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knn.py

+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)
+    
+      
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