def __init__(self, lower=-numpy.inf, upper=numpy.inf, mu=0, sigma=1):
super(TruncNormal, self).__init__(
trunc_normal(lower=lower, upper=upper, mu=mu, sigma=sigma))
self._repr_args = chaospy.format_repr_kwargs(lower=(lower, -numpy.inf))
self._repr_args += chaospy.format_repr_kwargs(upper=(upper, numpy.inf))
self._repr_args += chaospy.format_repr_kwargs(lower=(mu, 0))
self._repr_args += chaospy.format_repr_kwargs(lower=(sigma, 1))
def __init__(
self,
dist,
shift=0,
scale=1,
rotation=None,
repr_args=None,
):
assert isinstance(dist,
Distribution), "'dist' should be a distribution"
if repr_args is None:
repr_args = dist._repr_args[:]
repr_args += chaospy.format_repr_kwargs(scale=(scale, 1))
repr_args += chaospy.format_repr_kwargs(shift=(shift, 0))
length = len(dist) if len(dist) > 1 else None
dependencies, parameters, rotation = chaospy.declare_dependencies(
distribution=self,
parameters=dict(shift=shift, scale=scale),
rotation=rotation,
is_operator=True,
length=length,
extra_parameters=dict(dist=dist),
)
super(ShiftScaleDistribution, self).__init__(
parameters=parameters,
rotation=rotation,
dependencies=dependencies,
repr_args=repr_args,
)
self._dist = dist
permute = numpy.zeros((len(self._rotation), len(self._rotation)),
dtype=int)
permute[numpy.arange(len(self._rotation), dtype=int),
self._rotation] = 1
self._permute = permute
def __init__(self, mu=0, sigma=1):
super(Laplace, self).__init__(
dist=laplace(),
scale=sigma,
shift=mu,
)
self._repr_args = (chaospy.format_repr_kwargs(mu=(mu, 0))+
chaospy.format_repr_kwargs(sigma=(sigma, 1)))
def __init__(self, shape=1, mu=0, sigma=1):
super(PowerNormal, self).__init__(
dist=power_normal(shape),
scale=sigma,
shift=mu,
)
self._repr_args = [shape]
self._repr_args += chaospy.format_repr_kwargs(mu=(mu, 0))
self._repr_args += chaospy.format_repr_kwargs(sigma=(sigma, 1))
def __init__(self, df=1, mu=0, sigma=1):
super(StudentT, self).__init__(
dist=student_t(df),
scale=sigma,
shift=mu,
)
self._repr_args = [df]
self._repr_args += chaospy.format_repr_kwargs(mu=(mu, 0))
self._repr_args += chaospy.format_repr_kwargs(sigma=(sigma, 1))
def __init__(self, dist, lower=None, upper=None):
"""
Constructor.
Args:
dist (Distribution):
Distribution to be truncated.
lower (Distribution, numpy.ndarray):
Lower truncation bound.
upper (Distribution, numpy.ndarray):
Upper truncation bound.
"""
assert isinstance(dist, Distribution)
repr_args = [dist]
repr_args += chaospy.format_repr_kwargs(lower=(lower, None))
repr_args += chaospy.format_repr_kwargs(upper=(upper, None))
exclusion = set()
for deps in dist._dependencies:
exclusion.update(deps)
if isinstance(lower, Distribution):
if lower.stochastic_dependent:
raise chaospy.StochasticallyDependentError(
"Joint distribution with dependencies not supported."
)
assert len(dist) == len(lower)
lower_ = lower.lower
elif lower is None:
lower = lower_ = dist.lower
else:
lower = lower_ = numpy.atleast_1d(lower)
if isinstance(upper, Distribution):
if upper.stochastic_dependent:
raise chaospy.StochasticallyDependentError(
"Joint distribution with dependencies not supported."
