def __init__(self,
eval,
doc,
name,
parents,
dtype=None,
trace=True,
cache_depth=2,
plot=None,
verbose=None,
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)
def __init__(
self,
eval,
doc,
name,
parents,
dtype=None,
trace=True,
cache_depth=2,
plot=None,
verbose=None,
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,
)
def addVarNode(self, nid, i, j):
"""
This function creates a variable node and adds it to the 'variables' list.
:param nid: node id.
:param i: row index of the variable.
:param j: column index of the variable.
:returns: variable node.
"""
new_var = Variable('X', nid, i, j, self.damp)
self.variables.append(new_var)
self.var_count += 1
return new_var
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 = None,
isdata=None):
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print "Deprecation Warning: the 'isdata' flag has been replaced by 'observed'.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
if observed and value is None:
raise ValueError, 'Stochastic %s must be given an initial value if observed=True.'%name
# This function will be used to evaluate self's log probability.
self._logp_fun = logp
# 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)
raise 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()
# Store the shape of the stochastic value
self._shape = np.shape(self._value)
if isinstance(self._value, ndarray):
self._value.flags['W'] = False
# 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 __init__(self,
logp,
doc,
name,
parents,
random=None,
trace=True,
value=None,
dtype=None,
rseed=False,
observed=False,
cache_depth=2,
plot=None,
verbose=None,
isdata=None,
check_logp=True,
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)
raise 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 __init__(
self,
logp,
doc,
name,
parents,
random=None,
trace=True,
value=None,
dtype=None,
rseed=False,
observed=False,
cache_depth=2,
plot=None,
verbose=None,
isdata=None,
check_logp=True,
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
)
raise 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__()
