def test_fit(self, params='ste', n_iter=15, verbose=False, **kwargs):
h = self.h
# Create training data by sampling from the HMM.
train_obs = [h.sample(n=10)[0] for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.startprob_ = hmm.normalize(self.prng.rand(self.n_components))
h.transmat_ = hmm.normalize(self.prng.rand(self.n_components,
self.n_components),
axis=1)
h.emissionprob_ = hmm.normalize(self.prng.rand(self.n_components,
self.n_symbols),
axis=1)
trainll = train_hmm_and_keep_track_of_log_likelihood(h,
train_obs,
n_iter=n_iter,
params=params,
**kwargs)[1:]
# Check that the loglik is always increasing during training
if not np.all(np.diff(trainll) > 0) and verbose:
print
print 'Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll))
self.assertTrue(np.all(np.diff(trainll) > -1.e-3))
def test_fit(self, params='stmwc', n_iter=5, verbose=False, **kwargs):
h = hmm.GMMHMM(self.n_components)
h.startprob = self.startprob
h.transmat = hmm.normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.gmms = self.gmms
# Create training data by sampling from the HMM.
train_obs = [h.rvs(n=10, random_state=self.prng) for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.fit(train_obs, n_iter=0)
h.transmat = hmm.normalize(self.prng.rand(self.n_components,
self.n_components),
axis=1)
h.startprob = hmm.normalize(self.prng.rand(self.n_components))
trainll = train_hmm_and_keep_track_of_log_likelihood(h,
train_obs,
n_iter=n_iter,
params=params,
covars_prior=1.0,
**kwargs)[1:]
if not np.all(np.diff(trainll) > 0) and verbose:
print
print 'Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll))
self.assertTrue(np.all(np.diff(trainll) > -0.5))
def test_fit(self, params='stmwc', n_iter=5, verbose=False, **kwargs):
h = hmm.GMMHMM(self.n_components, covars_prior=1.0)
h.startprob_ = self.startprob
h.transmat_ = hmm.normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.gmms = self.gmms
# Create training data by sampling from the HMM.
train_obs = [h.sample(n=10,
random_state=self.prng)[0] for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.n_iter = 0
h.fit(train_obs)
h.transmat_ = hmm.normalize(self.prng.rand(self.n_components,
self.n_components), axis=1)
h.startprob_ = hmm.normalize(self.prng.rand(self.n_components))
trainll = train_hmm_and_keep_track_of_log_likelihood(
h, train_obs, n_iter=n_iter, params=params)[1:]
if not np.all(np.diff(trainll) > 0) and verbose:
print
print 'Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll))
# XXX: this test appears to check that training log likelihood should
# never be decreasing (up to a tolerance of 0.5, why?) but this is not
# the case when the seed changes.
raise SkipTest("Unstable test: trainll is not always increasing "
"depending on seed")
self.assertTrue(np.all(np.diff(trainll) > -0.5))
def test_fit(self, params='ste', n_iter=15, verbose=False, **kwargs):
np.random.seed(0)
h = hmm.MultinomialHMM(self.n_components,
startprob=self.startprob,
transmat=self.transmat)
h.emissionprob = self.emissionprob
# Create training data by sampling from the HMM.
train_obs = [h.rvs(n=10) for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.startprob = hmm.normalize(self.prng.rand(self.n_components))
h.transmat = hmm.normalize(self.prng.rand(self.n_components,
self.n_components),
axis=1)
h.emissionprob = hmm.normalize(self.prng.rand(self.n_components,
self.n_symbols),
axis=1)
trainll = train_hmm_and_keep_track_of_log_likelihood(h,
train_obs,
n_iter=n_iter,
params=params,
**kwargs)[1:]
if not np.all(np.diff(trainll) > 0) and verbose:
print
print 'Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll))
self.assertTrue(np.all(np.diff(trainll) > 0))
def test_fit(self, params='stmwc', n_iter=5, verbose=False, **kwargs):
h = hmm.GMMHMM(self.n_components, covars_prior=1.0)
h.startprob_ = self.startprob
h.transmat_ = hmm.normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.gmms_ = self.gmms_
# Create training data by sampling from the HMM.
train_obs = [h.sample(n=10, random_state=self.prng)[0]
for x in range(10)]
# Mess up the parameters and see if we can re-learn them.
h.n_iter = 0
h.fit(train_obs)
h.transmat_ = hmm.normalize(self.prng.rand(self.n_components,
self.n_components), axis=1)
h.startprob_ = hmm.normalize(self.prng.rand(self.n_components))
trainll = train_hmm_and_keep_track_of_log_likelihood(
h, train_obs, n_iter=n_iter, params=params)[1:]
if not np.all(np.diff(trainll) > 0) and verbose:
print('Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll)))
# XXX: this test appears to check that training log likelihood should
# never be decreasing (up to a tolerance of 0.5, why?) but this is not
# the case when the seed changes.
raise SkipTest("Unstable test: trainll is not always increasing "
"depending on seed")
self.assertTrue(np.all(np.diff(trainll) > -0.5))
def fromJSON(self, jsonObj):
"""Initializes the prototype with a JSON dictionary."""
