def subgraph_pl_test(modelDict=dict(mix=mixture_model2,
family=family_model2,
unrelated=unrelated_model2,
environmental=environmental_model2),
obsSet=None):
'build multi-model hypergraph, apply to the obs, and print posterior liklihoods'
p = 1. / len(modelDict) # uninformative prior
modelWh = stats.norm(0, 1)
modelPu = stats.norm(10, 1)
if obsSet is None:
obsSet = get_family_obs(matingID=0)
stop = model.StopState(useObsLabel=False)
d = {}
d2 = {}
for model_name, model_f in modelDict.items(): # build distinct models
state = model.SilentState(model_name)
state.subgraph = model_f(modelWh, modelPu)
d[state] = p
d2[state] = {stop: 1.}
prior = model.StateGraph({'START': d})
term = model.StateGraph(d2)
dg = model.DependencyGraph({
'START': {
'model': prior
},
'model': {
'STOP': term
}
})
m = model.Model(dg, obsSet)
m.segmentGraph.p_forward(m.logPobsDict)
f = m.segmentGraph.fprob[m.start].f
fmerge = {}
for node, logP in f.items(): # merge forward calcs from subgraphs
try:
subgraph = node.segmentGraph
except AttributeError:
pass
else:
merge_forward_dict(subgraph.fprob[subgraph.start].f, logP, fmerge)
llDict = model.posterior_ll(fmerge)
print_pl(llDict)
return llDict
def subgraph_pl_test(modelDict=dict(mix=mixture_model2,
family=family_model2,
unrelated=unrelated_model2,
environmental=environmental_model2),
obsSet=None):
'build multi-model hypergraph, apply to the obs, and print posterior liklihoods'
p = 1./len(modelDict) # uninformative prior
modelWh = stats.norm(0, 1)
modelPu = stats.norm(10, 1)
if obsSet is None:
obsSet = get_family_obs(matingID=0)
stop = model.StopState(useObsLabel=False)
d = {}
d2 = {}
for model_name, model_f in modelDict.items(): # build distinct models
state = model.SilentState(model_name)
state.subgraph = model_f(modelWh, modelPu)
d[state] = p
d2[state] = {stop:1.}
prior = model.StateGraph({'START':d})
term = model.StateGraph(d2)
dg = model.DependencyGraph({'START':{'model':prior},
'model':{'STOP':term}})
m = model.Model(dg, obsSet)
m.segmentGraph.p_forward(m.logPobsDict)
f = m.segmentGraph.fprob[m.start].f
fmerge = {}
for node, logP in f.items(): # merge forward calcs from subgraphs
try:
subgraph = node.segmentGraph
except AttributeError:
pass
else:
merge_forward_dict(subgraph.fprob[subgraph.start].f, logP, fmerge)
llDict = model.posterior_ll(fmerge)
print_pl(llDict)
return llDict
def basic_pl(segmentGraph):
f = segmentGraph.fprob[segmentGraph.start].f # forward prob dictionary
return model.posterior_ll(f)
def basic_pl(segmentGraph):
f = segmentGraph.fprob[segmentGraph.start].f # forward prob dictionary
return model.posterior_ll(f)
