def unfreeze(self, destructive=False):
if destructive:
obs_distns = [
MixtureDistribution(weights_obj=o.weights,
components=o.components)
for o in self.obs_distns
]
dur_distns = self.dur_distns
trans_distn = self.trans_distn
else:
obs_distns = [
MixtureDistribution(
weights_obj=copy.deepcopy(o.weights),
components=[copy.deepcopy(c) for c in o.components])
for o in self.obs_distns
]
dur_distns = copy.deepcopy(self.dur_distns)
trans_distn = copy.deepcopy(self.trans_distn)
new = pyhsmm.models.HSMMIntNegBinVariant(obs_distns=obs_distns,
dur_distns=dur_distns,
trans_distn=trans_distn)
for s in self.states_list:
new.add_data(s.data,
stateseq=s.stateseq,
left_censoring=s.left_censoring)
return new
def test_hssmm(self):
import numpy as np
from matplotlib import pyplot as plt
from pyhsmm.models import HSMMIntNegBinVariant
from pyhsmm.basic.models import MixtureDistribution
from pyhsmm.basic.distributions import Gaussian, NegativeBinomialIntegerRVariantDuration
from pyhsmm.util.text import progprint_xrange
#############################
# generate synthetic data #
#############################
states_in_hsmm = 5
components_per_GMM = 3
component_hyperparameters = dict(mu_0=np.zeros(2), sigma_0=np.eye(2), kappa_0=0.01, nu_0=3)
GMMs = [MixtureDistribution(
alpha_0=4.,
components=[Gaussian(**component_hyperparameters) for i in range(components_per_GMM)])
for state in range(states_in_hsmm)]
true_dur_distns = [
NegativeBinomialIntegerRVariantDuration(np.r_[0., 0, 0, 0, 0, 1, 1, 1], alpha_0=5., beta_0=5.)
for state in range(states_in_hsmm)]
truemodel = HSMMIntNegBinVariant(
init_state_concentration=10.,
alpha=6., gamma=6.,
obs_distns=GMMs,
dur_distns=true_dur_distns)
training_datas = [truemodel.generate(1000)[0] for i in range(5)]
test_data = truemodel.generate(5000)[0]
#####################################
# set up FrozenMixture components #
#####################################
# list of all Gaussians
component_library = [c for m in GMMs for c in m.components]
library_size = len(component_library)
# initialize weights to indicator on one component
init_weights = np.eye(library_size)
#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)]
from pyhsmm.basic.models import Mixture, MixtureDistribution
from library_models import FrozenMixtureDistribution, LibraryMM
from pyhsmm.basic.distributions import Gaussian, NegativeBinomialIntegerRVariantDuration
from pyhsmm.util.text import progprint_xrange
#############################
# generate synthetic data #
#############################
groups_in_metamm = 5
components_per_gmm = 2
component_hyperparameters = dict(mu_0=np.zeros(2),sigma_0=np.eye(2),kappa_0=0.01,nu_0=3)
GMMs = [MixtureDistribution(
alpha_0=4.,
components=[Gaussian(**component_hyperparameters) for i in range(components_per_gmm)])
for state in range(groups_in_metamm)]
truemodel = Mixture(
alpha_0=6,
components=GMMs)
data, truelabels = truemodel.generate(2000)
#####################################
# set up FrozenMixture components #
#####################################
# list of all Gaussians
component_library = [c for m in GMMs for c in m.components]
#############################
# generate synthetic data #
#############################
states_in_hsmm = 5
components_per_GMM = 3
component_hyperparameters = dict(mu_0=np.zeros(2),
sigma_0=np.eye(2),
kappa_0=0.025,
nu_0=3)
GMMs = [
MixtureDistribution(alpha_0=4.,
components=[
Gaussian(**component_hyperparameters)
for i in range(components_per_GMM)
]) for state in range(states_in_hsmm)
]
true_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(states_in_hsmm)
]
truemodel = HSMMIntNegBinVariant(init_state_concentration=10.,
alpha=6.,
gamma=2.,