From be1fe1ceaba1cff8f9c4d101f86b45cabd8eec24 Mon Sep 17 00:00:00 2001
From: Aya SAIDI <aya.saidi@auditeur.ec-lyon.fr>
Date: Sat, 22 Oct 2022 12:11:29 +0100
Subject: [PATCH] Update knn.py
---
knn.py | 27 +++++++++++++++++++++++++--
1 file changed, 25 insertions(+), 2 deletions(-)
diff --git a/knn.py b/knn.py
index a322bea..f0c66e8 100644
--- a/knn.py
+++ b/knn.py
@@ -1,8 +1,31 @@
+#Libraries
+import numpy as np
+import torch
+#Functions
def distance_matrix(Y , X):
#This function takes as parameters two matrices X and Y
dists = np.sqrt(np.sum(-2 * np.multiply(X, Y)+ np.multiply(Y, Y) + np.multiply(X, X)))
#dists is the euclidian distance between two matrices
return dists
-def knn_predict(dists, labels_train,k):
-
+def knn_predict(dists, labels_train, k):
+ #This function takes as parameters: dists (from above), labels_train, and k the number of neighbors
+ labels_test_pred=torch.zeros(len(data_test), dtype=torch.int64)
+ for i in range(dists.shape[1]):
+ # Find index of k lowest values
+ x = torch.topk(dists[:,i], k, largest=False).indices
+
+ # Index the labels according to x
+ k_lowest_labels = labels_train[x]
+
+ # y_test_pred[i] = the most frequent occuring index
+ labels_test_pred[i] = torch.argmax(torch.bincount(k_lowest_labels))
+
+ return labels_test_pred
+
+def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
+ labels_test_pred=knn_predict(distance_matrix(data_train, data_test), labels_train, k)
+ num_samples= data_test.shape[0]
+ num_correct= (labels_test == labels_test_pred).sum().item()
+ accuracy= 100 * num_correct / num_samples
+ return accuracy
--
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