2e448864f54e5d8040221a1bcfa3492a55cc8465,dipy/segment/tests/test_metric.py,,test_distance_matrix,#,218

Before Change

    assert_array_equal(D, D.T)
    assert_array_equal(np.diag(D), np.zeros(len(data1)))

    for i in range(1, len(data1)):
        for j in range(i+1, len(data1)):
            assert_equal(D[i, j], dipymetric.dist(metric, data1[i], data1[j]))


if __name__ == "__main__":
    run_module_suite()

After Change

def test_distance_matrix():
    metric = dipymetric.SumPointwiseEuclideanMetric()

    for dtype in [np.int32, np.int64, np.float32, np.float64]:
        // Compute distances of all tuples spawn by the Cartesian product
        // of `data` with itself.
        data = (np.random.rand(4, 10, 3)*10).astype(dtype)
        D = dipymetric.distance_matrix(metric, data)
        assert_equal(D.shape, (len(data), len(data)))
        assert_array_equal(np.diag(D), np.zeros(len(data)))

        if metric.is_order_invariant:
            // Distance matrix should be symmetric
            assert_array_equal(D, D.T)

        for i in range(len(data)):
            for j in range(len(data)):
                assert_equal(D[i, j], dipymetric.dist(metric, data[i], data[j]))

        // Compute distances of all tuples spawn by the Cartesian product
        // of `data` with `data2`.
        data2 = (np.random.rand(3, 10, 3)*10).astype(dtype)
        D = dipymetric.distance_matrix(metric, data, data2)
        assert_equal(D.shape, (len(data), len(data2)))

        for i in range(len(data)):
            for j in range(len(data2)):
                assert_equal(D[i, j], dipymetric.dist(metric, data[i], data2[j]))


if __name__ == "__main__":
    run_module_suite()

Instances