python-pymc 2.2+ds-1 / usr / share / pyshared / pymc / PyMCObjects.py

__docformat__='reStructuredText'
__author__ = 'Anand Patil, anand.prabhakar.patil@gmail.com'
__all__ = ['extend_children', 'extend_parents', 'ParentDict', 'Stochastic', 'Deterministic', 'Potential']


from copy import copy
try:
    import builtins
except ImportError:
    import __builtin__ as builtins
from numpy import array, ndarray, reshape, Inf, asarray, dot, sum, float, isnan, size, NaN, asanyarray
import numpy as np
from numpy import shape, size, ravel, zeros, ones, reshape, newaxis, broadcast, ndim, expand_dims 
from .Node import Node, ZeroProbability, Variable, PotentialBase, StochasticBase, DeterministicBase
from . import Container
from .Container import DictContainer, ContainerBase, file_items, ArrayContainer
import sys
import pdb
from . import calc_utils

from . import datatypes

from . import six
from .six import print_

d_neg_inf = float(-1.7976931348623157e+308)

# from PyrexLazyFunction import LazyFunction
from .LazyFunction import LazyFunction, Counter

def extend_children(children):
    """
    extend_children(children)

    Returns a set containing
    nearest conditionally stochastic (Stochastic, not Deterministic) descendants.
    """
    new_children = copy(children)
    need_recursion = False
    dtrm_children = set()

    for child in children:
        if isinstance(child,Deterministic):
            new_children |= child.children
            dtrm_children.add(child)
            need_recursion = True

    new_children -= dtrm_children

    if need_recursion:
        new_children = extend_children(new_children)

    return new_children

def extend_parents(parents):
    """
    extend_parents(parents)

    Returns a set containing
    nearest conditionally stochastic (Stochastic, not Deterministic) ancestors.
    """
    new_parents = set()

    for parent in parents:

        new_parents.add(parent)

        if isinstance(parent, DeterministicBase):
            new_parents.remove(parent)
            new_parents |= parent.extended_parents

        elif isinstance(parent, ContainerBase):
            for contained_parent in parent.stochastics:
                new_parents.add(contained_parent)
            for contained_parent in parent.deterministics:
                new_parents |= contained_parent.extended_parents


    return new_parents


class ParentDict(DictContainer):
    """
    A special subclass of DictContainer which makes it safe to change
    variables' parents. When __setitem__ is called, a ParentDict instance
    removes its owner from the old parent's children set (if appropriate)
    and adds its owner to the new parent's children set. It then asks
    its owner to generate a new LazyFunction instance using its new
    parents.

    Also manages the extended_parents attribute of owner.

    NB: StepMethod and Model are expecting variables'
    children to be static. If you want to change independence structure
    over the course of an MCMC loop, please do so with indicator variables.

    :SeeAlso: DictContainer
    """
    def __init__(self, regular_dict, owner):
        DictContainer.__init__(self, dict(regular_dict))
        self.owner = owner
        self.owner.extended_parents = extend_parents(self.variables)
        if isinstance(self.owner, StochasticBase) or isinstance(self.owner, PotentialBase):
            self.has_logp = True
        else:
            self.has_logp = False

    def detach_parents(self):
        for parent in six.itervalues(self):
            if isinstance(parent, Variable):
                parent.children.discard(self.owner)
            elif isinstance(parent, ContainerBase):
                for variable in parent.variables:
                    variable.chidren.discard(self.owner)

        if self.has_logp:
            self.detach_extended_parents()


    def detach_extended_parents(self):
        for e_parent in self.owner.extended_parents:
            if isinstance(e_parent, StochasticBase):
                e_parent.extended_children.discard(self.owner)


    def attach_parents(self):
        for parent in six.itervalues(self):
            if isinstance(parent, Variable):
                parent.children.add(self.owner)
            elif isinstance(parent, ContainerBase):
                for variable in parent.variables:
                    variable.children.add(self.owner)

        if self.has_logp:
            self.attach_extended_parents()

    def attach_extended_parents(self):
        for e_parent in self.owner.extended_parents:
            if isinstance(e_parent, StochasticBase):
                e_parent.extended_children.add(self.owner)


    def __setitem__(self, key, new_parent):
        old_parent = self[key]

