def ppf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
# do the transformation
if self.grid.getStorage().dim() == 1:
op = createOperationInverseRosenblattTransformation1D(self.grid)
ans = np.ndarray(len(x))
for i, xi in enumerate(x.array()):
ans[i] = op.doTransformation1D(self.alpha, xi)
if len(ans) == 1:
return ans[0]
else:
return ans
else:
if isinstance(x, DataMatrix):
A = x
B = DataMatrix(A.getNrows(), A.getNcols())
B.setAll(0.0)
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
B = DataMatrix(1, len(x))
B.setAll(0)
# do the transformation
op = createOperationInverseRosenblattTransformation(self.grid)
op.doTransformation(self.alpha, A, B)
# extract the outcome
if isNumerical(x) or isinstance(x, DataVector):
return B.get(0, 0)
elif isinstance(x, DataMatrix):
return B.array()
def ppf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
# do the transformation
if self.grid.getStorage().dim() == 1:
op = createOperationInverseRosenblattTransformation1D(self.grid)
ans = np.ndarray(len(x))
for i, xi in enumerate(x.array()):
ans[i] = op.doTransformation1D(self.alpha, xi)
if len(ans) == 1:
return ans[0]
else:
return ans
else:
if isinstance(x, DataMatrix):
A = x
B = DataMatrix(A.getNrows(), A.getNcols())
B.setAll(0.0)
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
B = DataMatrix(1, len(x))
B.setAll(0)
# do the transformation
op = createOperationInverseRosenblattTransformation(self.grid)
op.doTransformation(self.alpha, A, B)
# extract the outcome
if isNumerical(x) or isinstance(x, DataVector):
return B.get(0, 0)
elif isinstance(x, DataMatrix):
return B.array()
def eval(self, x):
"""
Evaluate the ANOVA decomposition at the given position
@param x: coordinates to be evaluated
"""
if not self.__anova_components:
raise Exception('Do the decomposition first')
# type check
if isNumerical(x):
x = np.array(x, dtype='float')
# evaluation function
# add constant term
s = self.__E
# add higher order terms
for components in self.__anova_components.values():
s += self.__evalHigherOrderComponent(components, x)
return s
def eval(self, x):
"""
Evaluate the ANOVA decomposition at the given position
@param x: coordinates to be evaluated
"""
if not self.__anova_components:
raise Exception('Do the decomposition first')
# type check
if isNumerical(x):
x = np.array(x, dtype='float')
# evaluation function
# add constant term
s = self.__E
# add higher order terms
for components in self.__anova_components.values():
s += self.__evalHigherOrderComponent(components, x)
return s
def pdf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
return evalSGFunction(self.grid, self.alpha, DataVector([x]))
if isinstance(x, DataMatrix):
A = x
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
else:
raise AttributeError('data type "%s" is not supported in SGDEdist' % type(x))
# evaluate the sparse grid density
fx = evalSGFunctionMulti(self.grid, self.alpha, A)
# if there is just one value given, extract it from the list
if len(fx) == 1:
fx = fx[0]
return fx
def pdf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
return evalSGFunction(self.grid, self.alpha, DataVector([x]))
if isinstance(x, DataMatrix):
A = x
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
else:
raise AttributeError(
'data type "%s" is not supported in SGDEdist' % type(x))
# evaluate the sparse grid density
fx = evalSGFunctionMulti(self.grid, self.alpha, A)
# if there is just one value given, extract it from the list
if len(fx) == 1:
fx = fx[0]
return fx