avancement

kkn.py

@@ -29,14 +29,14 @@ def affichage(d_train, l_train, d_test, l_test):
     liste_names= np.array(batch_data[b'label_names'])
     fig, axes = plt.subplots(large, long, figsize=(12, 5))
     fig.subplots_adjust(hspace=0.5)
-    for i in range(len(l_train)):
+    for i,_ in enumerate(l_train):
         im = np.array(np.reshape(d_train[i, 0:3072], (32, 32, 3), order='F'), dtype=np.int64)
         im = np.transpose(im, (1, 0, 2))
         name=liste_names[l_train[i]]
         axes[i // long, i % long].imshow(im)
         axes[i // long, i % long].set_title(f"Train : {name.decode('utf-8')}")
         axes[i // long, i % long].axis('off')
-    for i in range(len(l_test)):
+    for i,_ in enumerate(l_test):
         im = np.array(np.reshape(d_test[i, 0:3072], (32, 32, 3), order='F'), dtype=np.int64)
         im = np.transpose(im, (1, 0, 2))
         j = i + len(l_train)

read_cifar.py

+"""import numpy"""
 import numpy as np
 import pickle
 import os
 
 def read_cifar_batch(batch_path):
+    """F"""
     with open(batch_path, 'rb') as file:
         batch_data = pickle.load(file, encoding='bytes')
     data = np.array(batch_data[b'data'], dtype=np.float32)
     labels = np.array(batch_data[b'labels'], dtype=np.int64)
-
     return data, labels
 
 def read_cifar(path_folder):
@@ -22,6 +23,7 @@ def read_cifar(path_folder):
     return(data,labels)
 
 def split_dataset(data, labels, split_factor):
+    """fonction"""
     num_samples = len(data)
     shuffled_indices = np.random.permutation(num_samples)
     split_index = int(num_samples * split_factor)

requirements.txt

+numpy
+open-cv
\ No newline at end of file

result/desktop.ini

+[.ShellClassInfo]
+IconResource=C:\Program Files\Google\Drive File Stream\82.0.1.0\GoogleDriveFS.exe,22

test.py

+import numpy as np
+
+def count_matching_elements(vector1, vector2):
+    if len(vector1) != len(vector2):
+        raise ValueError("Les vecteurs doivent avoir la même taille.")
+
+    matching_elements = np.sum(vector1 == vector2)
+    return matching_elements
+
+# Exemple d'utilisation
+vector1 = np.array([1, 2, 3, 4, 5])
+vector2 = np.array([1, 0, 3, 3, 5])