import numpy as np
from sklearn.datasets import make_spd_matrix
from sklearn.utils import check_random_state
from hmmlearn.utils import normalize
# Make NumPy complain about underflows/overflows etc.
np.seterr(all="warn")
def make_covar_matrix(covariance_type, n_components, n_features,
random_state=None):
mincv = 0.1
prng = check_random_state(random_state)
if covariance_type == 'spherical':
return (mincv + mincv * prng.random_sample((n_components,))) ** 2
elif covariance_type == 'tied':
return (make_spd_matrix(n_features)
+ mincv * np.eye(n_features))
elif covariance_type == 'diag':
return (mincv + mincv *
prng.random_sample((n_components, n_features))) ** 2
elif covariance_type == 'full':
return np.array([
(make_spd_matrix(n_features, random_state=prng)
+ mincv * np.eye(n_features))
for x in range(n_components)
])
def normalized(X, axis=None):
X_copy = X.copy()
normalize(X_copy, axis=axis)
return X_copy
def log_likelihood_increasing(h, X, lengths, n_iter):
h.n_iter = 1 # make sure we do a single iteration at a time
h.init_params = '' # and don't re-init params
log_likelihoods = np.empty(n_iter, dtype=float)
for i in range(n_iter):
h.fit(X, lengths=lengths)
log_likelihoods[i] = h.score(X, lengths=lengths)
# XXX the rounding is necessary because LL can oscillate in the
# fractional part, failing the tests.
diff = np.round(np.diff(log_likelihoods), 10) >= 0
return diff.all()
