases/q6/2-independent-eliminate.test.
+variableDomainsDict: """
+D : dry wet
+W : sun rain
+T : hot cold
+"""
+
+unconditionedVariables: "W"
+
+conditionedVariables: ""
+
+FactorTable: """
+W : sun = 0.8
+W : rain = 0.2
+"""
+
+
+callTrackingList: "[('join', 'T'), ('eliminate', 'T'), ('join', 'D')]"

tracking/test_cases/q4/2-independent-eliminate.test

@@ -0,0 +1,68 @@
+class: "BayesNetInputFactorEqualityTest"
+max_points: "4"
+alg: "inferenceByVariableElimination"
+seed: "cs188"
+constructRandomly: "False"
+
+variables: """
+W
+D
+T
+"""
+
+edges: """
+W D
+T D
+"""
+
+variableDomainsDict: """
+W : sun rain
+D : dry wet
+T : hot cold
+"""
+
+queryVariables: "W"
+
+variableEliminationOrder: "T D"
+
+evidenceDict: """
+
+"""
+
+# endOfNonFactors
+
+DunconditionedVariables: "D"
+
+DconditionedVariables: "T W"
+
+DFactorTable: """
+D : dry, T : hot, W : sun = 0.9
+D : wet, T : hot, W : sun = 0.1
+D : dry, T : cold, W : sun = 0.8
+D : wet, T : cold, W : sun = 0.2
+D : dry, T : hot, W : rain = 0.5
+D : wet, T : hot, W : rain = 0.5
+D : dry, T : cold, W : rain = 0.3
+D : wet, T : cold, W : rain = 0.7
+"""
+
+
+WunconditionedVariables: "W"
+
+WconditionedVariables: ""
+
+WFactorTable: """
+W : sun = 0.8
+W : rain = 0.2
+"""
+
+
+TunconditionedVariables: "T"
+
+TconditionedVariables: ""
+
+TFactorTable: """
+T : hot = 0.75
+T : cold = 0.25
+"""
+

tracking/test_cases/q4/3-independent-eliminate-extended.solution

@@ -0,0 +1,19 @@
+# This is the solution file for test_cases/q6/3-independent-eliminate-extended.test.
+variableDomainsDict: """
+S : slippery rough
+D : dry wet
+W : sun rain
+T : hot
+"""
+
+unconditionedVariables: "W"
+
+conditionedVariables: "T"
+
+FactorTable: """
+W : sun, T : hot = 0.8
+W : rain, T : hot = 0.2
+"""
+
+
+callTrackingList: "[('join', 'S'), ('join', 'D')]"

tracking/test_cases/q4/3-independent-eliminate-extended.test

@@ -0,0 +1,83 @@
+class: "BayesNetInputFactorEqualityTest"
+max_points: "4"
+alg: "inferenceByVariableElimination"
+seed: "cs188"
+constructRandomly: "False"
+
+variables: """
+W
+D
+T
+S
+"""
+
+edges: """
+W D
+T D
+D S
+"""
+
+variableDomainsDict: """
+W : sun rain
+D : dry wet
+T : hot cold
+S : slippery rough
+"""
+
+queryVariables: "W"
+
+variableEliminationOrder: "S D"
+
+evidenceDict: """
+T : hot
+"""
+
+# endOfNonFactors
+
+SunconditionedVariables: "S"
+
+SconditionedVariables: "D"
+
+SFactorTable: """
+S : slippery, D : dry = 0.1
+S : rough, D : dry = 0.9
+S : slippery, D : wet = 0.5
+S : rough, D : wet = 0.5
+"""
+
+
+DunconditionedVariables: "D"
+
+DconditionedVariables: "T W"
+
+DFactorTable: """
+D : dry, T : hot, W : sun = 0.9
+D : wet, T : hot, W : sun = 0.1
+D : dry, T : cold, W : sun = 0.8
+D : wet, T : cold, W : sun = 0.2
+D : dry, T : hot, W : rain = 0.5
+D : wet, T : hot, W : rain = 0.5
+D : dry, T : cold, W : rain = 0.3
+D : wet, T : cold, W : rain = 0.7
+"""
+
+
+WunconditionedVariables: "W"
+
+WconditionedVariables: ""
+
+WFactorTable: """
+W : sun = 0.8
+W : rain = 0.2
+"""
+
+
+TunconditionedVariables: "T"
+
+TconditionedVariables: ""
+
+TFactorTable: """
+T : hot = 0.75
+T : cold = 0.25
+"""
+

