def load_inference(self, method):
if method in self.method2inference:
self.method = method
self.inference = self.method2inference[method]
else:
mthdname2props = dai.parseNameProperties(method,
self.method_aliases)
alg = dai.newInfAlg(mthdname2props.first, self.dai_factor_graph,
mthdname2props.second)
self.method2props[method] = mthdname2props
self.method2inference[method] = alg
self.method = method
self.inference = alg
def get_inf(self, name, **kwds):
"""
cls, config = network_toolkit.dai_conf.config_map[name]
prop = dai.PropertySet()
for c in config:
prop[c] = config[c]
if verbose:
prop["verbose"] = "1"
sn = dai.FactorGraph(self.vecfac)
inf = cls(sn, prop)
return inf
"""
sn = self.get_factor_graph()
prop = dai.PropertySet()
for k in kwds:
prop[k] = str(kwds[k])
inf_alg = dai.newInfAlg(name, sn, prop)
return inf_alg
def get_inf(self, name, **kwds):
"""
cls, config = network_toolkit.dai_conf.config_map[name]
prop = dai.PropertySet()
for c in config:
prop[c] = config[c]
if verbose:
prop["verbose"] = "1"
sn = dai.FactorGraph(self.vecfac)
inf = cls(sn, prop)
return inf
"""
sn = self.get_factor_graph()
prop = dai.PropertySet()
for k in kwds:
prop[k] = str(kwds[k])
inf_alg = dai.newInfAlg(name, sn, prop )
return inf_alg
def run(self, method_name, pseudo_count=0.1, **kwds):
self.dai_factor_graph.setup_factor_graph()
obsvec = dai.ObservationVec()
for sample in self.evidence_map:
obs = dai.Observation()
for var in self.evidence_map[sample]:
#obs[ self.factor_graph.var_map[var].dai_variable ] = self.evidence_map[sample][var]
#obs[ var.get_dai_value() ] = self.evidence_map[sample][var]
obs[self.dai_factor_graph.
dai_variables[var]] = self.evidence_map[sample][var]
obsvec.append(obs)
sp_vec = dai.VecSharedParameters()
for sp in self.shared_param_list:
fo = dai.FactorOrientations()
for variable_list, factor in sp.shared_list:
vec = dai.VecVar()
for v in variable_list:
vec.append(self.dai_factor_graph.dai_variables[v])
fo[factor.factor_id] = vec
total_dim = 1
for d in sp.variable_dims:
total_dim *= d
#props = dai.PropertySet()
#props["total_dim"] = str(total_dim)
#props["target_dim"] = str(sp.variable_dims[0])
#pe = dai.ParameterEstimation.construct("CondProbEstimation", props)
pseudo_counts = dai.Prob(total_dim, pseudo_count)
#for i in range(total_dim):
# pseudo_count.append( 0.01 )
pe = dai.CondProbEstimation(sp.variable_dims[0], pseudo_counts)
dai_sp = dai.SharedParameters(fo, pe)
sp_vec.append(dai_sp)
max_step = dai.MaximizationStep(sp_vec)
vec_max_step = dai.VecMaximizationStep()
vec_max_step.append(max_step)
evidence = dai.Evidence(obsvec)
#inf_alg = self.factor_graph.get_inf_bp(verbose=True)
prop = dai.PropertySet()
for k in kwds:
prop[k] = str(kwds[k])
inf_alg = dai.newInfAlg(method_name,
self.dai_factor_graph.get_factor_graph(), prop)
em_props = dai.PropertySet()
dai_em_alg = dai.EMAlg(evidence, inf_alg, vec_max_step, em_props)
while not dai_em_alg.hasSatisfiedTermConditions():
print "Cycle 1"
l = dai_em_alg.iterate()
if kwds.get("verbose", False):
print "Iteration ", dai_em_alg.Iterations(), " likelihood: ", l
for max_step in dai_em_alg:
print max_step, len(max_step)
for shared_param in max_step:
print "\t", shared_param.currentExpectations()
print "\t", shared_param.getPEst().estimate(
shared_param.currentExpectations())
for i in range(len(dai_em_alg[0])):
shared_param = dai_em_alg[0][i]
param_vec = shared_param.getPEst().estimate(
shared_param.currentExpectations())
var_list = self.shared_param_list[i].variable_set_labels
var_dims = self.shared_param_list[i].variable_dims
self.shared_param_list[i].result = param_vec
def run(self, method_name, pseudo_count=0.1, **kwds):
self.dai_factor_graph.setup_factor_graph()
obsvec = dai.ObservationVec()
for sample in self.evidence_map:
obs = dai.Observation()
for var in self.evidence_map[sample]:
#obs[ self.factor_graph.var_map[var].dai_variable ] = self.evidence_map[sample][var]
#obs[ var.get_dai_value() ] = self.evidence_map[sample][var]
obs[ self.dai_factor_graph.dai_variables[var] ] = self.evidence_map[sample][var]
obsvec.append(obs)
sp_vec = dai.VecSharedParameters()
for sp in self.shared_param_list:
fo = dai.FactorOrientations()
for variable_list, factor in sp.shared_list:
vec = dai.VecVar()
for v in variable_list:
vec.append(self.dai_factor_graph.dai_variables[v])
fo[factor.factor_id] = vec
total_dim = 1
for d in sp.variable_dims:
total_dim *= d
#props = dai.PropertySet()
#props["total_dim"] = str(total_dim)
#props["target_dim"] = str(sp.variable_dims[0])
#pe = dai.ParameterEstimation.construct("CondProbEstimation", props)
pseudo_counts = dai.Prob(total_dim, pseudo_count)
#for i in range(total_dim):
# pseudo_count.append( 0.01 )
pe = dai.CondProbEstimation(sp.variable_dims[0], pseudo_counts)
dai_sp = dai.SharedParameters(fo, pe)
sp_vec.append(dai_sp)
max_step = dai.MaximizationStep(sp_vec)
vec_max_step = dai.VecMaximizationStep()
vec_max_step.append(max_step)
evidence = dai.Evidence(obsvec)
#inf_alg = self.factor_graph.get_inf_bp(verbose=True)
prop = dai.PropertySet()
for k in kwds:
prop[k] = str(kwds[k])
inf_alg = dai.newInfAlg(method_name, self.dai_factor_graph.get_factor_graph(), prop )
em_props= dai.PropertySet()
dai_em_alg = dai.EMAlg(evidence, inf_alg, vec_max_step, em_props)
while not dai_em_alg.hasSatisfiedTermConditions():
print "Cycle 1"
l = dai_em_alg.iterate()
if kwds.get("verbose", False):
print "Iteration ", dai_em_alg.Iterations(), " likelihood: ", l
for max_step in dai_em_alg:
print max_step, len(max_step)
for shared_param in max_step:
print "\t", shared_param.currentExpectations()
print "\t", shared_param.getPEst().estimate(shared_param.currentExpectations())
for i in range(len(dai_em_alg[0])):
shared_param = dai_em_alg[0][i]
param_vec = shared_param.getPEst().estimate(shared_param.currentExpectations())
var_list = self.shared_param_list[i].variable_set_labels
var_dims = self.shared_param_list[i].variable_dims
self.shared_param_list[i].result = param_vec
