Exemplos de LibraryHSMMIntNegBinVariant em Python

Exemplo n.º 1

]

################
#  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  #
##################

for i in progprint_xrange(25):
    model.resample_model()

#################

Exemplo n.º 2

    for i in range(Nmax)
]

################
#  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(Nmax)
]

model = LibraryHSMMIntNegBinVariant(init_state_concentration=10.,
                                    alpha=6.,
                                    gamma=6.,
                                    obs_distns=obs_distns,
                                    dur_distns=dur_distns)

for data in datas:
    model.add_data_parallel(data, left_censoring=True)

##################
#  infer things  #
##################

for i in progprint_xrange(25):
    model.resample_model_parallel()

# plt.figure()
# truemodel.plot()

Exemplo n.º 3

Arquivo: parallel-test.py Projeto: dattalab/pyhsmm-library-models

Nmax = 4*library_size

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4.) for i in range(Nmax)]

################
#  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(Nmax)]

model = LibraryHSMMIntNegBinVariant(
        init_state_concentration=10.,
        alpha=6.,gamma=6.,
        obs_distns=obs_distns,
        dur_distns=dur_distns)

for data in datas:
    model.add_data_parallel(data,left_censoring=True)

##################
#  infer things  #
##################

for i in progprint_xrange(25):
    model.resample_model_parallel()

# plt.figure()
# truemodel.plot()

Exemplo n.º 4

Arquivo: saving-samples.py Projeto: dattalab/pyhsmm-library-models

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4,
    weights=row)
    for row in init_weights]

################
#  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)

##################
#  infer things  #
##################

samples1 = [model.resample_and_copy() for i in progprint_xrange(1)]
samples2 = [model.resample_and_copy() for i in progprint_xrange(10)]
samples3 = [model.resample_and_copy() for i in progprint_xrange(100)]
# samples4 = [model.resample_and_copy() for i in progprint_xrange(1000)]

Exemplo n.º 5

Arquivo: continuous-synthetic-degraded.py Projeto: quliuwuyihmy/pyhsmm-library-models

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:

Exemplo n.º 6

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4.,
    weights=row)
    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)

#####################
#  sample and save  #
#####################

models = [model.resample_and_copy() for itr in progprint_xrange(100)]

with open('models.pickle','w') as outfile:
    cPickle.dump(models,outfile,protocol=-1)

with open('model.pickle','w') as outfile:

Exemplo n.º 7

Arquivo: likelihood-and-predictive-likelihood.py Projeto: dattalab/pyhsmm-library-models

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4,
    weights=row)
    for row in init_weights]

################
#  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  #
##################

for i in progprint_xrange(25):
    model.resample_model()

Exemplo n.º 8

]

################
#  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)

#####################
#  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!'

Exemplo n.º 9

Arquivo: library-hsmm-synthetic-viterbi.py Projeto: dattalab/pyhsmm-library-models

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4.,
    weights=row)
    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,left_censoring=True)

##################
#  infer things  #
##################

for i in progprint_xrange(50):
    model.resample_model()

likes = model.Viterbi_EM_fit()

plt.figure()

Exemplo n.º 10

Arquivo: continuous-synthetic-degraded.py Projeto: dattalab/pyhsmm-library-models

    for row in init_weights]

################
#  fit models  #
################

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:

Exemplo n.º 11

Arquivo: unfreeze.py Projeto: quliuwuyihmy/pyhsmm-library-models

]

################
#  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):

Exemplo n.º 12

Arquivo: truncate.py Projeto: dattalab/pyhsmm-library-models

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4,
    weights=row)
    for row in init_weights]

################
#  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)

##################
#  infer things  #
##################

train_likes = []
test_likes = []

for i in progprint_xrange(5):
# for i in progprint_xrange(50):

Exemplo n.º 13

]

################
#  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)

##################
#  infer things  #
##################

train_likes = []
test_likes = []

for i in progprint_xrange(5):
    # for i in progprint_xrange(50):

Exemplo n.º 14

Arquivo: library-hsmm-synthetic.py Projeto: dattalab/pyhsmm-library-models

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4,
    weights=row)
    for row in init_weights]

################
#  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(50):

Exemplo n.º 15

]

################
#  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, left_censoring=True)

##################
#  infer things  #
##################

for i in progprint_xrange(50):
    model.resample_model()

likes = model.Viterbi_EM_fit()

plt.figure()

Exemplo n.º 16

Arquivo: library-hsmm-synthetic-missingdata.py Projeto: dattalab/pyhsmm-library-models

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4.,
    weights=row)
    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')

Exemplo n.º 17

]

################
#  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)

##################
#  infer things  #
##################

samples1 = [model.resample_and_copy() for i in progprint_xrange(1)]
samples2 = [model.resample_and_copy() for i in progprint_xrange(10)]
samples3 = [model.resample_and_copy() for i in progprint_xrange(100)]
# samples4 = [model.resample_and_copy() for i in progprint_xrange(1000)]

Exemplo n.º 18

Arquivo: unfreeze.py Projeto: dattalab/pyhsmm-library-models

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4,
    weights=row)
    for row in init_weights]

################
#  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):

Exemplo n.º 19

Arquivo: library-hsmm-synthetic.py Projeto: quliuwuyihmy/pyhsmm-library-models

]

################
#  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(50):

Exemplo n.º 20

Arquivo: real_data_likelihoods.py Projeto: quliuwuyihmy/pyhsmm-library-models

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()

Exemplo n.º 21

Arquivo: add_data_caching_test.py Projeto: dattalab/pyhsmm-library-models

obs_distns = [FrozenMixtureDistribution(
    components=component_library,
    alpha_0=4.,
    weights=row)
    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)

#####################
#  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!'