        # Possibly remove owner from old parent's children set.
        if isinstance(old_parent, Variable) or isinstance(old_parent, ContainerBase):

            # Tell all extended parents to forget about owner
            if self.has_logp:
                self.detach_extended_parents()

            self.val_keys.remove(key)
            self.nonval_keys.append(key)

            if isinstance(old_parent, Variable):
                # See if owner only claims the old parent via this key.
                if sum([parent is old_parent for parent in six.itervalues(self)]) == 1:
                    old_parent.children.remove(self.owner)


            if isinstance(old_parent, ContainerBase):
                for variable in old_parent.variables:
                    if sum([parent is variable for parent in six.itervalues(self)]) == 1:
                        variable.children.remove(self.owner)



        # If the new parent is a variable, add owner to its children set.
        if isinstance(new_parent, Variable) or isinstance(new_parent, ContainerBase):

            self.val_keys.append(key)
            self.nonval_keys.remove(key)

            if isinstance(new_parent, Variable):
                new_parent.children.add(self.owner)

            elif isinstance(new_parent, ContainerBase):
                for variable in new_parent.variables:
                    new_parent.children.add(self.owner)

        # Totally recompute extended parents
        self.owner.extended_parents = extend_parents(self.variables)
        if self.has_logp:
            self.attach_extended_parents()

        dict.__setitem__(self, key, new_parent)

        file_items(self, self)

        # Tell my owner it needs a new lazy function.
        self.owner.gen_lazy_function()

class Potential(PotentialBase):
    """
    Not a variable; just an arbitrary log-probability term to multiply into the
    joint distribution. Useful for expressing models that aren't directed, such as
    Markov random fields.

    Decorator instantiation:

    @potential(trace = True)
    def A(x = B, y = C):
        return -.5 * (x-y)**2 / 3.

    Direct instantiation:

    :Parameters:

        -logp: function
              The function that computes the potential's value from the values
              of its parents.

        -doc: string
              The docstring for this potential.

        -name: string
              The name of this potential.

        -parents: dictionary
              A dictionary containing the parents of this potential.

        -cache_depth (optional): integer
              An integer indicating how many of this potential's value computations
              should be 'memoized'.

        - plot (optional) : boolean
            A flag indicating whether this variable is to be plotted.

        - verbose (optional) : integer
              Level of output verbosity: 0=none, 1=low, 2=medium, 3=high


    Externally-accessible attribute:

        -logp: float
              Returns the potential's log-probability given its parents' values. Skips
              computation if possible.

    No methods.

    :SeeAlso: Stochastic, Node, LazyFunction, stoch, dtrm, data, Model, Container
    """

    def __init__(self, logp,  doc, name, parents, cache_depth=2, plot=None, verbose=-1, logp_partial_gradients=None):

        if logp_partial_gradients is None:
            logp_partial_gradients = {}
            
        self.ParentDict = ParentDict

        # This function gets used to evaluate self's value.
        self._logp_fun = logp
        self._logp_partial_gradients_functions = logp_partial_gradients


        self.errmsg = "Potential %s forbids its parents' current values"%name

        Node.__init__(  self,
                        doc=doc,
                        name=name,
                        parents=parents,
                        cache_depth = cache_depth,
                        verbose=verbose)

