def test_det_shape():
rng = np.random.RandomState(utt.fetch_seed())
r = rng.randn(5, 5).astype(config.floatX)
x = matrix()
f = aesara.function([x], det(x))
f_shape = aesara.function([x], det(x).shape)
assert np.all(f(r).shape == f_shape(r))
def test_det_shape():
rng = np.random.default_rng(utt.fetch_seed())
r = rng.standard_normal((5, 5)).astype(config.floatX)
x = matrix()
f = aesara.function([x], det(x))
f_shape = aesara.function([x], det(x).shape)
assert np.all(f(r).shape == f_shape(r))
def test_det():
rng = np.random.RandomState(utt.fetch_seed())
r = rng.randn(5, 5).astype(config.floatX)
x = matrix()
f = aesara.function([x], det(x))
assert np.allclose(np.linalg.det(r), f(r))
def test_det():
rng = np.random.default_rng(utt.fetch_seed())
r = rng.standard_normal((5, 5)).astype(config.floatX)
x = matrix()
f = aesara.function([x], det(x))
assert np.allclose(np.linalg.det(r), f(r))
def test_jax_basic():
rng = np.random.default_rng(28494)
x = matrix("x")
y = matrix("y")
b = vector("b")
# `ScalarOp`
z = cosh(x**2 + y / 3.0)
# `[Inc]Subtensor`
out = aet_subtensor.set_subtensor(z[0], -10.0)
out = aet_subtensor.inc_subtensor(out[0, 1], 2.0)
out = out[:5, :3]
out_fg = FunctionGraph([x, y], [out])
test_input_vals = [
np.tile(np.arange(10), (10, 1)).astype(config.floatX),
np.tile(np.arange(10, 20), (10, 1)).astype(config.floatX),
]
(jax_res, ) = compare_jax_and_py(out_fg, test_input_vals)
# Confirm that the `Subtensor` slice operations are correct
assert jax_res.shape == (5, 3)
# Confirm that the `IncSubtensor` operations are correct
assert jax_res[0, 0] == -10.0
assert jax_res[0, 1] == -8.0
out = clip(x, y, 5)
out_fg = FunctionGraph([x, y], [out])
compare_jax_and_py(out_fg, test_input_vals)
out = aet.diagonal(x, 0)
out_fg = FunctionGraph([x], [out])
compare_jax_and_py(
out_fg, [np.arange(10 * 10).reshape((10, 10)).astype(config.floatX)])
out = aet_slinalg.cholesky(x)
out_fg = FunctionGraph([x], [out])
compare_jax_and_py(
out_fg,
[(np.eye(10) + rng.standard_normal(size=(10, 10)) * 0.01).astype(
config.floatX)],
)
# not sure why this isn't working yet with lower=False
out = aet_slinalg.Cholesky(lower=False)(x)
out_fg = FunctionGraph([x], [out])
compare_jax_and_py(
out_fg,
[(np.eye(10) + rng.standard_normal(size=(10, 10)) * 0.01).astype(
config.floatX)],
)
out = aet_slinalg.solve(x, b)
out_fg = FunctionGraph([x, b], [out])
compare_jax_and_py(
out_fg,
[
np.eye(10).astype(config.floatX),
np.arange(10).astype(config.floatX),
],
)
out = aet.diag(b)
out_fg = FunctionGraph([b], [out])
compare_jax_and_py(out_fg, [np.arange(10).astype(config.floatX)])
out = aet_nlinalg.det(x)
out_fg = FunctionGraph([x], [out])
compare_jax_and_py(
out_fg, [np.arange(10 * 10).reshape((10, 10)).astype(config.floatX)])
out = aet_nlinalg.matrix_inverse(x)
out_fg = FunctionGraph([x], [out])
compare_jax_and_py(
out_fg,
[(np.eye(10) + rng.standard_normal(size=(10, 10)) * 0.01).astype(
config.floatX)],
)
def test_det_shape():
x = matrix()
det_shape = det(x).shape
assert isinstance(det_shape, Constant)
assert tuple(det_shape.data) == ()