def train_hmm_and_keep_track_of_log_likelihood(hmm, obs, n_iter=1, **kwargs):
hmm.fit(obs, n_iter=1, **kwargs)
loglikelihoods = []
for n in xrange(n_iter):
hmm.fit(obs, n_iter=1, init_params='', **kwargs)
loglikelihoods.append(sum(hmm.score(x) for x in obs))
return loglikelihoods
def train_hmm_and_keep_track_of_log_likelihood(hmm, obs, n_iter=1, **kwargs):
hmm.fit(obs, n_iter=1, **kwargs)
loglikelihoods = []
for n in xrange(n_iter):
hmm.fit(obs, n_iter=1, init_params='', **kwargs)
loglikelihoods.append(sum(hmm.score(x) for x in obs))
return loglikelihoods
def test_hmms(hmms, chain, trajectory):
"""Returns the scores for a set of hmms over a single trajectory.
Parameters:
hmms: a dictionary where the keys are the names for the hmms the the values are MultinomialHMM instances.
chain: the pre-processing chain created with create_preprocessing_chain.py
trajectory: a [n_points, n_features] matrix to be preprocessed by chain.
"""
input_traj = chain.transform(trajectory)
scores = {}
for name, hmm in hmms.iteritems():
scores[name] = hmm.score([input_traj])
return scores
def test_hmms(hmms, chain, trajectory):
"""Returns the scores for a set of hmms over a single trajectory.
Parameters:
hmms: a dictionary where the keys are the names for the hmms the the values are MultinomialHMM instances.
chain: the pre-processing chain created with create_preprocessing_chain.py
trajectory: a [n_points, n_features] matrix to be preprocessed by chain.
"""
input_traj = chain.transform(trajectory)
scores = {}
for name, hmm in hmms.iteritems():
scores[name] = hmm.score([input_traj])
return scores
def test_hmms(hmms, pca, kmeans, trajectory):
input_traj = kmeans.predict(pca.transform(trajectory))
scores = {}
for name, hmm in hmms.iteritems():
scores[name] = hmm.score( [input_traj])
return scores
def test_hmms(hmms, pca, kmeans, trajectory):
input_traj = kmeans.predict(pca.transform(trajectory))
scores = {}
for name, hmm in hmms.iteritems():
scores[name] = hmm.score([input_traj])
return scores
