def test_RandomVariable_bcast():
rv = RandomVariable("normal", 0, [0, 0], config.floatX, inplace=True)
mu = tensor(config.floatX, [True, False, False])
mu.tag.test_value = np.zeros((1, 2, 3)).astype(config.floatX)
sd = tensor(config.floatX, [False, False])
sd.tag.test_value = np.ones((2, 3)).astype(config.floatX)
s1 = iscalar()
s1.tag.test_value = 1
s2 = iscalar()
s2.tag.test_value = 2
s3 = iscalar()
s3.tag.test_value = 3
s3 = Assert("testing")(s3, eq(s1, 1))
res = rv(mu, sd, size=(s1, s2, s3))
assert res.broadcastable == (False, ) * 3
size = aet.as_tensor((1, 2, 3), dtype=np.int32).astype(np.int64)
res = rv(mu, sd, size=size)
assert res.broadcastable == (True, False, False)
res = rv(0, 1, size=aet.as_tensor(1, dtype=np.int64))
assert res.broadcastable == (True, )
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_RandomVariable_bcast_specify_shape():
rv = RandomVariable("normal", 0, [0, 0], config.floatX, inplace=True)
s1 = aet.as_tensor(1, dtype=np.int64)
s2 = iscalar()
s2.tag.test_value = 2
s3 = iscalar()
s3.tag.test_value = 3
s3 = Assert("testing")(s3, eq(s1, 1))
size = specify_shape(aet.as_tensor([s1, s3, s2, s2, s1]), (5, ))
mu = tensor(config.floatX, [False, False, True])
mu.tag.test_value = np.random.normal(size=(2, 2, 1)).astype(config.floatX)
std = tensor(config.floatX, [False, True, True])
std.tag.test_value = np.ones((2, 1, 1)).astype(config.floatX)
res = rv(mu, std, size=size)
assert res.broadcastable == (True, False, False, False, True)
def test_RandomVariable_basics():
str_res = str(
RandomVariable(
"normal",
0,
[0, 0],
"float32",
inplace=True,
))
assert str_res == "normal_rv{0, (0, 0), float32, True}"
# `ndims_params` should be a `Sequence` type
with raises(TypeError, match="^Parameter ndims_params*"):
RandomVariable(
"normal",
0,
0,
config.floatX,
inplace=True,
)
# `size` should be a `Sequence` type
with raises(TypeError, match="^Parameter size*"):
RandomVariable(
"normal",
0,
[0, 0],
config.floatX,
inplace=True,
)(0, 1, size={1, 2})
# No dtype
with raises(TypeError, match="^dtype*"):
RandomVariable(
"normal",
0,
[0, 0],
inplace=True,
)(0, 1)
# Confirm that `inplace` works
rv = RandomVariable(
"normal",
0,
[0, 0],
"normal",
inplace=True,
)
assert rv.inplace
assert rv.destroy_map == {0: [0]}
# A no-params `RandomVariable`
rv = RandomVariable(name="test_rv", ndim_supp=0, ndims_params=())
with raises(TypeError):
rv.make_node(rng=1)
# `RandomVariable._infer_shape` should handle no parameters
rv_shape = rv._infer_shape(aet.constant([]), (), [])
assert rv_shape.equals(aet.constant([], dtype="int64"))
# Integer-specificed `dtype`
dtype_1 = all_dtypes[1]
rv_node = rv.make_node(None, None, 1)
rv_out = rv_node.outputs[1]
rv_out.tag.test_value = 1
assert rv_out.dtype == dtype_1
with raises(NullTypeGradError):
grad(rv_out, [rv_node.inputs[0]])
def test_RandomVariable_basics():
str_res = str(
RandomVariable(
"normal",
0,
[0, 0],
config.floatX,
inplace=True,
))
assert str_res == "normal_rv"
# `ndims_params` should be a `Sequence` type
with raises(TypeError, match="^Parameter ndims_params*"):
RandomVariable(
"normal",
0,
0,
config.floatX,
inplace=True,
)
# `size` should be a `Sequence` type
with raises(TypeError, match="^Parameter size*"):
RandomVariable(
"normal",
0,
[0, 0],
config.floatX,
inplace=True,
)(0, 1, size={1, 2})
# No dtype
with raises(TypeError, match="^dtype*"):
RandomVariable(
"normal",
0,
[0, 0],
inplace=True,
)(0, 1)
# Confirm that `inplace` works
rv = RandomVariable(
"normal",
0,
[0, 0],
"normal",
inplace=True,
)
assert rv.inplace
assert rv.destroy_map == {0: [3]}
# A no-params `RandomVariable`
rv = RandomVariable(name="test_rv", ndim_supp=0, ndims_params=())
with raises(TypeError):
rv.make_node(rng=1)
# `RandomVariable._infer_shape` should handle no parameters
rv_shape = rv._infer_shape(aet.constant([]), (), [])
assert rv_shape.equals(aet.constant([], dtype="int64"))
# Integer-specificed `dtype`
dtype_1 = all_dtypes[1]
rv_node = rv.make_node(None, None, 1)
rv_out = rv_node.outputs[1]
rv_out.tag.test_value = 1
assert rv_out.dtype == dtype_1
with raises(NullTypeGradError):
grad(rv_out, [rv_node.inputs[0]])
rv = RandomVariable("normal", 0, [0, 0], config.floatX, inplace=True)
mu = tensor(config.floatX, [True, False, False])
mu.tag.test_value = np.zeros((1, 2, 3)).astype(config.floatX)
sd = tensor(config.floatX, [False, False])
sd.tag.test_value = np.ones((2, 3)).astype(config.floatX)
s1 = iscalar()
s1.tag.test_value = 1
s2 = iscalar()
s2.tag.test_value = 2
s3 = iscalar()
s3.tag.test_value = 3
s3 = Assert("testing")(s3, eq(s1, 1))
res = rv.compute_bcast([mu, sd], (s1, s2, s3))
assert res == [False] * 3