def ppf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
elif isMatrix(x):
x = DataMatrix(x)
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
opInvRosen = createOperationInverseRosenblattTransformationKDE(self.dist)
opInvRosen.doTransformation(A, B)
# transform 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])
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
assert A.getNcols() == B.getNcols() == self.trainData.getNcols()
op = createOperationInverseRosenblattTransformationKDE(self.trainData)
op.doTransformation(A, B)
# transform the outcome
if isNumerical(x) or isinstance(x, DataVector):
return B.get(0, 0)
elif isinstance(x, DataMatrix):
return B.array()
def cdf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
elif isMatrix(x):
x = DataMatrix(x)
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
self.dist.cdf(A, B)
# transform 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])
elif isMatrix(x):
x = DataMatrix(x)
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
self.dist.ppf(A, B)
# transform the outcome
if isNumerical(x) or isinstance(x, DataVector):
return B.get(0, 0)
elif isinstance(x, DataMatrix):
return B.array()
def pdf(self, x):
if isNumerical(x):
x = [x]
x = tuple(x)
if x in self.testData:
return self.testData[x]
else:
raise AttributeError("No pdf value for '%s' available" % (x,))
def pdf(self, x):
if isNumerical(x):
x = [x]
x = tuple(x)
if x in self.testData:
return self.testData[x]
else:
raise AttributeError("No pdf value for '%s' available" % (x, ))
def _convertEvalPoint(self, x):
# convert the parameter to the right format
if self.dim == 1:
if isNumerical(x):
x = np.array([[x]])
elif isList(x) or len(x.shape) == 1:
x = np.array([x]).reshape(len(x), 1)
else:
x = np.array(x)
if len(x.shape) == 1:
x = np.array([x])
return x
def pdf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
if isinstance(x, DataMatrix):
A = x
res = DataVector(A.getNrows())
res.setAll(0.0)
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
res = DataVector(1)
res.setAll(0)
self.dist.pdf(A, res)
if len(res) == 1:
return res[0]
else:
return res.array()
def pdf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
if isinstance(x, DataMatrix):
A = x
res = DataVector(A.getNrows())
res.setAll(0.0)
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
res = DataVector(1)
res.setAll(0)
self.dist.pdf(A, res)
if len(res) == 1:
return res[0]
else:
return res.array()
