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()
################
# build HSMM #
################
dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,0,0,1,1,1],alpha_0=5.,beta_0=5.)
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))
dur_distns = [
NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 1, 1, 1, 1, 1],
alpha_0=5.,
beta_0=5.)
for state in range(library_size)
]
model = LibraryHSMMIntNegBinVariant(init_state_concentration=10.,
alpha=6.,
gamma=6.,
obs_distns=obs_distns,
dur_distns=dur_distns)
#####################
# add_data timing #
#####################
print 'this one should be slower!'
tic = time.time()
model.add_data(data)
toc = time.time()
print '...done in %f seconds' % (toc - tic)
print ''
print 'this one sholud be faster!'
tic = time.time()
model.add_data(data)
toc = time.time()
print '...done in %f seconds' % (toc - tic)
print ''
for row in init_weights]
################
# build HSMM #
################
dur_distns = [NegativeBinomialIntegerRVariantDuration(np.r_[0.,0,0,1,1,1,1,1],alpha_0=5.,beta_0=5.)
for state in range(library_size)]
model = LibraryHSMMIntNegBinVariant(
init_state_concentration=10.,
alpha=6.,gamma=6.,
obs_distns=obs_distns,
dur_distns=dur_distns)
model.add_data(data)
##################
# infer things #
##################
for i in progprint_xrange(50):
model.resample_model()
plt.figure()
truemodel.plot()
plt.gcf().suptitle('truth')
plt.figure()
model.plot()
plt.gcf().suptitle('inferred')
dur_distns = [
NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 0, 0, 1, 1, 1],
alpha_0=5.,
beta_0=5.)
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 #
##################
for i in progprint_xrange(25):
model.resample_model()
#################
# check likes #
#################
computed_directly = model.log_likelihood(test_data, left_censoring=True)
################
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:
hmm.add_data(data)
for itr in progprint_xrange(resample_iter):
hmm.resample_model()