def __init__(self, bn,burnin=1000):
"""
@param bn: A causal Bayesian network from which samples are drawn.
@type bn: L{CBN}
@param burnin: The burn in for the L{GibbsSampler} used for generating
interventional data.
@type burnin: int
"""
super(CausalWorld,self).__init__(domain=Domain.copy(bn),variables=bn.variables())
self._pure_model = bn.copy(copy_domain=True)
self._pure_sampler = ForwardSampler(self._pure_model)
self._burnin = burnin
self._data = None
self._inter_sampler = {}
self._inter_data = {}
def tryModel(self, model):
# generate some samples
cf = []
sampler = ForwardSampler(model)
samples = sampler.samples(100)
cf.append(CompactFactor(samples,domain=Domain()))
icf = IncrementalCompactFactor(samples, domain=Domain())
for i in xrange(10):
samples = sampler.samples(100)
cf.append(CompactFactor(samples,domain=Domain()))
icf.update(samples)
# see if the sum of the CPT Factors in cf match that of icf
for child in model.variables():
family = model.adg().parents(child) | set([child])
a = icf.makeFactor(family)
b = cf[0].makeFactor(family)
for f in cf[1:]:
b += f.makeFactor(family)
self.assert_(same_factor(a,b))
