From a34e29fbe80bcf413be18bf93ec76d8af1d4afe4 Mon Sep 17 00:00:00 2001
From: Delorme Antonin <antonin.delorme@etu.ec-lyon.fr>
Date: Fri, 10 Nov 2023 19:04:12 +0000
Subject: [PATCH] Upload New File
---
knn.py | 77 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 77 insertions(+)
create mode 100644 knn.py
diff --git a/knn.py b/knn.py
new file mode 100644
index 0000000..7748c63
--- /dev/null
+++ b/knn.py
@@ -0,0 +1,77 @@
+import pickle
+import numpy as np
+import read_cifar as rd
+from math import *
+import matplotlib.pyplot as plt
+
+def distance_matrix(a,b):
+ at=np.transpose(a)
+ bt=np.transpose(b)
+ som_carr_a=np.sum(np.square(a), axis=1, keepdims=True)
+ som_carr_b=np.sum(np.square(b), axis=1, keepdims=True)
+
+ prod=np.dot(a,bt)
+
+ return ( np.sqrt(som_carr_a + np.transpose(som_carr_b) - 2 * prod) )
+
+
+
+
+def knn_predict(dists,labels_train,k):
+ predict=[]
+ for i in range(len(dists)):
+ Glob_dist=[]
+ Glob_min=[]
+ for j in range(len(dists[i])):
+ Glob_dist.append(dists[i][j])
+ for p in range(k):
+ m = min(Glob_dist)
+ index = Glob_dist.index(m)
+ Glob_min.append(labels_train[index])
+ del(Glob_dist[index])
+
+ Temp=np.bincount(Glob_min)
+ predict.append(list(Temp).index(max(Temp)))
+
+
+ return predict
+
+
+def evaluate_knn(data_train,data_test,labels_train,labels_test,k,dist):
+ pred=knn_predict(dist, labels_train, k)
+ tot=0
+ bon=0
+ for i in range(len(labels_test)):
+ if labels_test[i]==pred[i]:
+ bon+=1
+ tot+=1
+
+ print("Accuracy :",bon/tot)
+ return bon/tot
+
+
+if __name__ == "__main__":
+ #a=np.random.random((2,2))
+ #b=np.random.random((2,2))
+ #c=np.eye(2)
+
+ path="data\\cifar-10-batches-py"
+ labels,data=rd.read_cifar(path)
+ (data_train,labels_train,data_test,labels_test)=rd.split_dataset(labels,data,0.1)
+
+ data_train,data_test=data_train.astype(np.float32),data_test.astype(np.float32)
+ labels_train,labels_test=np.array(labels_train, dtype=np.int16),np.array(labels_test, dtype=np.int16)
+ print("Taille train : ",len(data_train))
+ print("Taille test :",len(data_test))
+ dist=distance_matrix(data_test,data_train)
+ Res=[]
+ for k in range(1,21):
+ print(k,":")
+ Res.append(evaluate_knn(data_train, data_test, labels_train, labels_test, k,dist))
+
+
+ plt.figure()
+ plt.title("Accurracy with different k")
+ plt.plot([i for i in range (1,21)],Res)
+
+
\ No newline at end of file
--
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