def pheno1_test(modelWh, modelPu):
m, obsSet = pheno1_setup(modelWh, modelPu)
logPobs = m.calc_fb()
print 'logPobs:', logPobs, m.segmentGraph.p_forward(m.logPobsDict)
llDict = m.posterior_ll()
mixModel = get_mix_model(modelWh, modelPu)
for plant in range(20):
obsLabel = obsSet.get_subset(plantID=plant)
Le = entropy.SampleEstimator(numpy.array(llDict[obsLabel]))
LeMix = entropy.sample_Le(obsLabel.get_obs(), mixModel)
Ie = Le - LeMix
He = entropy.box_entropy(obsLabel.get_obs(), 7)
Ip = -Le - He
print 'plant %d, Ie > %1.3f, mean = %1.3f\tIp > %1.3f, mean = %1.3f' \
% (plant, Ie.get_bound(), Ie.mean, Ip.get_bound(), Ip.mean)
return llDict
def pheno1_test(modelWh, modelPu):
m, obsSet = pheno1_setup(modelWh, modelPu)
logPobs = m.calc_fb()
print 'logPobs:', logPobs, m.segmentGraph.p_forward(m.logPobsDict)
llDict = m.posterior_ll()
mixModel = get_mix_model(modelWh, modelPu)
for plant in range(20):
obsLabel = obsSet.get_subset(plantID=plant)
Le = entropy.SampleEstimator(numpy.array(llDict[obsLabel]))
LeMix = entropy.sample_Le(obsLabel.get_obs(), mixModel)
Ie = Le - LeMix
He = entropy.box_entropy(obsLabel.get_obs(), 7)
Ip = -Le - He
print 'plant %d, Ie > %1.3f, mean = %1.3f\tIp > %1.3f, mean = %1.3f' \
% (plant, Ie.get_bound(), Ie.mean, Ip.get_bound(), Ip.mean)
return llDict
def calc_entropy(data, m):
vec = entropy.box_entropy(data, m)
return numpy.average(vec.sample)
