From cfaf9737cbb14ee2cad40813abcdf529ce3895ca Mon Sep 17 00:00:00 2001
From: Malo Bourry <malo.bourry@ecl20.ec-lyon.fr>
Date: Fri, 20 Oct 2023 22:09:51 +0200
Subject: [PATCH] Partie knn finie
---
__pycache__/read_cifar.cpython-38.pyc | Bin 0 -> 1871 bytes
knn.py | 42 +++++++++++++++++++++++---
read_cifar.py | 6 ++--
3 files changed, 41 insertions(+), 7 deletions(-)
create mode 100644 __pycache__/read_cifar.cpython-38.pyc
diff --git a/__pycache__/read_cifar.cpython-38.pyc b/__pycache__/read_cifar.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d01a55faa65f9a5c3e47242daef27b820c55d340
GIT binary patch
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diff --git a/knn.py b/knn.py
index e499927..b39e134 100644
--- a/knn.py
+++ b/knn.py
@@ -1,4 +1,6 @@
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)
\ No newline at end of file
+ data, labels = read_cifar()
+ data_train, labels_train, data_test, labels_test = split_dataset(data, labels, 0.8)
+ k = 5 #Nombre de voisins
+ accuracy = evaluate_knn(data_train, labels_train, data_test, labels_test, k)
\ No newline at end of file
diff --git a/read_cifar.py b/read_cifar.py
index 5d1e780..df7393f 100644
--- a/read_cifar.py
+++ b/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)
--
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