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knn.py

+import numpy as np
+import pickle
+import os
+
+
+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
+
+split_dataset (data, labels, split) 
+
+def knn_predict (dists, labels_train, k) :
+    train_set
+    test_set
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