def setup_method(self, _):
import scipy.io
mat = scipy.io.loadmat(os.path.join(os.getcwd(), 'tests', 'data/speechDataDigits4And5.mat'))
self.X = np.hstack([mat['train4'][0], mat['train5'][0]])
dim = self.X[0].shape[0]
from mlpy.stats import normal_invwishart
emission_prior = normal_invwishart(np.ones(dim), dim, dim + 1, 0.1 * np.eye(dim))
from mlpy.stats.dbn.hmm import GaussianHMM
self.model = GaussianHMM(ncomponents=2, startprob_prior=[3, 2], emission_prior=emission_prior)
self.model.fit(self.X, n_init=3)
class TestHMM(object):
def setup_method(self, _):
import scipy.io
mat = scipy.io.loadmat(os.path.join(os.getcwd(), 'tests', 'data/speechDataDigits4And5.mat'))
self.X = np.hstack([mat['train4'][0], mat['train5'][0]])
dim = self.X[0].shape[0]
from mlpy.stats import normal_invwishart
emission_prior = normal_invwishart(np.ones(dim), dim, dim + 1, 0.1 * np.eye(dim))
from mlpy.stats.dbn.hmm import GaussianHMM
self.model = GaussianHMM(ncomponents=2, startprob_prior=[3, 2], emission_prior=emission_prior)
self.model.fit(self.X, n_init=3)
def test_score_sample(self):
obs, hidden = self.model.sample(2, 10)
self.model.score_samples(obs)
def test_decode(self):
obs = np.array([[-0.546801713509048, -0.419653261327029],
[-0.935576633566449, -0.657506401748589],
[-0.725820776888200, 0.599246387463915],
[-0.704569013561647, -0.262774535633877],
[0.687557416511721, 0.576826951661595],
[-0.192086395176301, 0.115146226815108],
[-0.132247659696746, -0.927512789802041],
[-0.295411326485649, -0.0143719802889745],
[0.927473783664851, 0.0131668616996862],
[1.44181054016088, 1.20913420252569],
[0.873087639691135, -0.881745308344566],
[0.894788321023500, 1.20110834379367],
[0.920403967222602, -0.158058390367012]])
self.model.decode(obs)
def teardown_method(self, _):
pass