From a28545d911dce6afa556e235ee2a9796afc63305 Mon Sep 17 00:00:00 2001
From: Milan <milan.cart@ecl20.ec-lyon.fr>
Date: Thu, 9 Nov 2023 22:15:19 +0100
Subject: [PATCH] Part 2 : KNN
---
__pycache__/read_cifar.cpython-311.pyc | Bin 2090 -> 2710 bytes
knn.py | 80 +++++++++++++++++++++++++
2 files changed, 80 insertions(+)
diff --git a/__pycache__/read_cifar.cpython-311.pyc b/__pycache__/read_cifar.cpython-311.pyc
index 3c4534351e5e03b53d76e9087a1ffc193f08cd06..3d053de8f665020f45e54022379f58efabed47fd 100644
GIT binary patch
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diff --git a/knn.py b/knn.py
index e69de29..0a687fb 100644
--- a/knn.py
+++ b/knn.py
@@ -0,0 +1,80 @@
+import read_cifar
+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
+
+ dot_product = np.dot(matrix1, matrix2.T) # A * B (matrix mutliplication)
+
+ dists = np.sqrt(sum_of_squares_matrix1 + sum_of_squares_matrix2.T - 2 * dot_product) # Compute the 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
+ 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() :
+
+ 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')
+ plt.plot(k_indices, accuracies)
+ plt.title("Accuracy as function of k")
+ plt.legend()
+ plt.show()
+ plt.savefig('/Users/milancart/Documents/GitHub/image-classification/result/knn.png')
+
+
+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
--
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