super(PrototypeHMM, self).fromJSON(jsonObj)
self.N = jsonObj["N"]
self.model = WeightedGaussianHMM(self.N, "diag", algorithm="map", params="mc")
self.model.n_features = jsonObj["n_features"]
self.model.transmat_ = normalize(jsonObj["transmat"], axis=1)
self.model.startprob_ = normalize(jsonObj["startprob"], axis=0)
self.model._means_ = np.asarray(jsonObj["means"])
self.model._covars_ = np.asarray(jsonObj["covars"])
def _do_mstep(self, stats, params):
if self.startprob_prior is None:
self.startprob_prior = 1.0
if self.transmat_prior is None:
self.transmat_prior = 1.0
if 's' in params:
self.startprob_ = normalize(
np.maximum(self.startprob_prior - 1.0 + stats['start'], 1e-20))
if 't' in params:
self.transmat_ = normalize(
np.maximum(self.transmat_prior - 1.0 + stats['trans'], 1e-20),
axis=1)
def test_fit(self, params='stmc', n_iter=25, verbose=False, **kwargs):
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
h.startprob_ = self.startprob
h.transmat_ = hmm.normalize(self.transmat
+ np.diag(self.prng.rand(self.n_components)), 1)
h.means_ = 20 * self.means
h.covars_ = self.covars[self.covariance_type]
# Create training data by sampling from the HMM.
train_obs = [h.sample(n=10)[0] for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.n_iter = 0
h.fit(train_obs)
trainll = train_hmm_and_keep_track_of_log_likelihood(
h, train_obs, n_iter=n_iter, params=params, **kwargs)[1:]
# Check that the loglik is always increasing during training
if not np.all(np.diff(trainll) > 0) and verbose:
print
print ('Test train: %s (%s)\n %s\n %s'
% (self.covariance_type, params, trainll, np.diff(trainll)))
delta_min = np.diff(trainll).min()
self.assertTrue(
delta_min > -0.8,
"The min nll increase is %f which is lower than the admissible"
" threshold of %f, for model %s. The likelihoods are %s."
% (delta_min, -0.8, self.covariance_type, trainll))
def test_fit(self, params='stmc', n_iter=25, verbose=False, **kwargs):
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
h.startprob_ = self.startprob
h.transmat_ = hmm.normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.means_ = 20 * self.means
h.covars_ = self.covars[self.covariance_type]
# Create training data by sampling from the HMM.
train_obs = [h.sample(n=10)[0] for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.fit(train_obs, n_iter=0)
trainll = train_hmm_and_keep_track_of_log_likelihood(h,
train_obs,
n_iter=n_iter,
params=params,
**kwargs)[1:]
# Check that the loglik is always increasing during training
if not np.all(np.diff(trainll) > 0) and verbose:
print
print('Test train: %s (%s)\n %s\n %s' %
(self.covariance_type, params, trainll, np.diff(trainll)))
delta_min = np.diff(trainll).min()
self.assertTrue(
delta_min > -0.8,
"The min nll increase is %f which is lower than the admissible"
" threshold of %f, for model %s. The likelihoods are %s." %
(delta_min, -0.8, self.covariance_type, trainll))
def test_fit(self, params="ste", n_iter=5, verbose=False, **kwargs):
h = self.h
# Create training data by sampling from the HMM.
train_obs = [h.sample(n=10)[0] for x in range(10)]
# Mess up the parameters and see if we can re-learn them.
h.startprob_ = hmm.normalize(self.prng.rand(self.n_components))
h.transmat_ = hmm.normalize(self.prng.rand(self.n_components, self.n_components), axis=1)
h.emissionprob_ = hmm.normalize(self.prng.rand(self.n_components, self.n_symbols), axis=1)
trainll = train_hmm_and_keep_track_of_log_likelihood(h, train_obs, n_iter=n_iter, params=params, **kwargs)[1:]
# Check that the loglik is always increasing during training
if not np.all(np.diff(trainll) > 0) and verbose:
print("Test train: (%s)\n %s\n %s" % (params, trainll, np.diff(trainll)))
self.assertTrue(np.all(np.diff(trainll) > -1.0e-3))
def test_fit(self, params='ste', n_iter=15, verbose=False, **kwargs):
np.random.seed(0)
h = hmm.MultinomialHMM(self.n_components,
startprob=self.startprob,
transmat=self.transmat)
