5 examples of 'sklearn knn regressor' in Python

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def trainKNN(trainData,trainLabel):
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    knn = neighbors.KNeighborsClassifier(n_neighbors=3,algorithm='ball_tree') 
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    #print(type(trainData))
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    return knn.fit(trainData,trainLabel)

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def predict_knn(X_train, X_test, y_train, y_test):
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    clf=knn(n_neighbors=3)
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    print("knn started")
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    clf.fit(X_train,y_train)
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    y_pred=clf.predict(X_test)
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    calc_accuracy("K nearest neighbours",y_test,y_pred)
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    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')

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def __init__(self, X, Y):
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    '''
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    :param X:
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    :param Y:
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    '''
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    self.model = KNeighborsClassifier(n_neighbors=3)
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    self.X = X
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    self.Y = Y

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def skl_knn(self, k):
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    """k: number of neighbors to use in classification
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    test_data: the data/targets used to test the classifier
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    stored_data: the data/targets used to classify the test_data
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    """
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    fifty_x, fifty_y = self.mk_dataset(50000)
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    test_img = [self.data[i] for i in self.indx[60000:70000]]
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    test_img1 = np.array(test_img)
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    test_target = [self.target[i] for i in self.indx[60000:70000]]
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    test_target1 = np.array(test_target)
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    self.classifier.fit(fifty_x, fifty_y)
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    y_pred = self.classifier.predict(test_img1)
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    pickle.dump(self.classifier, open('knn.sav', 'wb'))
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    print(classification_report(test_target1, y_pred))
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    print("KNN Classifier model saved as knn.sav!")

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def predict(self, X=None):
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    """
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    Predicts the labels for the given data. Model needs to be already fitted.
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    X : 2D-Array or Matrix, default=None
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        The dataset to be used. If None, the training set will be used. In this case, the prediction will be made
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        using Leave One Out (that is, the sample to predict will be taken away from the training set).
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    Returns
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    -------
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    y : 1D-Array
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        The vector with the label predictions.
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    """
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    if X is None:
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        return self.loo_pred(self.trX)
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    else:
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        X = self.dml.transform(X)
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    return self.knn.predict(X)