25/10 It seems working but accuracy 0.35

knn.py

-import numpy as np
+from read_cifar import *
+from collections import Counter
 
 def compute_distance(m1, m2):
     if m1.shape != m2.shape:
         raise ValueError("Dimensions must be identical")
-
+    #distance = np.linalg.norm(m1 - m2)
     x = (m1 - m2) ** 2
     y = np.sum(x)
     dist = np.sqrt(y)
 
     return dist
-
 def distance_matrix(data_train, data_test):
     dists = []
 
@@ -21,11 +21,48 @@ def distance_matrix(data_train, data_test):
 
     return dists
 
+def knn_predict(dists, labels_train, k):
+    predictions=[]
+    for distances in dists:
+        min_indexes = np.argpartition(distances, k)[:k]
+        possible_pred = labels_train[min_indexes]
+        counted = Counter(possible_pred)
+        pred = counted.most_common(1)[0][0]
+        predictions.append(pred)
+    return predictions
 
-    return dists
+def evaluate_knn(predictions, labels_test):
+    sum=0
+    for i in range(len(predictions)):
+        if predictions[i] == labels_test[i]:
+            sum+=1
+
+    return sum / len(predictions)
+
+'''def evaluate_knn(data_train , labels_train,data_test ,labels_test, k):
+        
+    return'''
+
+def main():
+    folder_path = 'data/cifar-10-batches-py'
+    data, labels = read_cifar(folder_path)
+
+    print((data.shape))
+    print((labels.shape))
+
+    data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
+
+    print("Training set shape:", data_train.shape, labels_train.shape)
+    print("Testing set shape:", data_test.shape, labels_test.shape)
+
+    dists=distance_matrix(data_train, data_test)
+
+    prediction=knn_predict(dists, labels_train, 4)
+
+    accuracy = evaluate_knn(prediction, labels_test)
+
+    print(accuracy)
 
-def knn_predict(dist, labels_train, k):
-    return
+if __name__ == "__main__":
+    main()
 
-def evaluate_knn(data_train , labels_train,data_test ,labels_test, k):
-    return
\ No newline at end of file

read_cifar.py

@@ -3,7 +3,6 @@ import numpy as np
 import os
 from sklearn.model_selection import train_test_split
 
-
 def read_cifar_batch(batch):
     with open(batch, 'rb') as fo:
         dict = pickle.load(fo, encoding='bytes')
@@ -21,30 +20,11 @@ def read_cifar(path):
         data_batch, labels_batch = read_cifar_batch(path + '/' + batch)
         data.append(data_batch)
         labels.append(labels_batch)
-    return np.array(data, dtype=np.float32).reshape((60000,3072)), np.array(labels, dtype=np.int64).reshape(-1)
+    return np.array(data, dtype=np.float32).reshape((60000, 3072)), np.array(labels, dtype=np.int64).reshape(-1)
 
 def split_dataset(data, labels, split):
     data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=1-split, shuffle=True)
-
     return data_train, data_test, labels_train, labels_test
 
 
-def main():
-    folder_path = 'data/cifar-10-batches-py'
-    data, labels = read_cifar(folder_path)
-
-    print((data.shape))
-    print((labels.shape))
-
-    data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
-
-    print("Training set shape:", data_train.shape, labels_train.shape)
-    print("Testing set shape:", data_test.shape, labels_test.shape)
-
-
-
-
-
-if __name__ == "__main__":
-    main()