test_pairwise.py 文件源码

def test_euclidean_distances():
    # Check the pairwise Euclidean distances computation
    X = [[0]]
    Y = [[1], [2]]
    D = euclidean_distances(X, Y)
    assert_array_almost_equal(D, [[1., 2.]])

    X = csr_matrix(X)
    Y = csr_matrix(Y)
    D = euclidean_distances(X, Y)
    assert_array_almost_equal(D, [[1., 2.]])

    rng = np.random.RandomState(0)
    X = rng.random_sample((10, 4))
    Y = rng.random_sample((20, 4))
    X_norm_sq = (X ** 2).sum(axis=1).reshape(1, -1)
    Y_norm_sq = (Y ** 2).sum(axis=1).reshape(1, -1)

    # check that we still get the right answers with {X,Y}_norm_squared
    D1 = euclidean_distances(X, Y)
    D2 = euclidean_distances(X, Y, X_norm_squared=X_norm_sq)
    D3 = euclidean_distances(X, Y, Y_norm_squared=Y_norm_sq)
    D4 = euclidean_distances(X, Y, X_norm_squared=X_norm_sq,
                             Y_norm_squared=Y_norm_sq)
    assert_array_almost_equal(D2, D1)
    assert_array_almost_equal(D3, D1)
    assert_array_almost_equal(D4, D1)

    # check we get the wrong answer with wrong {X,Y}_norm_squared
    X_norm_sq *= 0.5
    Y_norm_sq *= 0.5
    wrong_D = euclidean_distances(X, Y,
                                  X_norm_squared=np.zeros_like(X_norm_sq),
                                  Y_norm_squared=np.zeros_like(Y_norm_sq))
    assert_greater(np.max(np.abs(wrong_D - D1)), .01)


# Paired distances