Partie knn finie

knn.py

 import numpy as np
+from read_cifar import read_cifar, split_dataset
+import matplotlib.pyplot as plt
 
 def distance_matrix(matrix_a: np.ndarray, matrix_b: np.ndarray):
     sum_squares_1 = np.sum(matrix_a**2, axis = 1, keepdims = True)
@@ -10,11 +12,43 @@ def distance_matrix(matrix_a: np.ndarray, matrix_b: np.ndarray):
     return dists
 
 def knn_predict(dists: np.ndarray, labels_train: np.ndarray, k:int):
-    return 0
+    labels_predicts = np.zeros(np.size(dist, 0))
+    for i in range(np.size(labels_predicts, 0)):
+        #On extrait les indices des k valeurs plus petites (des k plus proches voisins)
+        k_neighbors_index = np.argmin(dists[i, :], np.sort(dists[i, :])[:k])
+        #On compte la classe la plus présente parmi les k voisins les plus proches
+        labels_k_neighbors = labels_train[k_neighbors_index]
+        #On compte le nombre d'occurence des classes parmis les k
+        _, count = np.unique(labels_k_neighbors, return_counts=True)
+        #On associe à la prédiction la classe la plus presente parmis les k
+        labels_predicts[i] = labels_k_neighbors[np.argmax(count)]
+    return labels_predicts
+
+def evaluate_knn(data_train:np.ndarray, labels_train: np.ndarray, data_test:np.ndarray, labels_test:np.ndarray, k:int):
+    dists = distance_matrix(data_test, data_train)
+    labels_predicts = knn_predict(dists, labels_train, k)
+    #calcul de l'accuracy
+    accuracy = 0
+    for i in range(np.size(labels_predicts, 0)):
+        if abs(labels_predicts[i]-labels_test[i])<10**(-7):
+            accuracy += 1
+    accuracy /= np.size(labels_predicts, 0)
+    return accuracy
+
+def plot_knn(data_train:np.ndarray, labels_train: np.ndarray, data_test:np.ndarray, labels_test:np.ndarray, n: int):
+    accuracy_vector = np.zeros(n)
+    for k in range(1, n+1):
+        accuracy_vector[k] = evaluate_knn(data_train, labels_train, data_test, labels_test)
+    plt.plot(accuracy_vector)
+    plt.show()
+    return
+
+
 
 
 
 if __name__ == "__main__":
-    A = np.ones((3,3))
-    B = np.ones((3,3))*2
-    dist = distance_matrix(A, B)
+    data, labels = read_cifar()
+    data_train, labels_train, data_test, labels_test = split_dataset(data, labels, 0.8)
+    k = 5 #Nombre de voisins
\ No newline at end of file
+    accuracy = evaluate_knn(data_train, labels_train, data_test, labels_test, k)
\ No newline at end of file

read_cifar.py

@@ -31,9 +31,9 @@ def read_cifar():
         dict = pickle.load(fo, encoding='bytes')
     data.append(dict[b'data'])
     labels.append(dict[b'labels'])
-    data = np.array(data, np.float32)
+    data = np.array(data, np.float16)
     labels = np.array(labels, np.int64)
-    return np.reshape(data, (np.size(data, 0)*np.size(data, 1), np.size(data, 2))), np.reshape(labels, (np.size(labels, 0)*np.size(labels, 1), 1))
+    return np.reshape(data, (np.size(data, 0)*np.size(data, 1), np.size(data, 2))), np.reshape(labels, (np.size(labels, 0)*np.size(labels, 1)))
 
 
 def split_dataset(data: np.ndarray, labels: np.ndarray, split: float):
@@ -50,5 +50,5 @@ def split_dataset(data: np.ndarray, labels: np.ndarray, split: float):
 
 if __name__ == "__main__":
     data, labels = read_cifar()
-    a, b, c, d = split_dataset(data, labels, 0.8)
+    data_train, labels_train, data_test, labels_test = split_dataset(data, labels, 0.8)
     print(1)