From 8cb839500de9cc2e513e6fbc58fc624248d43154 Mon Sep 17 00:00:00 2001
From: BaptisteBrd <75663738+BaptisteBrd@users.noreply.github.com>
Date: Fri, 10 Nov 2023 23:18:05 +0100
Subject: [PATCH] modif knn
---
knn.py | 25 ++++++++++---------------
1 file changed, 10 insertions(+), 15 deletions(-)
diff --git a/knn.py b/knn.py
index 9e44028..365c081 100644
--- a/knn.py
+++ b/knn.py
@@ -3,26 +3,21 @@ import read_cifar
import matplotlib.pyplot as plt
def distance_matrix(A,B):
-
+ # Calculating the squared sum of elements in each row for matrices A and B
sum_of_squares_A = np.sum(A**2, axis=1,keepdims=True)
sum_of_squares_B = np.sum(B**2, axis=1,keepdims=True).T
dot_product = np.dot(A, B.T)
-
+ # Computing the Euclidean distance matrix
dists=np.sqrt(sum_of_squares_A+sum_of_squares_B-2*dot_product)
return dists
-
-
-#def knn_predict(dists, labels_train, k):
- #
- #
def knn_predict(dists, labels_train, k):
predicted_labels = []
- # For every image in the test set
+ # Iterating through each test data point's distances to train data
for i in range(len(dists)):
- # Initialize an array to store the neighbors
+ # Counting the frequency of each class among the k nearest neighbors
classes = [0] * 10
# indexes of the closest neighbors
indexes_closest_nb = np.argsort(dists[i])[:k]
@@ -37,8 +32,7 @@ def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
rate = 0
dist_train_test = distance_matrix(data_test, data_train)
prediction = knn_predict(dist_train_test, labels_train, k)
- print(len(prediction))
- print(len(labels_test))
+ # Comparing predictions to actual test labels to calculate accuracy
for j in range(len(prediction)):
if prediction[j]==labels_test[j]:
rate +=1
@@ -51,12 +45,16 @@ def knn_final():
data,labels = read_cifar.read_cifar("data/cifar-10-batches-py")
data_train_f, labels_train_f, data_test_f, labels_test_f = read_cifar.split_dataset(data, labels, 0.9)
+
+ # Testing KNN for different values of k and storing the accuracy
for k in range_k :
print(k)
rate_k = evaluate_knn(data_train_f, labels_train_f, data_test_f, labels_test_f, k)
rates.append(rate_k)
+ # Plotting the accuracy as a function of k
+
plt.figure(figsize=(10, 7))
plt.xlabel('k')
plt.ylabel('Accuracy rate')
@@ -72,7 +70,4 @@ def knn_final():
if __name__ == "__main__" :
- knn_final()
- #a1 = np.array([[0,0,1],[0,0,0],[1,1,2]])
- #b1 = np.array([[1,3,1], [1,1,4], [1,5,1]])
- #print(distance_matrix(a1,b1))
\ No newline at end of file
+ knn_final()
\ No newline at end of file
--
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