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knn.py

+import numpy as np
+import pickle
+import os
+from read_cifar import *
+import matplotlib.pyplot as plt
+
+
+def distance_matrix (M1, M2) :
+    sum_squares_1 = np.sum(M1**2, axis = 1, keepdims = True)
+    sum_squares_2 = np.sum(M2**2, axis = 1, keepdims = True)
+
+    dot_product = np.dot(M1, M2.T)
+    dists = np.sqrt(sum_squares_1 - 2*dot_product + sum_squares_2.T)
+
+    return dists
+
+
+def k_smallest_indexes (liste, k) :
+    if k <= 0 or k > len(liste) : 
+        return []
+    
+    indexes = list(range(len(liste)))
+    indexes.sort(key=lambda i: liste[i])
+
+    k_smallest_indexes = indexes[:k]
+
+    return k_smallest_indexes
+
+
+def knn_predict (data_train, labels_train, data_test, k) :
+    
+    dists = distance_matrix(data_train, data_test)
+
+    predicted_labels = []
+
+    for i in range (len(data_test)) :
+        distance = dists[i]
+        labels = []
+        k_nearest_neighbors = k_smallest_indexes(distance, k)
+        for j in k_nearest_neighbors :
+            labels.append(labels_train[j])
+        predicted_label = max(labels, key=labels.count)
+        predicted_labels.append(predicted_label)
+
+    return predicted_labels
+
+def evaluate_knn (data_train, labels_train, data_test, labels_test, k) :
+    predicted_labels = knn_predict(data_train, labels_train, data_test, k)
+    accuracy = 0
+    for i in range (len(predicted_labels)) :
+        if predicted_labels[i] == labels_test[i] :
+            accuracy +=1 
+    accuracy_rate = accuracy/len(predicted_labels)
+    return accuracy_rate
+
+
+
+if __name__ == "__main__":
+    
+    K = 50
+    split = 0.9
+
+    batch_dir = 'data/cifar-10-batches-py/'
+    data, labels = read_cifar(batch_dir)
+    data_train, labels_train, data_test, labels_test = split_dataset(data, labels, split)
+    accuracy = evaluate_knn (data_train, labels_train, data_test, labels_test, K)
+    print(accuracy)
+
+    #k = list(range(20))
+    #k = [x+1 for x in k]
+    #accuracy_vector = []
+
+    #for i in k :
+        #accuracy_vector.append(evaluate_knn (data_train, labels_train, data_test, labels_test, i))
+
+    #plt.plot(k, accuracy_vector)
+    #plt.show()  
+
+
+
+    
+
+   
+    
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