def __call__(self, *args, **kwargs):
model = GaussianHMM(n_states=2, init_algo=self.init_algo,
reversible_type=self.reversible_type,
thresh=1e-4, n_iter=30, random_state=rs)
model.fit(X)
validate_timeseries(means, vars, transmat, model, 0.1, 0.05)
assert abs(model.fit_logprob_[-1] - model.score(X)) < 0.5
def __call__(self, *args, **kwargs):
model = GaussianHMM(n_states=2,
init_algo=self.init_algo,
reversible_type=self.reversible_type,
thresh=1e-4,
n_iter=30)
model.fit(X)
validate_timeseries(means, vars, transmat, model, 0.1, 0.05)
assert abs(model.fit_logprob_[-1] - model.score(X)) < 0.5
def test_3():
transmat = np.array([[0.2, 0.3, 0.5], [0.4, 0.4, 0.2], [0.8, 0.2, 0.0]])
means = np.array([[0.0], [10.0], [5.0]])
vars = np.array([[1.0], [2.0], [0.3]])
X = [create_timeseries(means, vars, transmat) for i in range(20)]
# For each value of various options, create a 3 state HMM and see if it is correct.
for init_algo in ('kmeans', 'GMM'):
for reversible_type in ('mle', 'transpose'):
model = GaussianHMM(n_states=3, init_algo=init_algo, reversible_type=reversible_type, thresh=1e-4, n_iter=30)
model.fit(X)
validate_timeseries(means, vars, transmat, model, 0.1, 0.1)
assert abs(model.fit_logprob_[-1]-model.score(X)) < 0.5
def test_3():
transmat = np.array([[0.2, 0.3, 0.5], [0.4, 0.4, 0.2], [0.8, 0.2, 0.0]])
means = np.array([[0.0], [10.0], [5.0]])
vars = np.array([[1.0], [2.0], [0.3]])
X = [create_timeseries(means, vars, transmat) for i in range(20)]
# For each value of various options, create a 3 state HMM and see if it is correct.
for init_algo in ('kmeans', 'GMM'):
for reversible_type in ('mle', 'transpose'):
model = GaussianHMM(n_states=3, init_algo=init_algo, reversible_type=reversible_type, thresh=1e-4, n_iter=30)
model.fit(X)
validate_timeseries(means, vars, transmat, model, 0.1, 0.1)
assert abs(model.fit_logprob_[-1]-model.score(X)) < 0.5
