5 examples of 'sklearn knn regressor' in Python

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24def trainKNN(trainData,trainLabel):
25 knn = neighbors.KNeighborsClassifier(n_neighbors=3,algorithm='ball_tree')
26 #print(type(trainData))
27 return knn.fit(trainData,trainLabel)
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169def 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')
221def __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
35def 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!")
67def predict(self, X=None):
68 """
69 Predicts the labels for the given data. Model needs to be already fitted.
70
71 X : 2D-Array or Matrix, default=None
72
73 The dataset to be used. If None, the training set will be used. In this case, the prediction will be made
74 using Leave One Out (that is, the sample to predict will be taken away from the training set).
75
76 Returns
77 -------
78
79 y : 1D-Array
80
81 The vector with the label predictions.
82 """
83 if X is None:
84 return self.loo_pred(self.trX)
85 else:
86 X = self.dml.transform(X)
87
88 return self.knn.predict(X)

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