)
assert len(dist) == len(upper)
upper_ = upper.upper
elif upper is None:
upper = upper_ = dist.upper
else:
upper = upper_ = numpy.atleast_1d(upper)
assert numpy.all(
upper_ > lower_
), "condition `upper > lower` not satisfied: %s <= %s" % (upper_, lower_)
dependencies, parameters, rotation = chaospy.declare_dependencies(
distribution=self,
parameters=dict(lower=lower, upper=upper),
length=len(dist),
)
super(Trunc, self).__init__(
parameters=parameters,
dependencies=dependencies,
exclusion=exclusion,
repr_args=repr_args,
)
self._dist = dist
def __init__(self, shape=1, mu=0, sigma=1, scale=1, shift=0):
dist = ShiftScaleDistribution(dist=power_log_normal(shape, sigma),
scale=numpy.e**mu)
super(PowerLogNormal, self).__init__(
dist=dist,
scale=scale,
shift=shift,
repr_args=[shape] + chaospy.format_repr_kwargs(mu=(mu, 0)) +
chaospy.format_repr_kwargs(sigma=(sigma, 1)),
)
def __init__(
self,
mu,
sigma=None,
rotation=None,
):
super(MvNormal, self).__init__(
dist=normal(),
mean=mu,
covariance=sigma,
rotation=rotation,
repr_args=chaospy.format_repr_kwargs(mu=(mu, None))+
chaospy.format_repr_kwargs(sigma=(sigma, None)),
)
def __init__(self, df=1, nc=1, scale=1, shift=0):
super(ChiSquared, self).__init__(
dist=chi_squared(df, nc),
scale=scale,
shift=shift,
repr_args=[df] + chaospy.format_repr_kwargs(nc=(nc, 1)),
)
def __init__(
self,
dist,
lower=0.0,
upper=1.0,
rotation=None,
repr_args=None,
):
assert isinstance(dist,
Distribution), "'dist' should be a distribution"
assert len(dist) == 1
if repr_args is None:
repr_args = dist._repr_args[:]
repr_args += chaospy.format_repr_kwargs(lower=(lower, 0),
upper=(upper, 1))
dependencies, parameters, rotation, = chaospy.declare_dependencies(
distribution=self,
parameters=dict(lower=lower, upper=upper),
is_operator=True,
rotation=rotation,
extra_parameters=dict(dist=dist),
)
assert len(dependencies) == 1
assert len(parameters["lower"]) == 1
assert len(parameters["upper"]) == 1
super(LowerUpperDistribution, self).__init__(
parameters=parameters,
dependencies=dependencies,
rotation=rotation,
repr_args=repr_args,
)
self._dist = dist
def __init__(
self,
df,
mu,
sigma=None,
rotation=None,
):
super(MvStudentT, self).__init__(
dist=student_t(df),
mean=mu,
covariance=sigma,
rotation=rotation,
repr_args=chaospy.format_repr_kwargs(df=(df, None)) +
chaospy.format_repr_kwargs(mu=(mu, None)) +
chaospy.format_repr_kwargs(sigma=(sigma, None)),
)
def __init__(
self,
dist,
lower=0.,
upper=1.,
rotation=None,
repr_args=None,
):
assert isinstance(dist,
Distribution), "'dist' should be a distribution"
if repr_args is None:
repr_args = dist._repr_args[:]
repr_args += chaospy.format_repr_kwargs(lower=(lower, 0),
upper=(upper, 1))
dependencies, parameters, rotation, = chaospy.declare_dependencies(
distribution=self,
parameters=dict(lower=lower, upper=upper),
rotation=rotation,
)
super(LowerUpperDistribution, self).__init__(
parameters=parameters,
dependencies=dependencies,
repr_args=repr_args,
)
self._dist = dist
def __init__(self, lower, mode, upper, gamma=4):
mu = (lower + 4 * mode + upper) / 6.0
alpha = 1 + gamma * (mu - lower) / (upper - lower)
beta = 1 + gamma * (upper - mu) / (upper - lower)
LowerUpperDistribution.__init__(
self,
dist=beta_(alpha, beta),
lower=lower,
upper=upper,
)
self._repr_args = [lower, mode, upper]
self._repr_args += chaospy.format_repr_kwargs(gamma=(gamma, 4))
def __init__(self, radius=1, shift=0):
super(Wigner, self).__init__(dist=beta_(1.5, 1.5),
scale=2 * radius,
shift=shift - radius)
self._repr_args = [radius
] + chaospy.format_repr_kwargs(shift=(shift, 0))
