From c48da75a0036783698689a1377bfcf032c3e1bf4 Mon Sep 17 00:00:00 2001
From: ZhuangYumin <zhuangyumin@sjtu.edu.cn>
Date: Fri, 5 Jul 2024 00:56:40 +0800
Subject: [PATCH] ml q1

---
 machinelearning/models.py | 22 ++++++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/machinelearning/models.py b/machinelearning/models.py
index c87d5bd..9904ddd 100644
--- a/machinelearning/models.py
+++ b/machinelearning/models.py
@@ -37,7 +37,11 @@ class PerceptronModel(Module):
         super(PerceptronModel, self).__init__()
         
         "*** YOUR CODE HERE ***"
-        self.w = None #Initialize your weights here
+        weight_vector = ones((1, dimensions))
+        self.w = Parameter(weight_vector)
+        # print(f"self.w: {self.w}")
+        "*** END YOUR CODE HERE ***"
+
 
     def get_weights(self):
         """
@@ -56,6 +60,9 @@ class PerceptronModel(Module):
         The pytorch function `tensordot` may be helpful here.
         """
         "*** YOUR CODE HERE ***"
+        # Calculate the dot product of the weight vector and the input vector
+        # print(f"the dot product of the weight vector and the input vector: {tensordot(x, self.w, dims=1)}")
+        return tensordot(x, self.w, dims=([1],[1]))
 
 
     def get_prediction(self, x):
@@ -65,6 +72,7 @@ class PerceptronModel(Module):
         Returns: 1 or -1
         """
         "*** YOUR CODE HERE ***"
+        return 1 if self.run(x) >= 0 else -1
 
 
 
@@ -79,7 +87,17 @@ class PerceptronModel(Module):
         """        
         with no_grad():
             dataloader = DataLoader(dataset, batch_size=1, shuffle=True)
-            "*** YOUR CODE HERE ***"
+            all_correct = False
+            
+            while not all_correct:
+                all_correct = True
+                for sample in dataloader:
+                    x = sample['x']
+                    y = sample['label']
+                    prediction = self.get_prediction(x)
+                    if prediction != y:
+                        all_correct = False
+                        self.w += y * x