        

        self._plot = plot

        # self._logp.force_compute()

        # Check initial value
        if not isinstance(self.logp, float):
            raise ValueError("Potential " + self.__name__ + "'s initial log-probability is %s, should be a float." %self.logp.__repr__())

    def gen_lazy_function(self):

        self._logp = LazyFunction(fun = self._logp_fun,
                                    arguments = self.parents,
                                    ultimate_args = self.extended_parents,
                                    cache_depth = self._cache_depth)
        self._logp.force_compute()

        self._logp_partial_gradients= {}
        for parameter, function in six.iteritems(self._logp_partial_gradients_functions):
            lazy_logp_partial_gradients = LazyFunction(fun = function,
                                            arguments = self.parents,
                                            ultimate_args = self.extended_parents,
                                            cache_depth = self._cache_depth)
            lazy_logp_partial_gradients.force_compute()
            self._logp_partial_gradients[parameter] = lazy_logp_partial_gradients

    def get_logp(self):
        if self.verbose > 1:
            print_('\t' + self.__name__ + ': log-probability accessed.')
        logp = self._logp.get()
        if self.verbose > 1:
            print_('\t' + self.__name__ + ': Returning log-probability ', logp)

        try:
            logp = float(logp)
        except:
            raise TypeError(self.__name__ + ': computed log-probability ' + str(logp) + ' cannot be cast to float')

        if logp != logp:
            raise ValueError(self.__name__ + ': computed log-probability is NaN')

        # Check if the value is smaller than a double precision infinity:
        if logp <= d_neg_inf:
            if self.verbose > 0:
                raise ZeroProbability(self.errmsg + ": %s" %self._parents.value)
            else:
                raise ZeroProbability(self.errmsg)

        return logp

    def set_logp(self,value):
        raise AttributeError('Potential '+self.__name__+'\'s log-probability cannot be set.')

    logp = property(fget = get_logp, fset=set_logp, doc="Self's log-probability value conditional on parents.")

    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 six.iteritems(self.parents):

                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))

class Deterministic(DeterministicBase):
    """
    A variable whose value is determined by the values of its parents.

    Decorator instantiation:

    @dtrm(trace=True)
    def A(x = B, y = C):
        return sqrt(x ** 2 + y ** 2)

    :Parameters:
      eval : function
        The function that computes the variable's value from the values
        of its parents.
      doc : string
        The docstring for this variable.
      name: string
        The name of this variable.
      parents: dictionary
        A dictionary containing the parents of this variable.
      trace (optional): boolean
        A boolean indicating whether this variable's value
        should be traced (in MCMC).
      cache_depth (optional): integer
        An integer indicating how many of this variable's
        value computations should be 'memoized'.
      plot (optional) : boolean
        A flag indicating whether this variable is to be plotted.
      verbose (optional) : integer
        Level of output verbosity: 0=none, 1=low, 2=medium, 3=high
      jacobian (optional) : function(parameter, **same args as function)
        function which calculates the analytical jacobian for the deterministic with respect to some parameter
      jacobian_format (optional) : dict <string, string>
        formats of the jacobians returned by the jacobian function for each parameter:
            'full' : the function returns the full jacobian 
            'broadcast_operation' : the function returns the jacobian for an operation where the argument arrays are broadcast to eachother
            'accumulation_operation' : the function returns the jacobian for an operation where the number of dimensions is reduced
        the default is 'full'

    :Attributes:
      value : any object
        Returns the variable's value given its parents' values. Skips
        computation if possible.

    :SeeAlso:
      Stochastic, Potential, deterministic, MCMC, Lambda,
      LinearCombination, Index
    """
    __array_priority__ =1000

    def __init__(self, eval,  doc, name, parents, dtype=None, trace=True, cache_depth=2, plot=None, verbose=-1, jacobians = {}, jacobian_formats = {}):

        self.ParentDict = ParentDict

        
        # This function gets used to evaluate self's value.
        self._eval_fun = eval
        self._jacobian_functions = jacobians
        self._jacobian_formats = jacobian_formats    

