obs_distns = [FrozenMixtureDistribution(
components=component_library,
a_0=1.0,b_0=0.05)
for i in xrange(Nmax)]
################
# build HSMM #
################
dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1],alpha_0=25.,beta_0=25.)
for state in range(Nmax)]
model = LibraryHSMMIntNegBinVariant(
init_state_concentration=10.,
alpha_a_0=1.0,alpha_b_0=0.1,
gamma_a_0=0.1,gamma_b_0=200.,
obs_distns=obs_distns,
dur_distns=dur_distns)
for data in training_datas:
model.add_data_parallel(data,left_censoring=True)
# model.add_data(data,left_censoring=True)
##################
# infer things #
##################
train_likes = []
for i in progprint_xrange(50):
model.resample_model_parallel()
models = collections.OrderedDict()
### 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: