def __init__(self, distribution, lower, upper, transform="infer", *args, **kwargs):
dtype = kwargs.get("dtype", theano.config.floatX)
if lower is not None:
lower = tt.as_tensor_variable(lower).astype(dtype)
if upper is not None:
upper = tt.as_tensor_variable(upper).astype(dtype)
if transform == "infer":
if lower is None and upper is None:
transform = None
default = None
elif lower is not None and upper is not None:
transform = transforms.interval(lower, upper)
default = 0.5 * (lower + upper)
elif upper is not None:
transform = transforms.upperbound(upper)
default = upper - 1
else:
transform = transforms.lowerbound(lower)
default = lower + 1
else:
default = None
super().__init__(
distribution, lower, upper, default, *args, transform=transform, **kwargs
)
def __init__(self, distribution, lower, upper,
transform='infer', *args, **kwargs):
dtype = kwargs.get('dtype', theano.config.floatX)
if lower is not None:
lower = tt.as_tensor_variable(lower).astype(dtype)
if upper is not None:
upper = tt.as_tensor_variable(upper).astype(dtype)
if transform == 'infer':
if lower is None and upper is None:
transform = None
default = None
elif lower is not None and upper is not None:
transform = transforms.interval(lower, upper)
default = 0.5 * (lower + upper)
elif upper is not None:
transform = transforms.upperbound(upper)
default = upper - 1
else:
transform = transforms.lowerbound(lower)
default = lower + 1
else:
default = None
super(_ContinuousBounded, self).__init__(
distribution=distribution, lower=lower, upper=upper,
transform=transform, default=default, *args, **kwargs)
def __init__(self, distribution, lower, upper, transform='infer', *args, **kwargs):
import pymc3.distributions.transforms as transforms
self.dist = distribution.dist(*args, **kwargs)
self.__dict__.update(self.dist.__dict__)
self.__dict__.update(locals())
if hasattr(self.dist, 'mode'):
self.mode = self.dist.mode
if transform == 'infer':
default = self.dist.default()
if not np.isinf(lower) and not np.isinf(upper):
self.transform = transforms.interval(lower, upper)
if default <= lower or default >= upper:
self.testval = 0.5 * (upper + lower)
if not np.isinf(lower) and np.isinf(upper):
self.transform = transforms.lowerbound(lower)
if default <= lower:
self.testval = lower + 1
if np.isinf(lower) and not np.isinf(upper):
self.transform = transforms.upperbound(upper)
if default >= upper:
self.testval = upper - 1
if issubclass(distribution, Discrete):
self.transform = None
def __init__(self, distribution, lower, upper,
transform='infer', *args, **kwargs):
dtype = kwargs.get('dtype', theano.config.floatX)
if lower is not None:
lower = tt.as_tensor_variable(lower).astype(dtype)
if upper is not None:
upper = tt.as_tensor_variable(upper).astype(dtype)
if transform == 'infer':
if lower is None and upper is None:
transform = None
default = None
elif lower is not None and upper is not None:
transform = transforms.interval(lower, upper)
default = 0.5 * (lower + upper)
elif upper is not None:
transform = transforms.upperbound(upper)
default = upper - 1
else:
transform = transforms.lowerbound(lower)
default = lower + 1
else:
default = None
super(_ContinuousBounded, self).__init__(
distribution=distribution, lower=lower, upper=upper,
transform=transform, default=default, *args, **kwargs)
def __init__(self,
distribution,
lower,
upper,
transform="infer",
*args,
**kwargs):
if lower is not None:
lower = tt.as_tensor_variable(floatX(lower))
if upper is not None:
upper = tt.as_tensor_variable(floatX(upper))
if transform == "infer":
if lower is None and upper is None:
transform = None
default = None
elif lower is not None and upper is not None:
transform = transforms.interval(lower, upper)
default = 0.5 * (lower + upper)
elif upper is not None:
transform = transforms.upperbound(upper)
default = upper - 1
else:
transform = transforms.lowerbound(lower)
default = lower + 1
else:
default = None
super().__init__(distribution,
lower,
upper,
default,
*args,
transform=transform,
**kwargs)
def test_interval():
for a, b in [(-4, 5.5), (.1, .7), (-10, 4.3)]:
domain = Unit * np.float64(b - a) + np.float64(a)
trans = tr.interval(a, b)
check_transform(trans, domain)
check_jacobian_det(trans, domain, elemwise=True)
vals = get_values(trans)
close_to_logical(vals > a, True, tol)
close_to_logical(vals < b, True, tol)
def test_interval():
for a, b in [(-4, 5.5), (.1, .7), (-10, 4.3)]:
domain = Unit * np.float64(b-a) + np.float64(a)
trans = tr.interval(a,b)
check_transform_identity(trans, domain)
check_jacobian_det(trans, domain, elemwise=True)
vals = get_values(trans)
close_to(vals > a, True, tol)
close_to(vals < b, True, tol)
def __init__(self, distribution, lower, upper, transform, *args, **kwargs):
self.dist = distribution.dist(*args, **kwargs)
self.__dict__.update(self.dist.__dict__)
self.__dict__.update(locals())
if hasattr(self.dist, 'mode'):
self.mode = self.dist.mode
if transform == 'interval':
self.transform = transforms.interval(lower, upper)
def test_interval():
for a, b in [(-4, 5.5), (0.1, 0.7), (-10, 4.3)]:
domain = Unit * np.float64(b - a) + np.float64(a)
def transform_params(x, z=a, y=b):
return z, y
trans = tr.interval(transform_params)
check_transform(trans, domain)
check_jacobian_det(trans, domain, elemwise=True)
vals = get_values(trans)
close_to_logical(vals > a, True, tol)
close_to_logical(vals < b, True, tol)
def test_upperbound():
def transform_params(rv_var):
return None, 0.0
trans = tr.interval(transform_params)
check_transform(trans, Rminusbig)
check_jacobian_det(trans, Rminusbig, elemwise=True)
check_jacobian_det(trans,
Vector(Rminusbig, 2),
at.dvector, [-1, -1],
elemwise=True)
vals = get_values(trans)
close_to_logical(vals < 0, True, tol)
def test_lowerbound():
def transform_params(rv_var):
return 0.0, None
trans = tr.interval(transform_params)
check_transform(trans, Rplusbig)
check_jacobian_det(trans, Rplusbig, elemwise=True)
check_jacobian_det(trans,
Vector(Rplusbig, 2),
at.dvector, [0, 0],
elemwise=True)
vals = get_values(trans)
close_to_logical(vals > 0, True, tol)
def __init__(self, lower, upper):
self.interval_transform = interval(lower, upper)
