def __init__(self, **kwargs):
super(Boa, self).__init__(**kwargs)
self.network = BayesNet(numVariables=self.config.space.dim,
**self.config.__properties__)
self.network = StructureProposal(**self.config.__properties__).search(
self.network)
self.trained = False
def parse(self, fname):
f = open(fname)
totalLine = ""
done = False
for line in f:
totalLine += line
lefts = len(totalLine.split("("))
rights = len(totalLine.split(")"))
if lefts == rights:
self.processLine(totalLine)
totalLine = ""
categories = [[]] * self.index
for name, idx in self.indexMap.iteritems():
categories[idx] = self.variables[name]['vals']
cfg = Config()
cfg.numVariables = len(self.variables)
cfg.variableGenerator = MultinomialVariableGenerator(categories)
cfg.randomizer = MultinomialRandomizer()
cfg.sampler = DAGSampler()
cfg.structureGenerator = StructureProposal(cfg)
net = BayesNet(cfg)
for variable in net.variables:
variable.tables = self.variables[self.revIndexMap[
variable.index]]['cpt']
#print names[variable.index], self.variables[self.revIndexMap[variable.index]]['parents']
variable.known = [
self.indexMap[parent] for parent in self.variables[
self.revIndexMap[variable.index]]['parents']
]
variable.known = sorted(variable.known)
variable.parents = dict([(i, net.variables[i])
for i in variable.known])
net.dirty = True
net.computeEdgeStatistics()
"""
for variable in net.variables:
print "(var ", self.revIndexMap[variable.index], " (", " ".join(variable.categories[variable.index]), "))"
for variable in net.variables:
print "(parents ", self.revIndexMap[variable.index], " (", " ".join([self.revIndexMap[i] for i in variable.known]), ") "
for key, val in variable.tables.iteritems():
if key == "":
expanded = ""
else:
cfg = array([int(num) for num in key.split(",")])
expanded = " ".join(self.variables[self.revIndexMap[variable.known[k]]]['vals'][c-1] for k,c in enumerate(cfg))
total = val.sum()
vals = " ".join([str(i) for i in val])
print "((", expanded, ") ", vals, (1. - total), ")"
print ")"
"""
return net
def update(self, history, fitness):
super(Boa, self).update(history, fitness)
if history.lastPopulation() is None:
return
population = [x for x, s in history.lastPopulation()]
selected = int(self.config.truncate * len(population))
self.network = BayesNet(numVariables=self.config.space.dim,
**self.config.__properties__)
self.network.config.data = None
self.network = StructureProposal(
**self.network.config.__properties__).search(self.network)
self.network.config.data = population
self.network.structureSearch(population)
self.network.update(self.history.updates, population)
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
