def test_RandomVariable_floatX():
test_rv_op = RandomVariable(
"normal",
0,
[0, 0],
"floatX",
inplace=True,
)
assert test_rv_op.dtype == "floatX"
assert test_rv_op(0, 1).dtype == config.floatX
new_floatX = "float64" if config.floatX == "float32" else "float32"
with config.change_flags(floatX=new_floatX):
assert test_rv_op(0, 1).dtype == new_floatX
def test_broadcast_params():
ndims_params = [0, 0]
mean = np.array([0, 1, 2])
cov = np.array(1e-6)
params = [mean, cov]
res = broadcast_params(params, ndims_params)
assert np.array_equal(res[0], mean)
assert np.array_equal(res[1], np.broadcast_to(cov, (3, )))
ndims_params = [1, 2]
mean = np.r_[1, 2, 3]
cov = np.stack([np.eye(3) * 1e-5, np.eye(3) * 1e-4])
params = [mean, cov]
res = broadcast_params(params, ndims_params)
assert np.array_equal(res[0], np.broadcast_to(mean, (2, 3)))
assert np.array_equal(res[1], cov)
mean = np.stack([np.r_[0, 0, 0], np.r_[1, 1, 1]])
cov = np.arange(3 * 3).reshape((3, 3))
params = [mean, cov]
res = broadcast_params(params, ndims_params)
assert np.array_equal(res[0], mean)
assert np.array_equal(res[1], np.broadcast_to(cov, (2, 3, 3)))
mean = np.stack([np.r_[0, 0, 0], np.r_[1, 1, 1]])
cov = np.stack([
np.arange(3 * 3).reshape((3, 3)),
np.arange(3 * 3).reshape((3, 3)) * 10
])
params = [mean, cov]
res = broadcast_params(params, ndims_params)
assert np.array_equal(res[0], mean)
assert np.array_equal(res[1], cov)
mean = np.array([[1, 2, 3]])
cov = np.stack([np.eye(3) * 1e-5, np.eye(3) * 1e-4])
params = [mean, cov]
res = broadcast_params(params, ndims_params)
assert np.array_equal(res[0], np.array([[1, 2, 3], [1, 2, 3]]))
assert np.array_equal(res[1], cov)
mean = np.array([[0], [10], [100]])
cov = np.diag(np.array([1e-6]))
params = [mean, cov]
res = broadcast_params(params, ndims_params)
assert np.array_equal(res[0], mean)
assert np.array_equal(res[1], np.broadcast_to(cov, (3, 1, 1)))
# Try it in Aesara
with config.change_flags(compute_test_value="raise"):
mean = tensor(config.floatX, [False, True])
mean.tag.test_value = np.array([[0], [10], [100]], dtype=config.floatX)
cov = matrix()
cov.tag.test_value = np.diag(np.array([1e-6], dtype=config.floatX))
params = [mean, cov]
res = broadcast_params(params, ndims_params)
assert np.array_equal(res[0].get_test_value(), mean.get_test_value())
assert np.array_equal(res[1].get_test_value(),
np.broadcast_to(cov.get_test_value(), (3, 1, 1)))
def set_aesara_flags():
opts = OptimizationQuery(include=[None], exclude=[])
py_mode = Mode("py", opts)
with config.change_flags(mode=py_mode, compute_test_value="warn"):
yield
def set_aesara_flags():
with config.change_flags(cxx="", compute_test_value="raise"):
yield