h.emissionprob = self.emissionprob
# Create training data by sampling from the HMM.
train_obs = [h.rvs(n=10) for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.startprob = hmm.normalize(self.prng.rand(self.n_components))
h.transmat = hmm.normalize(self.prng.rand(self.n_components,
self.n_components), axis=1)
h.emissionprob = hmm.normalize(
self.prng.rand(self.n_components, self.n_symbols), axis=1)
trainll = train_hmm_and_keep_track_of_log_likelihood(
h, train_obs, n_iter=n_iter, params=params, **kwargs)[1:]
if not np.all(np.diff(trainll) > 0) and verbose:
print
print 'Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll))
self.assertTrue(np.all(np.diff(trainll) > 0))
def create_random_gmm(n_mix, n_features, covariance_type, prng=0):
prng = check_random_state(prng)
g = mixture.GMM(n_mix, covariance_type=covariance_type)
g.means_ = prng.randint(-20, 20, (n_mix, n_features))
mincv = 0.1
g.covars_ = {
"spherical": (mincv + mincv * np.dot(prng.rand(n_mix, 1), np.ones((1, n_features)))) ** 2,
"tied": (make_spd_matrix(n_features, random_state=prng) + mincv * np.eye(n_features)),
"diag": (mincv + mincv * prng.rand(n_mix, n_features)) ** 2,
"full": np.array(
[make_spd_matrix(n_features, random_state=prng) + mincv * np.eye(n_features) for x in range(n_mix)]
),
}[covariance_type]
g.weights_ = hmm.normalize(prng.rand(n_mix))
return g
def test_fit(self, params='stmwc', n_iter=5, verbose=False, **kwargs):
h = hmm.GMMHMM(self.n_components)
h.startprob = self.startprob
h.transmat = hmm.normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.gmms = self.gmms
# Create training data by sampling from the HMM.
train_obs = [h.rvs(n=10, random_state=self.prng) for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.fit(train_obs, n_iter=0)
h.transmat = hmm.normalize(self.prng.rand(self.n_components,
self.n_components), axis=1)
h.startprob = hmm.normalize(self.prng.rand(self.n_components))
trainll = train_hmm_and_keep_track_of_log_likelihood(
h, train_obs, n_iter=n_iter, params=params,
covars_prior=1.0, **kwargs)[1:]
if not np.all(np.diff(trainll) > 0) and verbose:
print
print 'Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll))
self.assertTrue(np.all(np.diff(trainll) > -0.5))
def create_random_gmm(n_mix, n_features, cvtype, prng=prng):
from sklearn import mixture
g = mixture.GMM(n_mix, cvtype=cvtype)
g.means = prng.randint(-20, 20, (n_mix, n_features))
mincv = 0.1
g.covars = {
'spherical': (mincv + mincv * prng.rand(n_mix)) ** 2,
'tied': (make_spd_matrix(n_features, random_state=prng)
+ mincv * np.eye(n_features)),
'diag': (mincv + mincv * prng.rand(n_mix, n_features)) ** 2,
'full': np.array(
[make_spd_matrix(n_features, random_state=prng)
+ mincv * np.eye(n_features) for x in xrange(n_mix)])
}[cvtype]
g.weights = hmm.normalize(prng.rand(n_mix))
return g
def create_random_gmm(n_mix, n_features, covariance_type, prng=0):
prng = check_random_state(prng)
g = mixture.GMM(n_mix, covariance_type=covariance_type)
g.means_ = prng.randint(-20, 20, (n_mix, n_features))
mincv = 0.1
g.covars_ = {
'spherical': (mincv + mincv * np.dot(prng.rand(n_mix, 1),
np.ones((1, n_features)))) ** 2,
'tied': (make_spd_matrix(n_features, random_state=prng)
+ mincv * np.eye(n_features)),
'diag': (mincv + mincv * prng.rand(n_mix, n_features)) ** 2,
'full': np.array(
[make_spd_matrix(n_features, random_state=prng)
+ mincv * np.eye(n_features) for x in range(n_mix)])
}[covariance_type]
g.weights_ = hmm.normalize(prng.rand(n_mix))
return g
def create_random_gmm(n_mix, n_features, cvtype, prng=prng):
from sklearn import mixture
g = mixture.GMM(n_mix, cvtype=cvtype)
g.means = prng.randint(-20, 20, (n_mix, n_features))
mincv = 0.1
g.covars = {
'spherical': (mincv + mincv * prng.rand(n_mix))**2,
'tied': (make_spd_matrix(n_features, random_state=prng) +
mincv * np.eye(n_features)),
'diag': (mincv + mincv * prng.rand(n_mix, n_features))**2,
'full':
np.array([
make_spd_matrix(n_features, random_state=prng) +
mincv * np.eye(n_features) for x in xrange(n_mix)
])
}[cvtype]
g.weights = hmm.normalize(prng.rand(n_mix))
return g
def test_fit_with_priors(self, params='stmc', n_iter=5, verbose=False):
startprob_prior = 10 * self.startprob + 2.0
transmat_prior = 10 * self.transmat + 2.0
means_prior = self.means
means_weight = 2.0
covars_weight = 2.0
if self.covariance_type in ('full', 'tied'):
covars_weight += self.n_features
covars_prior = self.covars[self.covariance_type]
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
h.startprob_ = self.startprob
h.startprob_prior = startprob_prior
h.transmat_ = hmm.normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.transmat_prior = transmat_prior
h.means_ = 20 * self.means
h.means_prior = means_prior
h.means_weight = means_weight
h.covars_ = self.covars[self.covariance_type]
