def withBetaDistribution(self, p, q, accLevel=0., width=1.):
self._dist = Beta(p, q, accLevel, width)
return self
def withTNormalDistribution(self, mu, sigma, a, b):
self._dist = TNormal(mu, sigma, a, b)
return self
def withUniformDistribution(self, a, b):
self._dist = Uniform(a, b)
return self
class UncertainParameterDesciptor(ParameterDescriptor):
"""
Descriptor, specifying one uncertain parameter
"""
def __init__(self):
super(UncertainParameterDesciptor, self).__init__()
self._dist = None
self.__trans = None
def withUniformDistribution(self, a, b):
self._dist = Uniform(a, b)
return self
def withTNormalDistribution(self, mu, sigma, a, b):
self._dist = TNormal(mu, sigma, a, b)
return self
def withNormalDistribution(self, mu, sigma, alpha):
self._dist = Normal.by_alpha(mu, sigma, alpha)
return self
def withTLognormalDistribution(self, mu, sigma, alpha):
self._dist = TLognormal.by_alpha(mu, sigma, alpha)
return self
def withLognormalDistribution(self, mu, sigma, alpha):
self._dist = Lognormal.by_alpha(mu, sigma, alpha)
return self
def withBetaDistribution(self, p, q, accLevel=0., width=1.):
self._dist = Beta(p, q, accLevel, width)
return self
def withMultivariateNormalDistribution(self, mu, cov, a, b):
self._dist = MultivariateNormal(mu, cov, a, b)
return self
def withSampleDistribution(self, samples):
self._dist = DataDist(samples)
return self
def withDistribution(self, dist):
if issubclass(dist.__class__, Dist):
self._dist = dist
return self
else:
raise TypeError('dist has to have type dists.Dist')
def withLinearTransformation(self):
if self._dist is not None:
a, b = self._dist.getBounds()
self.__trans = LinearTransformation(a, b)
else:
raise AttributeError('the distribution of "%s" is not specified \
yet but it is needed to know to apply the \
LinearTransformation' % self.__name)
return self
def withRosenblattTransformation(self):
if self._dist is not None:
self.__trans = RosenblattTransformation(self._dist)
else:
raise AttributeError(
'the distribution of "%s" is not specified yet but it is needed to know to apply the Rosenblatt transformation'
% self.__name)
return self
def withTransformation(self, trans):
self.__trans = trans
return self
def andGetResult(self):
if self._dist is None:
raise Exception("No distribution specified for parameter '%s'" %
self._name)
# if there is no transformation defined, use the
# linear transformation as standard
if self.__trans is None:
bounds = self._dist.getBounds()
if self._dist.getDim() == 1:
self.__trans = LinearTransformation(bounds[0], bounds[1])
else:
self.__trans = JointTransformation()
for i in range(self._dist.getDim()):
a, b = bounds[i]
self.__trans.add(LinearTransformation(a, b))
# check if there are enough identifiers given
if self._dist.getDim() > 1 and self._dist.getDim() != len(self._name):
raise AttributeError(
"not enough names provided; given %i (%s), expected %i" %
(len(self._name), ", ".join(self._name), self._dist.getDim()))
# check if there is and SGDE involved that need this
# transformation
if isinstance(self._dist, SGDEdist):
self._dist.transformation = self.__trans
# check wiener askey scheme
partOfWienerAskeyScheme = False
orthogPolyConfig = OrthogonalPolynomialBasis1DConfiguration()
if isinstance(self.__trans, RosenblattTransformation) or \
isinstance(self._dist, Uniform):
orthogPolyConfig.polyParameters.type_ = OrthogonalPolynomialBasisType_LEGENDRE
orthogPolyConfig.polyParameters.lowerBound_ = self._dist.getBounds(
)[0]
orthogPolyConfig.polyParameters.upperBound_ = self._dist.getBounds(
)[1]
orthogPoly = OrthogonalPolynomialBasis1D(orthogPolyConfig)
elif isinstance(self._dist, Normal):
orthogPolyConfig.polyParameters.type_ = OrthogonalPolynomialBasisType_HERMITE
orthogPoly = OrthogonalPolynomialBasis1D(orthogPolyConfig)
elif isinstance(self._dist, Beta):
orthogPolyConfig.polyParameters.type_ = OrthogonalPolynomialBasisType_JACOBI
orthogPolyConfig.polyParameters.alpha_ = self._dist.alpha()
orthogPolyConfig.polyParameters.beta_ = self._dist.beta()
orthogPoly = OrthogonalPolynomialBasis1D(orthogPolyConfig)
elif isinstance(self._dist, TLognormal):
orthogPolyConfig.polyParameters.type_ = OrthogonalPolynomialBasisType_BOUNDED_LOGNORMAL
orthogPolyConfig.polyParameters.logmean_ = np.log(self._dist.mu)
orthogPolyConfig.polyParameters.stddev_ = self._dist.sigma
orthogPolyConfig.polyParameters.lowerBound_ = self._dist.getBounds(
)[0]
orthogPolyConfig.polyParameters.upperBound_ = self._dist.getBounds(
)[1]
orthogPoly = OrthogonalPolynomialBasis1D(orthogPolyConfig)
else:
orthogPoly = None
return UncertainParameter(self._name, self._dist, self.__trans,
self._value, orthogPoly)
def withSampleDistribution(self, samples):
self._dist = DataDist(samples)
return self
def withMultivariateNormalDistribution(self, mu, cov, a, b):
self._dist = MultivariateNormal(mu, cov, a, b)
return self