tracking/test_cases/q4/4-common-effect-eliminate.solution

@@ -0,0 +1,19 @@
+# This is the solution file for test_cases/q6/4-common-effect-eliminate.test.
+variableDomainsDict: """
+S : slippery
+D : dry wet
+W : sun rain
+T : hot cold
+"""
+
+unconditionedVariables: "W"
+
+conditionedVariables: "S"
+
+FactorTable: """
+W : sun, S : slippery = 0.652173913043
+W : rain, S : slippery = 0.347826086957
+"""
+
+
+callTrackingList: "[('join', 'T'), ('eliminate', 'T'), ('join', 'D'), ('eliminate', 'D')]"

tracking/test_cases/q4/4-common-effect-eliminate.test

@@ -0,0 +1,83 @@
+class: "BayesNetInputFactorEqualityTest"
+max_points: "4"
+alg: "inferenceByVariableElimination"
+seed: "cs188"
+constructRandomly: "False"
+
+variables: """
+W
+D
+T
+S
+"""
+
+edges: """
+W D
+T D
+D S
+"""
+
+variableDomainsDict: """
+W : sun rain
+D : dry wet
+T : hot cold
+S : slippery rough
+"""
+
+queryVariables: "W"
+
+variableEliminationOrder: "T D"
+
+evidenceDict: """
+S : slippery
+"""
+
+# endOfNonFactors
+
+SunconditionedVariables: "S"
+
+SconditionedVariables: "D"
+
+SFactorTable: """
+S : slippery, D : dry = 0.1
+S : rough, D : dry = 0.9
+S : slippery, D : wet = 0.5
+S : rough, D : wet = 0.5
+"""
+
+
+DunconditionedVariables: "D"
+
+DconditionedVariables: "T W"
+
+DFactorTable: """
+D : dry, T : hot, W : sun = 0.9
+D : wet, T : hot, W : sun = 0.1
+D : dry, T : cold, W : sun = 0.8
+D : wet, T : cold, W : sun = 0.2
+D : dry, T : hot, W : rain = 0.5
+D : wet, T : hot, W : rain = 0.5
+D : dry, T : cold, W : rain = 0.3
+D : wet, T : cold, W : rain = 0.7
+"""
+
+
+WunconditionedVariables: "W"
+
+WconditionedVariables: ""
+
+WFactorTable: """
+W : sun = 0.8
+W : rain = 0.2
+"""
+
+
+TunconditionedVariables: "T"
+
+TconditionedVariables: ""
+
+TFactorTable: """
+T : hot = 0.75
+T : cold = 0.25
+"""
+

tracking/test_cases/q4/5-grade-var-elim.solution

@@ -0,0 +1,21 @@
+# This is the solution file for test_cases/q6/5-grade-var-elim.test.
+variableDomainsDict: """
+A : 0 1 2
+C : -10.5 1.34
+B : m
+E : singleEntryForThisVariable
+D : help! me! I'mTrapped InaBayesNet
+"""
+
+unconditionedVariables: "A"
+
+conditionedVariables: "B"
+
+FactorTable: """
+A : 0, B : m = 0.516311327629
+A : 1, B : m = 0.387878666494
+A : 2, B : m = 0.0958100058765
+"""
+
+
+callTrackingList: "[('join', 'C'), ('join', 'D'), ('join', 'E')]"

