1],
alpha_0=5.,
beta_0=5.)
for state in range(library_size)
]
hsmm = LibraryHSMMIntNegBinVariant(init_state_concentration=10.,
alpha=6.,
gamma=2.,
obs_distns=obs_distns,
dur_distns=dur_distns)
for data in training_datas:
hsmm.add_data(data, left_censoring=True)
for itr in progprint_xrange(resample_iter):
hsmm.resample_model()
### degrade into HMM, use the same learned syllables!
hmm = LibraryHMMFixedObs(init_state_concentration=10.,
alpha=6.,
gamma=2.,
obs_distns=hsmm.obs_distns)
for data in training_datas:
hmm.add_data(data)
for itr in progprint_xrange(resample_iter):
hmm.resample_model()
### degrade into GMM, use the same learned syllables!
model = LibraryHSMMIntNegBinVariant(init_state_concentration=10.,
alpha=6.,
gamma=6.,
obs_distns=obs_distns,
dur_distns=dur_distns)
for data in training_datas:
model.add_data(data, left_censoring=True)
# model.add_data_parallel(data,left_censoring=True)
##################
# infer things #
##################
for i in progprint_xrange(25):
model.resample_model()
#################
# check likes #
#################
computed_directly = model.log_likelihood(test_data, left_censoring=True)
# NOTE: this is like model.predictive_likelihoods(test_data,[1]) but it includes
# the first frame p(y_1) term instead of just starting at p(y_2|y_1)
s = model._states_class(model=model,
data=test_data,
stateseq=np.zeros(len(test_data)),
left_censoring=True)
alphal = s.messages_forwards()
cmaxes = alphal.max(axis=1)
for state in range(library_size)]
model = LibraryHSMMIntNegBinVariant(
init_state_concentration=10.,
alpha=6.,gamma=6.,
obs_distns=obs_distns,
dur_distns=dur_distns)
for data in training_datas:
model.add_data(data,left_censoring=True)
# model.add_data_parallel(data,left_censoring=True)
##################
# infer things #
##################
train_likes = []
test_likes = []
for i in progprint_xrange(5):
model.resample_model()
# model.resample_model_parallel()
train_likes.append(model.log_likelihood())
# test_likes.append(model.log_likelihood(test_data,left_censoring=True))
newmodel = model.unfreeze()
for i in progprint_xrange(5):
newmodel.resample_model()
### HSMM
dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,0,1,1,1,1],alpha_0=5.,beta_0=5.)
for state in range(library_size)]
hsmm = LibraryHSMMIntNegBinVariant(
init_state_concentration=10.,
alpha=6.,gamma=2.,
obs_distns=obs_distns,
dur_distns=dur_distns)
for data in training_datas:
hsmm.add_data(data,left_censoring=True)
for itr in progprint_xrange(resample_iter):
hsmm.resample_model()
### degrade into HMM, use the same learned syllables!
hmm = LibraryHMMFixedObs(
init_state_concentration=10.,
alpha=6.,gamma=2.,
obs_distns=hsmm.obs_distns)
for data in training_datas:
hmm.add_data(data)
for itr in progprint_xrange(resample_iter):
hmm.resample_model()
### degrade into GMM, use the same learned syllables!