def runTest(self):
num_vars = 5
num_vals = 10
for x in xrange(num_runs):
vs = dict([('V'+str(i),range(num_vals)) for i in xrange(num_vars)])
# should get some random data with 0.
f = Factor(variables = vs.keys()
,data = [abs(randint(0,20)) for i in xrange(num_vals**num_vars)]
#,data = [5, 0, 1, 2]
#,data = [13,0,0,20]
#,data = [14,15,20,16]
,domain = Domain()
,new_domain_variables=vs
,check = True)
records = []
for inst in f.insts():
records.append(inst + (f[inst],))
rawdata = (vs.keys(), vs, vs.keys(), records)
#print records
cf = IncrementalCompactFactor(rawdata, rmin=0)
#print 'old tree:'
#print cf
# g = Factor(variables = vs.keys()
# #,data = [13,2,0,0]
# ,data = [14,15,0,0]
# ,domain = Domain()
# ,new_domain_variables=vs
# ,check = True)
g = f.copy(copy_domain=True)
def swap_some(x):
r = random()
if x == 0:
if r <= 0.5:
return randint(1,5)
return 0
elif r <= 0.5:
return 0
return x
def invert_some(x):
return 5-x
g.map(swap_some)
#print g
f += g
records = []
for inst in g.insts():
records.append(inst + (g[inst],))
rawdata = (vs.keys(), vs, vs.keys(), records)
cf.update(rawdata)
#print 'new tree:'
#print cf
for variables in powerset(vs.keys()):
g = f.copy(copy_domain=True)
g.marginalise_away(g.variables() - frozenset(variables))
self.assert_(same_factor(g,cf.makeFactor(variables),verbose=True))
def observe(self, num_samples, skip=0):
"""Draw C{num_samples} observational samples, separated by C{skip}
steps of the sampler. These samples are appended to any existing
observational samples and then returned.
@return: L{IncrementalCompactFactor}
"""
samples = self._pure_sampler.samples(num_samples,skip)
if self._data is None:
self._data = IncrementalCompactFactor(samples,domain=Domain())
else:
self._data.update(samples)
return self._data
def runTest(self):
num_vars = 5
num_vals = 10
for i in xrange(num_runs):
vs = dict([('V'+str(i),range(num_vals)) for i in xrange(num_vars)])
# should get some random data with 0.
f = Factor(variables = vs.keys()
,data = [abs(randint(0,5)) for i in xrange(num_vals**num_vars)]
,domain = Domain()
,new_domain_variables=vs
,check = True)
records = []
for inst in f.insts():
records.append(inst + (f[inst],))
rawdata = (vs.keys(), vs, vs.keys(), records)
cf = IncrementalCompactFactor(rawdata)
for variables in powerset(vs.keys()):
g = f.copy(copy_domain=True)
g.marginalise_away(g.variables() - frozenset(variables))
self.assert_(same_factor(g,cf.makeFactor(variables),verbose=True))
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))
def query(self, intervention, num_samples,skip=0):
"""Draw C{num_samples} interventional samples, separated by C{skip}
steps of the sampler. These samples are appended to any existing
interventional samples and then returned. The intervention made is
that of C{intervention}.
@param intervention: A dictionary mapping variables in the L{CBN} to
a single value in the domain.
@return: L{IncrementalCompactFactor}
"""
k = frozenset(intervention.keys())
if not self._inter_sampler.has_key(k):
do_model = CBN.from_bn(self._pure_model.copy(copy_domain=True))
do_model.intervene(intervention)
self._inter_sampler[k] = GibbsSampler(do_model, self._burnin)
do_sampler = self._inter_sampler[k]
samples = do_sampler.samples(num_samples,skip)
if not self._inter_data.has_key(k):
self._inter_data[k] = IncrementalCompactFactor(samples,domain=Domain())
else:
self._inter_data[k].update(samples)
return self._inter_data[k]
