exchange data test and train

knn.py

@@ -2,11 +2,16 @@ import numpy as np
 import read_cifar
 import matplotlib.pyplot as plt
 
-def distance_matrix(a,b):
-    sum_a = np.sum(a**2, axis=1, keepdims=True)
-    sum_b = np.sum(b**2, axis=1, keepdims=True)
-    dist = np.sqrt(-2 * a.dot(b.T) + sum_a + sum_b)
-    return dist
+def distance_matrix(A,B):
+
+    sum_of_squares_A = np.sum(A**2, axis=1,keepdims=True)
+    sum_of_squares_B = np.sum(B**2, axis=1,keepdims=True).T
+    dot_product = np.dot(A, B.T)
+
+
+    dists=np.sqrt(sum_of_squares_A+sum_of_squares_B-2*dot_product)
+
+    return dists
 
 
 
@@ -30,8 +35,10 @@ def knn_predict(dists, labels_train, k):
 
 def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
     rate = 0
-    dist_train_test = distance_matrix(data_train, data_test)
+    dist_train_test = distance_matrix(data_test, data_train)
     prediction = knn_predict(dist_train_test, labels_train, k)
+    print(len(prediction))
+    print(len(labels_test))
     for j in range(len(prediction)):
         if prediction[j]==labels_test[j]:
             rate +=1
@@ -46,6 +53,7 @@ def knn_final():
     data_train_f, labels_train_f, data_test_f, labels_test_f = read_cifar.split_dataset(data, labels, 0.9)
 
     for k in range_k :
+        print(k)
         rate_k = evaluate_knn(data_train_f, labels_train_f, data_test_f, labels_test_f, k)
         rates.append(rate_k)
 
@@ -68,4 +76,3 @@ if __name__ == "__main__" :
     #a1 = np.array([[0,0,1],[0,0,0],[1,1,2]])
     #b1 = np.array([[1,3,1], [1,1,4], [1,5,1]])
     #print(distance_matrix(a1,b1))
\ No newline at end of file
-