def train_HMM(n_components,
observations,
lengths,
labels,
verbose=False,
cov_type='spherical',
iter=500):
AD_idx = np.where(labels == 1)
AD_lengths = lengths[AD_idx[0]]
nonAD_idx = np.where(labels != 1)
nonAD_lengths = lengths[nonAD_idx[0]]
l = np.nditer(labels)
nonAD_observations = np.zeros((0, observations.shape[1]))
AD_observations = np.zeros((0, observations.shape[1]))
for s, e, in iter_from_X_lengths(observations, lengths):
temp = observations[s:e, :]
if l.next() == 0:
nonAD_observations = np.vstack((nonAD_observations, temp))
else:
AD_observations = np.vstack((AD_observations, temp))
AD_hmm = cf.setup_and_train(n_components, AD_observations, AD_lengths,
cov_type, verbose, iter)
nonAD_hmm = cf.setup_and_train(n_components, nonAD_observations,
nonAD_lengths, cov_type, verbose, iter)
return AD_hmm, nonAD_hmm
def train_HMM(n_components, train_obs, train_len, test_obs, test_len, verbose=False,
cov_type='spherical', iter=500):
h0 = cf.setup_and_train(n_components, train_obs, train_len, cov_type,
verbose, iter)
train_state_seq = h0.predict(train_obs, train_len)
train_seq = cf.vecs2mats(train_state_seq, train_len)
test_state_seq = h0.predict(test_obs, test_len)
test_seq = cf.vecs2mats(test_state_seq, test_len)
""" Convert sequences to transition matrices """
train_transitions = np.zeros((len(train_len), n_components * n_components))
train_sequences = train_seq.astype(int)
for i, state_seq in enumerate(train_sequences):
temp = np.zeros((n_components, n_components))
for step in np.arange(1, train_len[i]):
temp[train_sequences[i, step - 1], train_sequences[i, step]] += 1
train_transitions[i, :] = temp.flatten()
test_transitions = np.zeros((len(test_len), n_components * n_components))
test_sequences = test_seq.astype(int)
for i, state_seq in enumerate(test_sequences):
temp = np.zeros((n_components, n_components))
for step in np.arange(1, test_len[i]):
temp[test_sequences[i, step - 1], test_sequences[i, step]] += 1
test_transitions[i, :] = temp.flatten()
return train_transitions, test_transitions
def train_HMM(n_comp,
train_obs,
train_len,
observations,
lengths,
verbose=True,
cov_type='spherical',
iter=500):
h0 = cf.setup_and_train(n_comp, train_obs, train_len, cov_type, verbose,
iter)
sequences = h0.predict(observations, lengths)
seq = cf.vec2mat(sequences, lengths)
return seq
def train_HMM(n_components,
observations,
lengths,
verbose=False,
cov_type='spherical',
iter=500,
params="mcts"):
h0 = cf.setup_and_train(n_components,
observations,
lengths,
cov_type,
verbose,
iter,
params=params)
return h0
def train_HMM(n_components,
train_obs,
train_len,
test_obs,
test_len,
verbose=False,
cov_type='spherical',
iter=500):
h0 = cf.setup_and_train(n_components, train_obs, train_len, cov_type,
verbose, iter)
train_state_seq = h0.predict(train_obs, train_len)
train_seq = cf.vecs2mats(train_state_seq, train_len)
test_state_seq = h0.predict(test_obs, test_len)
test_seq = cf.vecs2mats(test_state_seq, test_len)
return train_seq, test_seq