class BinaryBayesStructure(Binary, PreBayesSpace):
"""A binary genotype with a Bayesian network structure phenotype.
Bits indicate the presence or absence of an edge.
Built on top of a variable space that determines the variables of the
Bayes net. For example, a Euclidean space, say $\mathbb{R}^d$, would have
$d$ :class:`GaussianVariable`s. Or, a Binary space would have
:class:`BinaryVariable`s.
The default choice of variable is controlled by the ``_mapping`` class
variable based on the ``type`` field of the variable space. To have mixed
networks, use a :class:`Product` space.
:param space: The space of variables
:type space: :class:`Space`
"""
_mappings = { # map space types to bayesian variables
np.ndarray: GaussianVariable,
TernaryString: BinaryVariable,
}
def __init__(self, space):
self.space = space
self.sampler = DAGSampler()
if hasattr(space, 'dim'):
self.numVariables = space.dim
else:
raise ValueError("Cannot determine the number of dimensions "
"for BinaryBayesStructure space based on given "
"space; expected space to have property dim "
"indicating the number of required variables. ")
super(BinaryBayesStructure, self).__init__(self.numVariables**2)
self.config = Config(numVariables=self.numVariables,
variableGenerator=self.variable,
randomizer=self.randomize,
sampler=self.sample)
self.proposal = StructureProposal(**self.config.__properties__)
self.config.structureGenerator = self.proposal
def convert(self, x):
if not isinstance(x, self.type):
cname = self.__class__.__name__
raise ValueError("Type mismatch in {0}.convert".format(cname))
try:
net = BayesNet(**self.config.__properties__)
edges = []
indexMap = {}
for var in net.variables:
indexMap[var.index] = var
self.proposal.network = net
for i in xrange(x.length):
if x[i]:
frm, to = i % self.numVariables, i // self.numVariables
child = indexMap[frm]
parent = indexMap[to]
try:
self.proposal.addEdge(child, parent, self.config.data)
except CyclicException:
pass
self.proposal.network = None
return net
except Exception:
traceback.print_exc()
raise ValueError("Unable to convert point due to exception")
class BinaryBayesStructure(Binary, PreBayesSpace):
"""A binary genotype with a Bayesian network structure phenotype.
Bits indicate the presence or absence of an edge.
Built on top of a variable space that determines the variables of the
Bayes net. For example, a Euclidean space, say $\mathbb{R}^d$, would have
$d$ :class:`GaussianVariable`s. Or, a Binary space would have
:class:`BinaryVariable`s.
The default choice of variable is controlled by the ``_mapping`` class
variable based on the ``type`` field of the variable space. To have mixed
networks, use a :class:`Product` space.
:param space: The space of variables
:type space: :class:`Space`
"""
_mappings = { # map space types to bayesian variables
np.ndarray: GaussianVariable,
TernaryString: BinaryVariable,
}
def __init__(self, space):
self.space = space
self.sampler = DAGSampler()
if hasattr(space, 'dim'):
self.numVariables = space.dim
else:
raise ValueError("Cannot determine the number of dimensions "
"for BinaryBayesStructure space based on given "
"space; expected space to have property dim "
"indicating the number of required variables. ")
super(BinaryBayesStructure, self).__init__(self.numVariables**2)
self.config = Config(numVariables=self.numVariables,
variableGenerator=self.variable,
randomizer=self.randomize,
sampler=self.sample)
self.proposal = StructureProposal(**self.config.__properties__)
self.config.structureGenerator = self.proposal
def convert(self, x):
if not isinstance(x, self.type):
cname = self.__class__.__name__
raise ValueError("Type mismatch in {0}.convert".format(cname))
try:
net = BayesNet(**self.config.__properties__)
edges = []
indexMap = {}
for var in net.variables:
indexMap[var.index] = var
self.proposal.network = net
for i in xrange(x.length):
if x[i]:
frm, to = i % self.numVariables, i // self.numVariables
child = indexMap[frm]
parent = indexMap[to]
try:
self.proposal.addEdge(child, parent, self.config.data)
except CyclicException:
pass
self.proposal.network = None
return net
except Exception:
traceback.print_exc()
raise ValueError("Unable to convert point due to exception")
