Modif

knn.py

@@ -3,28 +3,24 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 def distance_matrix(matrix1, matrix2):
-    #X_test then X_train in this order
-    sum_of_squares_matrix1 = np.sum(np.square(matrix1), axis=1, keepdims=True) #A^2
-    sum_of_squares_matrix2 = np.sum(np.square(matrix2), axis=1, keepdims=True) #B^2
+    sum_of_squares_matrix1 = np.sum(np.square(matrix1), axis=1, keepdims=True) 
+    sum_of_squares_matrix2 = np.sum(np.square(matrix2), axis=1, keepdims=True) 
 
-    dot_product = np.dot(matrix1, matrix2.T) # A * B (matrix mutliplication)
+    dot_product = np.dot(matrix1, matrix2.T)
     
-    dists = np.sqrt(sum_of_squares_matrix1 + sum_of_squares_matrix2.T - 2 * dot_product) # Compute the product
+    dists = np.sqrt(sum_of_squares_matrix1 + sum_of_squares_matrix2.T - 2 * dot_product)
     return dists
 
 def knn_predict(dists, labels_train, k):
     output = []
-    # Loop on all the images_test
+    
     for i in range(len(dists)):
-        # Innitialize table to store the neighbors
+        
         res = [0] * 10
-        # Get the closest neighbors
         labels_close = np.argsort(dists[i])[:k]
         for label in labels_close:
-            #add a label to the table of result
             res[labels_train[label]] += 1
-        # Get the class with the maximum neighbors
-        label_temp = np.argmax(res) #Careful to the logic here, if there is two or more maximum, the function the first maximum encountered
+        label_temp = np.argmax(res) 
         output.append(label_temp)
     return(np.array(output))
 
@@ -32,29 +28,22 @@ def evaluate_knn(data_train, labels_train, data_test, labels_tests, k):
     dist = distance_matrix(data_test, data_train)
     result_test = knn_predict(dist, labels_train, k)
  
-    #accuracy 
     N = labels_tests.shape[0]
     accuracy = (labels_tests == result_test).sum() / N
     return(accuracy)
 
-def bench_knn() :
+def test_knn() :
 
     k_indices = [i for i in range(20) if i % 2 != 0]
     accuracies = []
 
-    # Load data
     data, labels = read_cifar.read_cifar('/Users/milancart/Documents/GitHub/image-classification/Data/cifar-10-batches-py')
     X_train, X_test, y_train, y_test = read_cifar.split_dataset(data, labels, 0.9)
-    #Load one batch
-    # data, labels = read_cifar.read_cifar_batch('image-classification/data/cifar-10-batches-py/data_batch_1')
-    # X_train, X_test, y_train, y_test = read_cifar.split_dataset(data, labels, 0.9)
     
-    # Loop on the k_indices to get all the accuracies
     for k in k_indices :
         accuracy = evaluate_knn(X_train, y_train, X_test, y_test, k)
         accuracies.append(accuracy)
     
-    # Save and show the graph of accuracies
     plt.figure(figsize=(8, 6))
     plt.xlabel('K')
     plt.ylabel('Accuracy')
@@ -66,15 +55,6 @@ def bench_knn() :
 
 
 if __name__ == "__main__":
-    print('milan')
-    bench_knn()
-    data, labels = read_cifar.read_cifar('/Users/milancart/Documents/GitHub/image-classification/Data/cifar-10-batches-py')
-    X_train, X_test, y_train, y_test = read_cifar.split_dataset(data, labels, 0.9)
-    print(evaluate_knn(X_train, y_train, X_test, y_test, 5))
-    print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
-
-    y_test = []
-    x_test = np.array([[1,2],[4,6]])
-    x_train = np.array([[2,4],[7,2],[4,6]])
-    y_train = [1,2,1]
-    dist = distance_matrix(x_test,x_train)      
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+    
+    test_knn()
+    
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