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