h.covars_prior = covars_prior
h.covars_weight = covars_weight
# Create training data by sampling from the HMM.
train_obs = [h.sample(n=10)[0] for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.n_iter = 0
h.fit(train_obs[:1])
trainll = train_hmm_and_keep_track_of_log_likelihood(h,
train_obs,
n_iter=n_iter,
params=params)[1:]
# Check that the loglik is always increasing during training
if not np.all(np.diff(trainll) > 0) and verbose:
print
print('Test MAP train: %s (%s)\n %s\n %s' %
(self.covariance_type, params, trainll, np.diff(trainll)))
# XXX: Why such a large tolerance?
self.assertTrue(np.all(np.diff(trainll) > -0.5))
def test_fit_with_priors(self, params='stmc', n_iter=10, verbose=False):
startprob_prior = 10 * self.startprob + 2.0
transmat_prior = 10 * self.transmat + 2.0
means_prior = self.means
means_weight = 2.0
covars_weight = 2.0
if self.covariance_type in ('full', 'tied'):
covars_weight += self.n_features
covars_prior = self.covars[self.covariance_type]
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
h.startprob_ = self.startprob
h.startprob_prior = startprob_prior
h.transmat_ = hmm.normalize(self.transmat
+ np.diag(self.prng.rand(self.n_components)), 1)
h.transmat_prior = transmat_prior
h.means_ = 20 * self.means
h.means_prior = means_prior
h.means_weight = means_weight
h.covars_ = self.covars[self.covariance_type]
h.covars_prior = covars_prior
h.covars_weight = covars_weight
# Create training data by sampling from the HMM.
train_obs = [h.sample(n=10)[0] for x in xrange(10)]
# Mess up the parameters and see if we can re-learn them.
h.n_iter = 0
h.fit(train_obs[:1])
trainll = train_hmm_and_keep_track_of_log_likelihood(
h, train_obs, n_iter=n_iter, params=params)[1:]
# Check that the loglik is always increasing during training
if not np.all(np.diff(trainll) > 0) and verbose:
print
print ('Test MAP train: %s (%s)\n %s\n %s'
% (self.covariance_type, params, trainll, np.diff(trainll)))
# XXX: Why such a large tolerance?
self.assertTrue(np.all(np.diff(trainll) > -0.5))
def train(self, obs, obs_weights=None, max_N=15):
"""Estimates the prototype from a set of observations.
Parameters
----------
max_N : int
The maximum lenght of the HMM.
"""
if obs_weights is None:
obs_weights = np.ones(len(obs))
else:
obs_weights = np.asarray(obs_weights)
# set the number of states
if self.num_states >= 1.0:
self.N = int(self.num_states)
else:
mean_length = np.mean([each_obs.shape[0] for each_obs in obs])
self.N = min(int(self.num_states * mean_length), max_N)
# transition prob: left-to-right
self.transmat = np.zeros((self.N, self.N))
for i in range(self.N):
self.transmat[i, i] = self.self_transprob
if i + 1 < self.N:
self.transmat[i, i + 1] = self.next_transprob
for j in range(i + 2, self.N):
self.transmat[i, j] = self.skip_transprob
self.transmat = normalize(self.transmat, axis=1)
# state prior prob: left-most only
self.startprob = np.zeros(self.N)
self.startprob[0] = 1.0
self.model = WeightedGaussianHMM(self.N, "diag", self.startprob, self.transmat, algorithm="map", params="mc")
self.num_obs = len(obs)
return self.model.fit(obs, obs_weights=obs_weights)
def test_normalize_3D():
A = rng.rand(2, 2, 2) + 1.0
for axis in range(3):
Anorm = hmm.normalize(A, axis)
assert np.all(np.allclose(Anorm.sum(axis), 1.0))
def test_normalize_3D():
A = rng.rand(2, 2, 2) + 1.0
for axis in range(3):
Anorm = hmm.normalize(A, axis)
assert np.all(np.allclose(Anorm.sum(axis), 1.0))
