4 examples of 'knn sklearn' in Python
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169 def predict_knn(X_train, X_test, y_train, y_test): 170 clf=knn(n_neighbors=3) 171 print("knn started") 172 clf.fit(X_train,y_train) 173 y_pred=clf.predict(X_test) 174 calc_accuracy("K nearest neighbours",y_test,y_pred) 175 np.savetxt('submission_surf_knn.csv', np.c_[range(1,len(y_test)+1),y_pred,y_test], delimiter=',', header = 'ImageId,Label,TrueLabel', comments = '', fmt='%d')
24 def trainKNN(trainData,trainLabel): 25 knn = neighbors.KNeighborsClassifier(n_neighbors=3,algorithm='ball_tree') 26 #print(type(trainData)) 27 return knn.fit(trainData,trainLabel)
35 def skl_knn(self, k): 36 """k: number of neighbors to use in classification 37 test_data: the data/targets used to test the classifier 38 stored_data: the data/targets used to classify the test_data 39 """ 40 fifty_x, fifty_y = self.mk_dataset(50000) 41 test_img = [self.data[i] for i in self.indx[60000:70000]] 42 test_img1 = np.array(test_img) 43 test_target = [self.target[i] for i in self.indx[60000:70000]] 44 test_target1 = np.array(test_target) 45 self.classifier.fit(fifty_x, fifty_y) 46 47 y_pred = self.classifier.predict(test_img1) 48 pickle.dump(self.classifier, open('knn.sav', 'wb')) 49 print(classification_report(test_target1, y_pred)) 50 print("KNN Classifier model saved as knn.sav!")
221 def __init__(self, X, Y): 222 ''' 223 :param X: 224 :param Y: 225 ''' 226 self.model = KNeighborsClassifier(n_neighbors=3) 227 self.X = X 228 self.Y = Y