From 6c0a1bf6cd1f344f0fcc91b89b5b4b01a01654b7 Mon Sep 17 00:00:00 2001
From: lucile <lucile.audard@ecl20.ec-lyon.fr>
Date: Fri, 10 Nov 2023 16:05:04 +0100
Subject: [PATCH] Update knn.py
---
knn.py | 30 ++++++++++--------------------
1 file changed, 10 insertions(+), 20 deletions(-)
diff --git a/knn.py b/knn.py
index 2edab7c..a7decbb 100644
--- a/knn.py
+++ b/knn.py
@@ -43,35 +43,25 @@ def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
return accuracy
-
-
-
-
-
-
if __name__ == "__main__":
-
+
+ # Extraction of the data from Cifar database
data, labels = read_cifar("./data/cifar-10-batches-py")
- data_train, labels_train, data_test, labels_test = split_dataset(data, labels, 0.9)
+ # Formatting the data into training and testing sets
+ data_train, labels_train, data_test, labels_test = split_dataset(data, labels, 0.1)
+
+ # Data to plot
k_list = [k for k in range(1, 21)]
accuracy = [evaluate_knn(data_train, labels_train, data_test, labels_test, k) for k in range (1, 21)]
+ # Plot the graph
+ plt.close()
plt.plot(k_list, accuracy)
plt.title("Variation of k-nearest neighbors method accuracy for k from 1 to 20")
plt.xlabel("k value")
plt.ylabel("Accuracy")
plt.grid(True, which='both')
- plt.savefig("results/knn.png")
-
+ plt.show()
+ #plt.savefig("results/knn.png")
- # x_test = np.array([[1,2],[4,6]])
- # x_labels_test = np.array([0,1])
- # x_train = np.array([[2,4],[7,2],[4,6]])
- # x_labels_train = np.array([0,1,1])
-
- # dist = distance_matrix(x_test, x_train)
- # accuracy = evaluate_knn(x_train, x_labels_train, x_test, x_labels_test, 1)
- # print(accuracy)
-
-
--
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