def test_no_extra(self):
a = tt.vector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
f_grad = ValueGradFunction([a.sum()], [a], [], mode='FAST_COMPILE')
assert f_grad.size == 3
def setUp(self):
extra1 = tt.iscalar('extra1')
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.tag.test_value = extra1_
extra1.dshape = tuple()
extra1.dsize = 1
val1 = tt.vector('val1')
val1_ = np.zeros(3, dtype=val1.dtype)
val1.tag.test_value = val1_
val1.dshape = (3,)
val1.dsize = 3
val2 = tt.matrix('val2')
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.tag.test_value = val2_
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
[self.cost], [val1, val2], [extra1], mode='FAST_COMPILE')
def setUp(self):
extra1 = at.iscalar("extra1")
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.dshape = tuple()
extra1.dsize = 1
val1 = at.vector("val1")
val1_ = np.zeros(3, dtype=val1.dtype)
val1.dshape = (3,)
val1.dsize = 3
val2 = at.matrix("val2")
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction(
[self.cost], [val1, val2], {extra1: extra1_}, mode="FAST_COMPILE"
)
def test_invalid_type(self):
a = tt.ivector('a')
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction([a.sum()], [a], [], mode='FAST_COMPILE')
err.match('Invalid dtype')
def test_invalid_type(self):
a = at.ivector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
a.dshape = (3,)
a.dsize = 3
with pytest.raises(TypeError) as err:
ValueGradFunction([a.sum()], [a], {}, mode="FAST_COMPILE")
err.match("Invalid dtype")
def test_edge_case(self):
# Edge case discovered in #2948
ndim = 3
with pm.Model() as m:
pm.Lognormal('sigma',
mu=np.zeros(ndim),
tau=np.ones(ndim),
shape=ndim) # variance for the correlation matrix
pm.HalfCauchy('nu', beta=10)
func = ValueGradFunction(m.logpt, m.basic_RVs)
func.set_extra_values(m.test_point)
q = func.dict_to_array(m.test_point)
logp, dlogp = func(q)
assert logp.size == 1
assert dlogp.size == 4
npt.assert_allclose(dlogp, 0., atol=1e-5)
def test_no_extra(self):
a = at.vector("a")
a.tag.test_value = np.zeros(3, dtype=a.dtype)
f_grad = ValueGradFunction([a.sum()], [a], {}, mode="FAST_COMPILE")
assert f_grad._extra_vars == []