def test_skip(self):
dim = 10
n = 50
skip = 17
for dtype in [np.float64, np.float32]:
sample_noskip = math_ops.sobol_sample(dim, n + skip, dtype=dtype)
sample_skip = math_ops.sobol_sample(dim, n, skip, dtype=dtype)
self.assertAllClose(
self.evaluate(sample_noskip)[skip:, :], self.evaluate(sample_skip))
def test_more_known_values(self):
for dtype in [np.float64, np.float32]:
sample = math_ops.sobol_sample(5, 31, dtype=dtype)
expected = [[0.50, 0.50, 0.50, 0.50, 0.50],
[0.75, 0.25, 0.25, 0.25, 0.75],
[0.25, 0.75, 0.75, 0.75, 0.25],
[0.375, 0.375, 0.625, 0.875, 0.375],
[0.875, 0.875, 0.125, 0.375, 0.875],
[0.625, 0.125, 0.875, 0.625, 0.625],
[0.125, 0.625, 0.375, 0.125, 0.125],
[0.1875, 0.3125, 0.9375, 0.4375, 0.5625],
[0.6875, 0.8125, 0.4375, 0.9375, 0.0625],
[0.9375, 0.0625, 0.6875, 0.1875, 0.3125],
[0.4375, 0.5625, 0.1875, 0.6875, 0.8125],
[0.3125, 0.1875, 0.3125, 0.5625, 0.9375],
[0.8125, 0.6875, 0.8125, 0.0625, 0.4375],
[0.5625, 0.4375, 0.0625, 0.8125, 0.1875],
[0.0625, 0.9375, 0.5625, 0.3125, 0.6875],
[0.09375, 0.46875, 0.46875, 0.65625, 0.28125],
[0.59375, 0.96875, 0.96875, 0.15625, 0.78125],
[0.84375, 0.21875, 0.21875, 0.90625, 0.53125],
[0.34375, 0.71875, 0.71875, 0.40625, 0.03125],
[0.46875, 0.09375, 0.84375, 0.28125, 0.15625],
[0.96875, 0.59375, 0.34375, 0.78125, 0.65625],
[0.71875, 0.34375, 0.59375, 0.03125, 0.90625],
[0.21875, 0.84375, 0.09375, 0.53125, 0.40625],
[0.15625, 0.15625, 0.53125, 0.84375, 0.84375],
[0.65625, 0.65625, 0.03125, 0.34375, 0.34375],
[0.90625, 0.40625, 0.78125, 0.59375, 0.09375],
[0.40625, 0.90625, 0.28125, 0.09375, 0.59375],
[0.28125, 0.28125, 0.15625, 0.21875, 0.71875],
[0.78125, 0.78125, 0.65625, 0.71875, 0.21875],
[0.53125, 0.03125, 0.40625, 0.46875, 0.46875],
[0.03125, 0.53125, 0.90625, 0.96875, 0.96875]]
self.assertAllClose(expected, self.evaluate(sample), .001)
def f(num_results): s = math_ops.sobol_sample(10, num_results, dtype=dtypes.float32) assert s.shape.as_list() == [None, 10] return s
def f(dim): s = math_ops.sobol_sample(dim, 100, dtype=dtypes.float32) assert s.shape.as_list() == [100, None] return s
def test_static_shape(self): s = math_ops.sobol_sample(10, 100, dtype=np.float32) self.assertAllEqual([100, 10], s.shape.as_list())
def test_basic(self):
for dtype in [np.float64, np.float32]:
expected = np.array([[.5, .5], [.75, .25], [.25, .75], [.375, .375]])
sample = self.evaluate(math_ops.sobol_sample(2, 4, dtype=dtype))
self.assertAllClose(expected, sample, 0.001)
def test_default_dtype(self): # Create an op without specifying the dtype. Dtype should be float32 in # this case. s = math_ops.sobol_sample(10, 100) self.assertEqual(dtypes.float32, s.dtype)
def test_dynamic_shape(self): s = math_ops.sobol_sample(10, 100, dtype=dtypes.float32) self.assertAllEqual([100, 10], self.evaluate(s).shape)
