def logp_partial_gradient(self, variable, calculation_set=None):
"""
gets the logp gradient of this deterministic with respect to variable
"""
if self.verbose > 0:
print '\t' + self.__name__ + ': logp_partial_gradient accessed.'
if not (datatypes.is_continuous(variable)
and datatypes.is_continuous(self)):
return zeros(shape(variable.value))
# loop through all the parameters and add up all the gradients of log p with respect to the approrpiate variable
gradient = __builtin__.sum([
child.logp_partial_gradient(self, calculation_set)
for child in self.children
])
totalGradient = 0
for parameter, value in self.parents.iteritems():
if value is variable:
totalGradient += self.apply_jacobian(parameter, variable,
gradient)
return np.reshape(totalGradient, shape(variable.value))
def logp_partial_gradient(self, variable, calculation_set=None):
"""
Calculates the partial gradient of the posterior of self with respect to variable.
Returns zero if self is not in calculation_set.
"""
if (calculation_set is None) or (self in calculation_set):
if not datatypes.is_continuous(variable):
return zeros(shape(variable.value))
if variable is self:
try:
gradient_func = self._logp_partial_gradients['value']
except KeyError:
raise NotImplementedError(
repr(self) + " has no gradient function for 'value'")
gradient = np.reshape(gradient_func.get(),
np.shape(variable.value))
else:
gradient = __builtin__.sum([
self._pgradient(variable, parameter, value)
for parameter, value in self.parents.iteritems()
])
return gradient
else:
return 0
def logp_partial_gradient(self, variable, calculation_set=None):
"""
Calculates the partial gradient of the posterior of self with respect to variable.
Returns zero if self is not in calculation_set.
"""
if (calculation_set is None) or (self in calculation_set):
if not datatypes.is_continuous(variable):
return zeros(shape(variable.value))
if variable is self:
try:
gradient_func = self._logp_partial_gradients["value"]
except KeyError:
raise NotImplementedError(repr(self) + " has no gradient function for 'value'")
gradient = np.reshape(gradient_func.get(), np.shape(variable.value))
else:
gradient = __builtin__.sum(
[self._pgradient(variable, parameter, value) for parameter, value in self.parents.iteritems()]
)
return gradient
else:
return 0
def logp_partial_gradient(self, variable, calculation_set = None):
"""
gets the logp gradient of this deterministic with respect to variable
"""
if self.verbose > 0:
print '\t' + self.__name__ + ': logp_partial_gradient accessed.'
if not (datatypes.is_continuous(variable) and datatypes.is_continuous(self)):
return zeros(shape(variable.value))
# loop through all the parameters and add up all the gradients of log p with respect to the approrpiate variable
gradient = __builtin__.sum([child.logp_partial_gradient(self, calculation_set) for child in self.children ])
totalGradient = 0
for parameter, value in self.parents.iteritems():
if value is variable:
totalGradient += self.apply_jacobian(parameter, variable, gradient )
return np.reshape(totalGradient, shape(variable.value))
def logp_partial_gradient(self, variable, calculation_set = None):
gradient = 0
if self in calculation_set:
if not datatypes.is_continuous(variable):
return zeros(shape(variable.value))
for parameter, value in self.parents.iteritems():
if value is variable:
try :
grad_func = self._logp_partial_gradients[parameter]
except KeyError:
raise NotImplementedError(repr(self) + " has no gradient function for parameter " + parameter)
gradient = gradient + grad_func.get()
return np.reshape(gradient, np.shape(variable.value)) #np.reshape(gradient, np.shape(variable.value))
def logp_partial_gradient(self, variable, calculation_set=None):
gradient = 0
if self in calculation_set:
if not datatypes.is_continuous(variable):
return zeros(shape(variable.value))
for parameter, value in self.parents.iteritems():
if value is variable:
try:
grad_func = self._logp_partial_gradients[parameter]
except KeyError:
raise NotImplementedError(
repr(self) +
" has no gradient function for parameter " +
parameter)
gradient = gradient + grad_func.get()
return np.reshape(gradient, np.shape(
variable.value)) #np.reshape(gradient, np.shape(variable.value))
