README.md

 # Image classification
 
-## k-nearest neighbors
-
-
+The codes are available in the different .py files.
+This readme.md file will only present the mathematical part of the Artificial Neural Network.
 
 ## Artificial Neural Network
 

knn.py

@@ -3,26 +3,21 @@ import read_cifar
 import matplotlib.pyplot as plt
 
 def distance_matrix(A,B):
-
+     # Calculating the squared sum of elements in each row for matrices A and 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)
 
-
+    # Computing the Euclidean distance matrix
     dists=np.sqrt(sum_of_squares_A+sum_of_squares_B-2*dot_product)
 
     return dists
 
-
-
-#def knn_predict(dists, labels_train, k):
-    #
-    # 
 def knn_predict(dists, labels_train, k):
     predicted_labels = []
-    # For every image in the test set
+    # Iterating through each test data point's distances to train data
     for i in range(len(dists)):
-        # Initialize an array to store the neighbors
+        # Counting the frequency of each class among the k nearest neighbors
         classes = [0] * 10
         # indexes of the closest neighbors
         indexes_closest_nb = np.argsort(dists[i])[:k]
@@ -37,8 +32,7 @@ def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
     rate = 0
     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))
+    # Comparing predictions to actual test labels to calculate accuracy
     for j in range(len(prediction)):
         if prediction[j]==labels_test[j]:
             rate +=1
@@ -52,11 +46,15 @@ def knn_final():
     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)
     
+    # Testing KNN for different values of k and storing the accuracy
+
     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)
 
+    # Plotting the accuracy as a function of k
+
     plt.figure(figsize=(10, 7))
     plt.xlabel('k')
     plt.ylabel('Accuracy rate')
@@ -73,6 +71,3 @@ def knn_final():
 if __name__ == "__main__" :
 
     knn_final()
\ No newline at end of file
-    #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