Read cifar finished and distance matrix computed

knn.py

 import numpy as np
 
-def distance_matrix(m1, m2):
+def compute_distance(m1, m2):
     if m1.shape != m2.shape:
         raise ValueError("Dimensions must be identical")
 
@@ -10,7 +10,22 @@ def distance_matrix(m1, m2):
 
     return dist
 
-def knn_predict(dist, labels_train, k, ):
+def distance_matrix(data_train, data_test):
+    dists = []
+
+    for test in data_test:
+        dist = []
+        for train in data_train:
+            dist.append(compute_distance(test, train))
+        dists.append(dist)
+
+    return dists
+
+
+    return dists
+
+def knn_predict(dist, labels_train, k):
     return
+
 def evaluate_knn(data_train , labels_train,data_test ,labels_test, k):
     return
\ No newline at end of file

read_cifar.py

 import pickle
 import numpy as np
+import os
+from sklearn.model_selection import train_test_split
+
 
 def read_cifar_batch(batch):
     with open(batch, 'rb') as fo:
         dict = pickle.load(fo, encoding='bytes')
         data = dict[b'data']
         labels = dict[b'labels']
-    return data.astype(np.float32), np.array(labels, dtype=np.int64)
+        print(dict[b'batch_label'])
+    return data, labels
+
+def read_cifar(path):
+    batches_list = os.listdir(path)
+    data, labels = [], []
+    for batch in batches_list:
+        if(batch == 'batches.meta' or batch == 'readme.html'):
+            continue
+        data_batch, labels_batch = read_cifar_batch(path + '/' + batch)
+        data.append(data_batch)
+        labels.append(labels_batch)
+    return np.array(data, dtype=np.float32).reshape((60000,3072)), np.array(labels, dtype=np.int64).reshape(-1)
+
+def split_dataset(data, labels, split):
+    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=1-split, shuffle=True)
+
+    return data_train, data_test, labels_train, labels_test
+
+
+def main():
+    folder_path = 'data/cifar-10-batches-py'
+    data, labels = read_cifar(folder_path)
+
+    print((data.shape))
+    print((labels.shape))
+
+    data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
+
+    print("Training set shape:", data_train.shape, labels_train.shape)
+    print("Testing set shape:", data_test.shape, labels_test.shape)
+
+
+
 
 
+if __name__ == "__main__":
+    main()
 
-batch='data/cifar-10-batches-py/data_batch_1'
-data, labels = read_cifar_batch(batch)