tracking/test_cases/q4/5-grade-var-elim.test

@@ -0,0 +1,121 @@
+class: "BayesNetInputFactorEqualityTest"
+max_points: "4"
+alg: "inferenceByVariableElimination"
+seed: "cs188"
+constructRandomly: "False"
+
+variables: """
+A
+B
+C
+D
+E
+"""
+
+edges: """
+A B
+B C
+A C
+"""
+
+variableDomainsDict: """
+A : 0 1 2
+B : l m n o
+C : -10.5 1.34
+D : help! me! I'mTrapped InaBayesNet
+E : singleEntryForThisVariable
+"""
+
+queryVariables: "A"
+
+evidenceDict: """
+B : m
+"""
+
+variableEliminationOrder: "C D E"
+
+# endOfNonFactors
+
+AunconditionedVariables: "A"
+
+AconditionedVariables: ""
+
+AFactorTable: """
+A : 0 = 0.644493971172
+A : 1 = 0.261296965582
+A : 2 = 0.0942090632453
+"""
+
+
+CunconditionedVariables: "C"
+
+CconditionedVariables: "A B"
+
+CFactorTable: """
+C : -10.5, A : 0, B : l = 0.0
+C : 1.34, A : 0, B : l = 1.0
+C : -10.5, A : 1, B : l = 0.157744145568
+C : 1.34, A : 1, B : l = 0.842255854432
+C : -10.5, A : 2, B : l = 0.676933020022
+C : 1.34, A : 2, B : l = 0.323066979978
+C : -10.5, A : 0, B : m = 0.164406527303
+C : 1.34, A : 0, B : m = 0.835593472697
+C : -10.5, A : 1, B : m = 0.0
+C : 1.34, A : 1, B : m = 1.0
+C : -10.5, A : 2, B : m = 0.0
+C : 1.34, A : 2, B : m = 1.0
+C : -10.5, A : 0, B : n = 1.0
+C : 1.34, A : 0, B : n = 0.0
+C : -10.5, A : 1, B : n = 0.304307665342
+C : 1.34, A : 1, B : n = 0.695692334658
+C : -10.5, A : 2, B : n = 0.419155889929
+C : 1.34, A : 2, B : n = 0.580844110071
+C : -10.5, A : 0, B : o = 0.169573242888
+C : 1.34, A : 0, B : o = 0.830426757112
+C : -10.5, A : 1, B : o = 0.19765016247
+C : 1.34, A : 1, B : o = 0.80234983753
+C : -10.5, A : 2, B : o = 0.505078886969
+C : 1.34, A : 2, B : o = 0.494921113031
+"""
+
+
+BunconditionedVariables: "B"
+
+BconditionedVariables: "A"
+
+BFactorTable: """
+B : l, A : 0 = 0.729463421654
+B : m, A : 0 = 0.270536578346
+B : n, A : 0 = 0.0
+B : o, A : 0 = 0.0
+B : l, A : 1 = 0.498703437888
+B : m, A : 1 = 0.501296562112
+B : n, A : 1 = 0.0
+B : o, A : 1 = 0.0
+B : l, A : 2 = 0.262692210105
+B : m, A : 2 = 0.343440399016
+B : n, A : 2 = 0.301851629618
+B : o, A : 2 = 0.0920157612613
+"""
+
+
+EunconditionedVariables: "E"
+
+EconditionedVariables: ""
+
+EFactorTable: """
+E : singleEntryForThisVariable = 1.0
+"""
+
+
+DunconditionedVariables: "D"
+
+DconditionedVariables: ""
+
+DFactorTable: """
+D : help! = 0.251719693616
+D : me! = 0.466369384686
+D : I'mTrapped = 0.0
+D : InaBayesNet = 0.281910921698
+"""
+

tracking/test_cases/q4/6-large-bayesNet-elim.solution

@@ -0,0 +1,38 @@
+# This is the solution file for test_cases/q6/6-large-bayesNet-elim.test.
+variableDomainsDict: """
+X1 : 0 1
+Y1 : water
+Y8 : ice
+Y5 : water
+Z : evil good indifferent
+Y10 : ice
+Y4 : ice
+Y9 : water
+X10 : 0 1
+X8 : 0 1
+X9 : 0 1
+Y7 : water
+Y6 : ice
+X2 : 0 1
+X3 : 0 1
+Y3 : water
+Y2 : ice
+X6 : 0 1
+X7 : 0 1
+X4 : 0 1
+X5 : 0 1
+"""
+
+unconditionedVariables: "X2 X1"
+
+conditionedVariables: "Y4 Y9 Y8 Y1 Y3 Y2 Y5 Y10 Y7 Y6"
+
+FactorTable: """
+X1 : 0, X2 : 0, Y10 : ice, Y9 : water, Y8 : ice, Y1 : water, Y3 : water, Y2 : ice, Y5 : water, Y4 : ice, Y7 : water, Y6 : ice = 1.0
+X1 : 1, X2 : 0, Y10 : ice, Y9 : water, Y8 : ice, Y1 : water, Y3 : water, Y2 : ice, Y5 : water, Y4 : ice, Y7 : water, Y6 : ice = 0.0
+X1 : 0, X2 : 1, Y10 : ice, Y9 : water, Y8 : ice, Y1 : water, Y3 : water, Y2 : ice, Y5 : water, Y4 : ice, Y7 : water, Y6 : ice = 0.0
+X1 : 1, X2 : 1, Y10 : ice, Y9 : water, Y8 : ice, Y1 : water, Y3 : water, Y2 : ice, Y5 : water, Y4 : ice, Y7 : water, Y6 : ice = 0.0
+"""
+
+
+callTrackingList: "[('join', 'X3'), ('eliminate', 'X3'), ('join', 'X4'), ('eliminate', 'X4'), ('join', 'X5'), ('eliminate', 'X5'), ('join', 'X6'), ('eliminate', 'X6'), ('join', 'X7'), ('eliminate', 'X7'), ('join', 'X8'), ('eliminate', 'X8'), ('join', 'X9'), ('eliminate', 'X9'), ('join', 'X10'), ('eliminate', 'X10'), ('join', 'Z'), ('eliminate', 'Z')]"