        Variable.__init__(  self,
                        doc=doc,
                        name=name,
                        parents=parents,
                        cache_depth = cache_depth,
                        dtype=dtype,
                        trace=trace,
                        plot=plot,
                        verbose=verbose)

        # self._value.force_compute()

        
             

    def gen_lazy_function(self):

        self._value = LazyFunction(fun = self._eval_fun,
                                    arguments = self.parents,
                                    ultimate_args = self.extended_parents,
                                    cache_depth = self._cache_depth)

        self._value.force_compute()

        self._jacobians = {}
        for parameter, function in six.iteritems(self._jacobian_functions):
            lazy_jacobian = LazyFunction(fun = function,
                                            arguments = self.parents,
                                            ultimate_args = self.extended_parents,
                                            cache_depth = self._cache_depth)
            lazy_jacobian.force_compute()
            self._jacobians[parameter] = lazy_jacobian

    def get_value(self):
        if self.verbose > 1:
            print_('\t' + self.__name__ + ': value accessed.')
        _value = self._value.get()
        if isinstance(_value, ndarray):
            _value.flags['W'] = False
        if self.verbose > 1:
            print_('\t' + self.__name__ + ': Returning value ',_value)
        return _value

    

    def set_value(self,value):
        raise AttributeError('Deterministic '+self.__name__+'\'s value cannot be set.')

    value = property(fget = get_value, fset=set_value, doc="Self's value computed from current values of parents.")

    def apply_jacobian(self, parameter, variable, gradient):
        try :
            jacobian_func = self._jacobians[parameter]
        except KeyError:
            raise NotImplementedError(repr(self) + " has no jacobian function for parameter " + parameter)
        
        jacobian = jacobian_func.get()
        
        
        mapping = self._jacobian_formats.get(parameter, 'full')

            
        p =  self._format_mapping[mapping](self, variable, jacobian, gradient)
        return p 
        
    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 = builtins.sum([child.logp_partial_gradient(self, calculation_set) for child in self.children ])

        totalGradient = 0
        for parameter, value in six.iteritems(self.parents):
            if value is variable:
                    
                totalGradient += self.apply_jacobian(parameter, variable, gradient )

        return np.reshape(totalGradient, shape(variable.value))
        
    
    def full_jacobian(self, variable, jacobian, gradient):
        return dot(np.transpose(jacobian), np.ravel(gradient)[:,np.newaxis])
    
    def transformation_operation_jacobian(self, variable, jacobian, gradient):
        return jacobian * gradient
    
    
    def broadcast_operation_jacobian(self, variable, jacobian, gradient):
        return calc_utils.sum_to_shape(id(variable), id(self), jacobian * gradient, shape(variable.value))

    def accumulation_operation_jacobian(self, variable, jacobian, gradient):   
        for i in range(ndim(jacobian)):
            if i >= ndim(gradient) or shape(gradient)[i] != shape(variable.value)[i]:
                expand_dims(gradient, i)
        return gradient * jacobian
        
    def index_operation_jacobian(self, variable, jacobian, gradient):
        derivative = zeros(shape(variable.value))
        derivative[jacobian] = gradient
        return derivative 
    
    _format_mapping = {'full' : full_jacobian,
                       'transformation_operation' : transformation_operation_jacobian,
                       'broadcast_operation' : broadcast_operation_jacobian,
                       'accumulation_operation' : accumulation_operation_jacobian,
                       'index_operation' : index_operation_jacobian}

class Stochastic(StochasticBase):

    """
    A variable whose value is not determined by the values of its parents.


    Decorator instantiation:

    @stoch(trace=True)
    def X(value = 0., mu = B, tau = C):
        return Normal_like(value, mu, tau)

    @stoch(trace=True)
    def X(value=0., mu=B, tau=C):

        def logp(value, mu, tau):
            return Normal_like(value, mu, tau)

        def random(mu, tau):
            return Normal_r(mu, tau)

        rseed = 1.


