def gaussian_hmm(P, means, sigmas, pi=None, stationary=True, reversible=True):
""" Initializes a 1D-Gaussian HMM
Parameters
----------
P : ndarray(nstates,nstates)
Hidden transition matrix
means : ndarray(nstates, )
Means of Gaussian output distributions
sigmas : ndarray(nstates, )
Standard deviations of Gaussian output distributions
pi : ndarray(nstates, )
Fixed initial (if stationary=False) or fixed stationary distribution (if stationary=True).
stationary : bool, optional, default=True
If True: initial distribution is equal to stationary distribution of transition matrix
reversible : bool, optional, default=True
If True: transition matrix will fulfill detailed balance constraints.
"""
from bhmm.hmm.gaussian_hmm import GaussianHMM
from bhmm.output_models.gaussian import GaussianOutputModel
# count states
nstates = _np.array(P).shape[0]
# initialize output model
output_model = GaussianOutputModel(nstates, means, sigmas)
# initialize general HMM
from bhmm.hmm.generic_hmm import HMM as _HMM
ghmm = _HMM(P,
output_model,
Pi=pi,
stationary=stationary,
reversible=reversible)
# turn it into a Gaussian HMM
ghmm = GaussianHMM(ghmm)
return ghmm
def __init__(self, nrep=10, kernel='c'):
self.kernel = kernel
self.nrep = nrep
# variables
self.nexamples = 0
self.A = []
self.pi = []
self.pobs = []
self.T = []
self.N = []
self.alpha = []
self.beta = []
self.gamma = []
self.time_alpha = []
self.time_beta = []
self.time_gamma = []
self.time_c = []
self.time_C = []
self.time_vpath = []
self.alpha_mem = []
self.beta_mem = []
self.gamma_mem = []
self.C_mem = []
# second example
A = np.array([[0.97, 0.02, 0.01], [0.1, 0.8, 0.1], [0.01, 0.02, 0.97]])
pi = np.array([0.45, 0.1, 0.45])
T = 1000000
means = np.array([-1.0, 0.0, 1.0])
sigmas = np.array([0.5, 0.5, 0.5])
gom = GaussianOutputModel(3, means=means, sigmas=sigmas)
obs = np.random.randint(3, size=T)
pobs = gom.p_obs(obs)
self.append_example(A, pi, pobs)
def setUp(self):
nstates = 3
means = np.array([-0.5, 0.0, 0.5])
sigmas = np.array([0.2, 0.2, 0.2])
self.G = GaussianOutputModel(nstates, means=means, sigmas=sigmas)
# random Gaussian samples
self.obs = np.random.randn((10000))
def setUp(self):
self.nexamples = 0
self.A = []
self.pi = []
self.pobs = []
self.T = []
self.N = []
self.logprob = []
self.alpha = []
self.time_alpha = []
self.beta = []
self.time_beta = []
self.gamma = []
self.time_gamma = []
self.c = []
self.time_c = []
self.C = []
self.time_C = []
self.vpath = []
self.time_vpath = []
self.alpha_mem = []
self.beta_mem = []
self.gamma_mem = []
self.C_mem = []
# first toy example
A = np.array([[0.9, 0.1], [0.1, 0.9]])
pi = np.array([0.5, 0.5])
pobs = np.array([[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9],
[0.5, 0.5], [0.9, 0.1], [0.9, 0.1], [0.9, 0.1],
[0.9, 0.1], [0.9, 0.1]])
self.append_example(A, pi, pobs)
# second example
A = np.array([[0.97, 0.02, 0.01], [0.1, 0.8, 0.1], [0.01, 0.02, 0.97]])
pi = np.array([0.45, 0.1, 0.45])
T = 10000
means = np.array([-1.0, 0.0, 1.0])
sigmas = np.array([0.5, 0.5, 0.5])
gom = GaussianOutputModel(3, means=means, sigmas=sigmas)
obs = np.random.randint(3, size=T)
pobs = gom.p_obs(obs)
self.append_example(A, pi, pobs)
