test_neighbors.py 文件源码

def test_neighbors_digits():
    # Sanity check on the digits dataset
    # the 'brute' algorithm has been observed to fail if the input
    # dtype is uint8 due to overflow in distance calculations.

    X = digits.data.astype('uint8')
    Y = digits.target
    (n_samples, n_features) = X.shape
    train_test_boundary = int(n_samples * 0.8)
    train = np.arange(0, train_test_boundary)
    test = np.arange(train_test_boundary, n_samples)
    (X_train, Y_train, X_test, Y_test) = X[train], Y[train], X[test], Y[test]

    clf = neighbors.KNeighborsClassifier(n_neighbors=1, algorithm='brute')
    score_uint8 = clf.fit(X_train, Y_train).score(X_test, Y_test)
    score_float = clf.fit(X_train.astype(float), Y_train).score(
        X_test.astype(float), Y_test)
    assert_equal(score_uint8, score_float)