def testL2DistanceSq(self):
n1 = numpy.array([[1., 2., 3., 4.], [1., 1., 1., 1.]], dtype=numpy.float32)
n2 = numpy.array([[5., 6., 7., -8.], [1., 1., 1., 1.]], dtype=numpy.float32)
out = self.Run(functions.l2_distance_sq(n1, n2))
testing.assert_allclose(
out[0],
numpy.power(
numpy.array(
[euclidean(n1[0], n2[0]), euclidean(
n1[1], n2[1])]), 2),
rtol=TOLERANCE)
def test_l2_distance_sq(self):
n1 = numpy.array([[1., 2., 3., 4.], [1., 1., 1., 1.]], dtype=numpy.float32)
n2 = numpy.array([[5., 6., 7., -8.], [1., 1., 1., 1.]], dtype=numpy.float32)
out = self.eval_tensor(functions.l2_distance_sq(n1, n2))
testing.assert_allclose(
out[0],
numpy.power(
numpy.array(
[euclidean(n1[0], n2[0]), euclidean(
n1[1], n2[1])]), 2),
rtol=TOLERANCE, atol=TOLERANCE)
def testL2DistanceSqWithBroadcast(self):
n1 = numpy.array([[[1., 2., 3., 4.], [1., 1., 1., 1.]], [[5., 6., 7., 8.],
[1., 1., 1., 2.]]],
dtype=numpy.float32)
n2 = numpy.array([[5., 6., 7., -8.], [1., 1., 1., 1.]], dtype=numpy.float32)
out = self.Run(functions.l2_distance_sq(n1, n2))
expected = numpy.array(
[[euclidean(n1[0, 0], n2[0]), euclidean(
n1[0, 1], n2[1])], [euclidean(n1[1, 0], n2[0]),
euclidean(n1[1, 1], n2[1])]])
expected = numpy.power(expected, 2)
testing.assert_allclose(expected, out[0], atol=TOLERANCE)
def test_l2_distance_sq_with_broadcast(self):
n1 = numpy.array([[[1., 2., 3., 4.], [1., 1., 1., 1.]], [[5., 6., 7., 8.],
[1., 1., 1., 2.]]],
dtype=numpy.float32)
n2 = numpy.array([[5., 6., 7., -8.], [1., 1., 1., 1.]], dtype=numpy.float32)
out = self.eval_tensor(functions.l2_distance_sq(n1, n2))
expected = numpy.array(
[[euclidean(n1[0, 0], n2[0]), euclidean(
n1[0, 1], n2[1])], [euclidean(n1[1, 0], n2[0]),
euclidean(n1[1, 1], n2[1])]])
expected = numpy.power(expected, 2)
testing.assert_allclose(expected, out[0], atol=TOLERANCE)