def test_uniform(self, lower, upper, shape):
interval = tr.Interval(lower, upper)
model = self.build_model(pm.Uniform, {
'lower': lower,
'upper': upper
},
shape=shape,
transform=interval)
self.check_transform_elementwise_logp(model)
def test_uniform_ordered(self, lower, upper, shape):
interval = tr.Interval(lower, upper)
testval = np.sort(np.abs(np.random.rand(*shape)))
model = self.build_model(pm.Uniform, {
'lower': lower,
'upper': upper
},
shape=shape,
testval=testval,
transform=tr.Chain([interval, tr.ordered]))
self.check_vectortransform_elementwise_logp(model, vect_opt=0)
def test_uniform(self, lower, upper, size):
def transform_params(rv_var):
_, _, _, lower, upper = rv_var.owner.inputs
lower = at.as_tensor_variable(lower) if lower is not None else None
upper = at.as_tensor_variable(upper) if upper is not None else None
return lower, upper
interval = tr.Interval(transform_params)
model = self.build_model(pm.Uniform, {
"lower": lower,
"upper": upper
},
size=size,
transform=interval)
self.check_transform_elementwise_logp(model)
def test_uniform_ordered(self, lower, upper, size):
def transform_params(rv_var):
_, _, _, lower, upper = rv_var.owner.inputs
lower = at.as_tensor_variable(lower) if lower is not None else None
upper = at.as_tensor_variable(upper) if upper is not None else None
return lower, upper
interval = tr.Interval(transform_params)
initval = np.sort(np.abs(np.random.rand(*size)))
model = self.build_model(
pm.Uniform,
{
"lower": lower,
"upper": upper
},
size=size,
initval=initval,
transform=tr.Chain([interval, tr.ordered]),
)
self.check_vectortransform_elementwise_logp(model, vect_opt=1)
name = "unitdisk"
def backward(self, y):
return tt.stack([y[0], y[1] * tt.sqrt(1 - y[0]**2)])
def forward(self, x):
return tt.stack([x[0], x[1] / tt.sqrt(1 - x[0]**2)])
def forward_val(self, x, point=None):
return np.array([x[0], x[1] / np.sqrt(1 - x[0]**2)])
def jacobian_det(self, y):
return tt.stack((tt.zeros_like(y[0]), 0.5 * tt.log(1 - y[0]**2)))
unit_disk = tr.Chain([UnitDiskTransform(), tr.Interval(-1, 1)])
class AngleTransform(tr.Transform):
"""An angle transformation
The variable is augmented to sample an isotropic 2D normal and the angle
is given by the arctan of the ratio of the two coordinates. This will have
a uniform distribution between -pi and pi.
Args:
regularized: The amplitude of the regularization term. If ``None``,
no regularization is applied. This has no effect on the
distribution over the transformed parameter, but it can make
sampling more efficient in some cases.
