From 5aabffaade4db6f75005aaca53ff0f06138c1c2c Mon Sep 17 00:00:00 2001
From: Milan <milan.cart@ecl20.ec-lyon.fr>
Date: Thu, 9 Nov 2023 23:34:03 +0100
Subject: [PATCH] Modif
---
knn.py | 46 +++++++++++++---------------------------------
mlp.py | 0
result/knn.png | Bin 0 -> 2396 bytes
3 files changed, 13 insertions(+), 33 deletions(-)
create mode 100644 mlp.py
create mode 100644 result/knn.png
diff --git a/knn.py b/knn.py
index 0a687fb..3ea3ce9 100644
--- a/knn.py
+++ b/knn.py
@@ -3,58 +3,47 @@ import numpy as np
import matplotlib.pyplot as plt
def distance_matrix(matrix1, matrix2):
- #X_test then X_train in this order
- sum_of_squares_matrix1 = np.sum(np.square(matrix1), axis=1, keepdims=True) #A^2
- sum_of_squares_matrix2 = np.sum(np.square(matrix2), axis=1, keepdims=True) #B^2
+ sum_of_squares_matrix1 = np.sum(np.square(matrix1), axis=1, keepdims=True)
+ sum_of_squares_matrix2 = np.sum(np.square(matrix2), axis=1, keepdims=True)
- dot_product = np.dot(matrix1, matrix2.T) # A * B (matrix mutliplication)
+ dot_product = np.dot(matrix1, matrix2.T)
- dists = np.sqrt(sum_of_squares_matrix1 + sum_of_squares_matrix2.T - 2 * dot_product) # Compute the product
+ dists = np.sqrt(sum_of_squares_matrix1 + sum_of_squares_matrix2.T - 2 * dot_product)
return dists
def knn_predict(dists, labels_train, k):
output = []
- # Loop on all the images_test
+
for i in range(len(dists)):
- # Innitialize table to store the neighbors
+
res = [0] * 10
- # Get the closest neighbors
labels_close = np.argsort(dists[i])[:k]
for label in labels_close:
- #add a label to the table of result
res[labels_train[label]] += 1
- # Get the class with the maximum neighbors
- label_temp = np.argmax(res) #Careful to the logic here, if there is two or more maximum, the function the first maximum encountered
+ label_temp = np.argmax(res)
output.append(label_temp)
return(np.array(output))
def evaluate_knn(data_train, labels_train, data_test, labels_tests, k):
dist = distance_matrix(data_test, data_train)
result_test = knn_predict(dist, labels_train, k)
-
- #accuracy
+
N = labels_tests.shape[0]
accuracy = (labels_tests == result_test).sum() / N
return(accuracy)
-def bench_knn() :
+def test_knn() :
k_indices = [i for i in range(20) if i % 2 != 0]
accuracies = []
- # Load data
data, labels = read_cifar.read_cifar('/Users/milancart/Documents/GitHub/image-classification/Data/cifar-10-batches-py')
X_train, X_test, y_train, y_test = read_cifar.split_dataset(data, labels, 0.9)
- #Load one batch
- # data, labels = read_cifar.read_cifar_batch('image-classification/data/cifar-10-batches-py/data_batch_1')
- # X_train, X_test, y_train, y_test = read_cifar.split_dataset(data, labels, 0.9)
-
- # Loop on the k_indices to get all the accuracies
+
for k in k_indices :
accuracy = evaluate_knn(X_train, y_train, X_test, y_test, k)
accuracies.append(accuracy)
- # Save and show the graph of accuracies
plt.figure(figsize=(8, 6))
plt.xlabel('K')
plt.ylabel('Accuracy')
@@ -66,15 +55,6 @@ def bench_knn() :
if __name__ == "__main__":
- print('milan')
- bench_knn()
- data, labels = read_cifar.read_cifar('/Users/milancart/Documents/GitHub/image-classification/Data/cifar-10-batches-py')
- X_train, X_test, y_train, y_test = read_cifar.split_dataset(data, labels, 0.9)
- print(evaluate_knn(X_train, y_train, X_test, y_test, 5))
- print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
-
- y_test = []
- x_test = np.array([[1,2],[4,6]])
- x_train = np.array([[2,4],[7,2],[4,6]])
- y_train = [1,2,1]
- dist = distance_matrix(x_test,x_train)
\ No newline at end of file
+
+ test_knn()
+
\ No newline at end of file
diff --git a/mlp.py b/mlp.py
new file mode 100644
index 0000000..e69de29
diff --git a/result/knn.png b/result/knn.png
new file mode 100644
index 0000000000000000000000000000000000000000..72354204692aa86cfb0bce31acc22fdbb378fc2f
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