    Direct instantiation:



    - logp : function
            The function that computes the variable's log-probability from
            its value and the values of its parents.

    - doc : string
            The docstring for this variable.

    - name : string
            The name of this variable.

    - parents: dict
            A dictionary containing the parents of this variable.

    - random (optional) : function
            A function that draws a new value for this
            variable given its parents' values.

    - trace (optional) : boolean
            A boolean indicating whether this variable's value
            should be traced (in MCMC).

    - value (optional) : number or array
            An initial value for this variable

    - dtype (optional) : type
            A type for this variable.

    - rseed (optional) : integer or rseed
            A seed for this variable's rng. Either value or rseed must
            be given.

    - observed (optional) :  boolean
            A flag indicating whether this variable is data; whether
            its value is known.

    - cache_depth (optional) : integer
            An integer indicating how many of this variable's
            log-probability computations should be 'memoized'.

    - plot (optional) : boolean
            A flag indicating whether this variable is to be plotted.

    - verbose (optional) : integer
            Level of output verbosity: 0=none, 1=low, 2=medium, 3=high


    Externally-accessible attribute:

    - value: any class
          Returns this variable's current value.

    - logp: float
          Returns the variable's log-probability given its value and its
          parents' values. Skips computation if possible.

    last_value: any class
          Returns this variable's last value. Useful for rejecting
          Metropolis-Hastings jumps. See touch() and the warning below.

    Externally-accessible methods:

    random():   Draws a new value for this variable from its distribution and
                returns it.

    :SeeAlso: Deterministic, Node, LazyFunction, stoch, dtrm, data, Model, Container
    """
    __array_priority__ = 1000

    def __init__(   self,
                    logp,
                    doc,
                    name,
                    parents,
                    random=None,
                    trace=True,
                    value=None,
                    dtype=None,
                    rseed=False,
                    observed=False,
                    cache_depth=2,
                    plot=None,
                    verbose = -1,
                    isdata=None, 
                    check_logp=True,
                    logp_partial_gradients=None):

        if logp_partial_gradients is None:
            logp_partial_gradients = {}
            
        self.counter = Counter()
        self.ParentDict = ParentDict
        
        # Support legacy 'isdata' for a while
        if isdata is not None:
            print_("Deprecation Warning: the 'isdata' flag has been replaced by 'observed'. Please update your model accordingly.")
            self.observed = isdata

        # A flag indicating whether self's value has been observed.
        self._observed = observed
        # Default value of None for mask
        self._mask = None
        if observed:

            if value is None:
                raise ValueError('Stochastic %s must be given an initial value if observed=True.'%name)
                
            try:
                
                # If there are missing values, store mask to missing elements
                self._mask = value.mask
                
                # Set to value of mean of observed data
                value.fill_value = value.mean()
                value = value.filled()
                
                # Set observed flag to False, so that missing values will update
                self._observed = False
                
            except AttributeError:
                # Must not have missing values
                pass

        # This function will be used to evaluate self's log probability.
        self._logp_fun = logp

        #This function will be used to evaluate self's gradient of log probability.
        self._logp_partial_gradient_functions = logp_partial_gradients

        # This function will be used to draw values for self conditional on self's parents.
        self._random = random

        # A seed for self's rng. If provided, the initial value will be drawn. Otherwise it's
        # taken from the constructor.
        self.rseed = rseed

        self.errmsg = "Stochastic %s's value is outside its support,\n or it forbids its parents' current values."%name

        dtype = np.dtype(dtype)

        # Initialize value, either from value provided or from random function.
        try:
            if dtype.kind != 'O' and value is not None:
                self._value = asanyarray(value, dtype=dtype)
                self._value.flags['W']=False
            else:
                self._value = value
        except:
            cls, inst, tb = sys.exc_info()
            new_inst = cls('Stochastic %s: Failed to cast initial value to required dtype.\n\nOriginal error message:\n'%name + inst.message)
            six.reraise(cls, new_inst, tb)

