def __init__(self, full_target, current_state, schedule="in_turns", index_block=None):
if not isinstance(full_target, Gaussian):
raise TypeError("Given full Gaussian is not a Gaussian")
FullConditionals.__init__(self, full_target, current_state, schedule, index_block)
# precompute full covariance matrix for slicing later
self.full_Sigma = full_target.L.dot(full_target.L.T)
def __init__(self,
full_target,
current_state,
schedule="in_turns",
index_block=None):
if not isinstance(full_target, Hopfield):
raise TypeError("Given full Hopfield is not a Hopfield")
FullConditionals.__init__(self, full_target, current_state, schedule,
index_block)
def __str__(self):
s = self.__class__.__name__ + "=["
s += FullConditionals.__str__(self)
s += "]"
return s
def __str__(self):
s = self.__class__.__name__ + "=["
s += FullConditionals.__str__(self)
s += "]"
return s
def __init__(self, full_target, current_state, schedule="in_turns", index_block=None):
if not isinstance(full_target, Hopfield):
raise TypeError("Given full Hopfield is not a Hopfield")
FullConditionals.__init__(self, full_target, current_state, schedule, index_block)
def __init__(self, full_target, current_state, schedule="in_turns", index_block=None):
if not isinstance(full_target, Bernoulli):
raise TypeError("Given full Bernoulli is not a Bernoulli")
FullConditionals.__init__(self, full_target, current_state, schedule, index_block)
def __init__(self, full_target, current_state, schedule="in_turns", index_block=None):
if not isinstance(full_target, Bernoulli):
raise TypeError("Given full Bernoulli is not a Bernoulli")
FullConditionals.__init__(self, full_target, current_state, schedule, index_block)
def __str__(self):
s = self.__class__.__name__ + "=["
s += "full_Sigma=" + str(self.full_Sigma)
s += ", " + FullConditionals.__str__(self)
s += "]"
return s
