knn final

knn.py

 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)
@@ -27,12 +29,43 @@ def knn_predict(dists, labels_train, k):
     return(np.array(predicted_labels))
 
 def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
+    rate = 0
+    dist_train_test = distance_matrix(data_train, data_test)
+    prediction = knn_predict(dist_train_test, labels_train, k)
+    for j in range(len(prediction)):
+        if prediction[j]==labels_test[j]:
+            rate +=1
+    rate = rate/len(prediction)
+    return rate
+
+def knn_final():
+    range_k = range(1,20)
+    rates = []
+
+    data,labels = read_cifar.read_cifar("data/cifar-10-batches-py")
+    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 :
+        rate_k = evaluate_knn(data_train_f, labels_train_f, data_test_f, labels_test_f, k)
+        rates.append(rate_k)
+
+    plt.figure(figsize=(10, 7))
+    plt.xlabel('k')
+    plt.ylabel('Accuracy rate')
+    plt.plot(range_k, rates)
+    plt.title("Accuracy rate = f(k)")
+    plt.legend()
+    plt.grid(True)
+    plt.show()
 
 
 
 
 
 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))
+
+    knn_final()
+    #a1 = np.array([[0,0,1],[0,0,0],[1,1,2]])
\ No newline at end of file
+    #b1 = np.array([[1,3,1], [1,1,4], [1,5,1]])
+    #print(distance_matrix(a1,b1))
+