        # Store the shape of the stochastic value
        self._shape = np.shape(self._value)
        
        Variable.__init__(  self,
                        doc=doc,
                        name=name,
                        parents=parents,
                        cache_depth=cache_depth,
                        trace=trace,
                        dtype=dtype,
                        plot=plot,
                        verbose=verbose)

        # self._logp.force_compute()
        
        self._shape = np.shape(self._value)

        if isinstance(self._value, ndarray):
            self._value.flags['W'] = False

        if check_logp:
            # Check initial value
            if not isinstance(self.logp, float):
                raise ValueError("Stochastic " + self.__name__ + "'s initial log-probability is %s, should be a float." %self.logp.__repr__())


    def gen_lazy_function(self):
        """
        Will be called by Node at instantiation.
        """

        # If value argument to __init__ was None, draw value from random method.
        if self._value is None:

            # Use random function if provided
            if self._random is not None:
                self.value = self._random(**self._parents.value)

            # Otherwise leave initial value at None and warn.
            else:
                raise ValueError('Stochastic ' + self.__name__ + "'s value initialized to None; no initial value or random method provided.")

        arguments = {}
        arguments.update(self.parents)
        arguments['value'] = self
        arguments = DictContainer(arguments)

        self._logp = LazyFunction(fun = self._logp_fun,
                                    arguments = arguments,
                                    ultimate_args = self.extended_parents | set([self]),
                                    cache_depth = self._cache_depth)
        self._logp.force_compute()

        
        self._logp_partial_gradients = {}
        for parameter, function in six.iteritems(self._logp_partial_gradient_functions):
            lazy_logp_partial_gradient = LazyFunction(fun = function,
                                            arguments = arguments,
                                            ultimate_args = self.extended_parents | set([self]),
                                            cache_depth = self._cache_depth)
            lazy_logp_partial_gradient.force_compute()
            self._logp_partial_gradients[parameter] = lazy_logp_partial_gradient
        
    def get_value(self):
        # Define value attribute
        if self.verbose > 1:
            print_('\t' + self.__name__ + ': value accessed.'    )
        return self._value

    def get_stoch_value(self):
        if self.verbose > 1:
            print_('\t' + self.__name__ + ': stoch_value accessed.')
        return self._value[self.mask]

    def set_value(self, value, force=False):
        # Record new value and increment counter

        # Value can't be updated if observed=True
        if self.observed and not force:
            raise AttributeError('Stochastic '+self.__name__+'\'s value cannot be updated if observed flag is set')

        if self.verbose > 0:
            print_('\t' + self.__name__ + ': value set to ', value)

        # Save current value as last_value
        # Don't copy because caching depends on the object's reference.
        self.last_value = self._value
        
        if self.mask is None:
            
            if self.dtype.kind != 'O':
                self._value = asanyarray(value, dtype=self.dtype)
                self._value.flags['W']=False
            else:
                self._value = value
        
        else:
            
            new_value = self.value.copy()
        
            new_value[self.mask] = asanyarray(value, dtype=self.dtype)[self.mask]
            self._value = new_value

        self.counter.click()

    value = property(fget=get_value, fset=set_value, doc="Self's current value.")
    
    def mask():
        doc = "Returns the mask for missing values"
        def fget(self):
            return self._mask
        return locals()
    mask = property(**mask())

    def shape():
        doc = "The shape of the value of self."
        def fget(self):
            if self.verbose > 1:
                print_('\t' + self.__name__ + ': shape accessed.')
            return self._shape
        return locals()
    shape = property(**shape())

    def revert(self):
        """
        Sets self's value to self's last value. Bypasses the data cleaning in
        the set_value method.
        """
        self.counter.unclick()
        self._value = self.last_value


    def get_logp(self):

        if self.verbose > 0:
            print_('\t' + self.__name__ + ': logp accessed.')
        logp = self._logp.get()

        try:
            logp = float(logp)
        except:
            raise TypeError(self.__name__ + ': computed log-probability ' + str(logp) + ' cannot be cast to float')

        if logp != logp:
            return -np.inf

        if self.verbose > 0:
            print_('\t' + self.__name__ + ': Returning log-probability ', logp)

        # Check if the value is smaller than a double precision infinity:
        if logp <= d_neg_inf:
            if self.verbose > 0:
                raise ZeroProbability(self.errmsg + "\nValue: %s\nParents' values:%s" % (self._value, self._parents.value))
            else:
                raise ZeroProbability(self.errmsg)

        return logp

    def set_logp(self, new_logp):
        raise AttributeError('Stochastic '+self.__name__+'\'s logp attribute cannot be set')

    logp = property(fget = get_logp, fset=set_logp, doc="Log-probability or log-density of self's current value\n given values of parents.")



    def logp_gradient_contribution(self, calculation_set = None):
        """
        Calculates the gradient of the joint log posterior with respect to self. 
        Calculation of the log posterior is restricted to the variables in calculation_set. 
        """
        #NEED some sort of check to see if the log p calculation has recently failed, in which case not to continue
            
        return self.logp_partial_gradient(self, calculation_set) + builtins.sum([child.logp_partial_gradient(self, calculation_set) for child in self.children] )


    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 = builtins.sum([self._pgradient(variable, parameter, value) for parameter, value in six.iteritems(self.parents)])
        
            return gradient
        else:
            return 0
    
    def _pgradient(self, variable, parameter, value):
        if value is variable:
            try :
                return np.reshape(self._logp_partial_gradients[parameter].get(), np.shape(variable.value))
            except KeyError:
                raise NotImplementedError(repr(self) + " has no gradient function for parameter " + parameter)
        else:
            return 0
     
    # Sample self's value conditional on parents.
    def random(self):
        """
        Draws a new value for a stoch conditional on its parents
        and returns it.

        Raises an error if no 'random' argument was passed to __init__.
        """

        if self._random:
            # Get current values of parents for use as arguments for _random()
            r = self._random(**self.parents.value)
        else:
            raise AttributeError('Stochastic '+self.__name__+' does not know how to draw its value, see documentation')

        if self.shape:
            r = np.reshape(r, self.shape)
                
        # Set Stochastic's value to drawn value
        if not self.observed:
            self.value = r

        return r

    # Shortcut alias to random
    rand = random

    def _get_isdata(self):
        import warnings
        warnings.warn('"isdata" is deprecated, please use "observed" instead.')
        return self._observed
    def _set_isdata(self, isdata):
        raise ValueError('Stochastic %s: "observed" flag cannot be changed.'%self.__name__)
    isdata = property(_get_isdata, _set_isdata)

    def _get_observed(self):
        return self._observed
    def _set_observed(self, observed):
        raise ValueError('Stochastic %s: "observed" flag cannot be changed.'%self.__name__)
    observed = property(_get_observed, _set_observed)


    def _get_coparents(self):
        coparents = set()
        for child in self.extended_children:
            coparents |= child.extended_parents
        coparents.add(self)
        return coparents
    coparents = property(_get_coparents, doc="All the variables whose extended children intersect with self's.")

    def _get_moral_neighbors(self):
        moral_neighbors = self.coparents | self.extended_parents | self.extended_children
        for neighbor in copy(moral_neighbors):
            if isinstance(neighbor, PotentialBase):
                moral_neighbors.remove(neighbor)
        return moral_neighbors
    moral_neighbors = property(_get_moral_neighbors, doc="Self's neighbors in the moral graph: self's Markov blanket with self removed.")

    def _get_markov_blanket(self):
        return self.moral_neighbors | set([self])
    markov_blanket = property(_get_markov_blanket, doc="Self's coparents, self's extended parents, self's children and self.")