def __init__(self, num_phonemes, num_features, var_diag_interval,
var_offdiag_interval):
self.num_features = num_features
self.num_phonemes = num_phonemes
self._labels = tuple("label%d" % i for i in range(num_phonemes))
# Randomly choose num_phonemes vectors
# KJB - need something more sophisticated here to ensure minimum
# distance between phonemes
self._targets = tuple(tuple(random() for i in range(num_features))
for j in range(num_phonemes))
def buildOneCovarMatrix():
uni = (uniform(var_offdiag_interval[0], var_offdiag_interval[1])
for i in xrange(num_features**2))
mat = numpy.fromiter(uni, 'float64').reshape(num_features, num_features)
# Make symetric
lower_tri = numpy.tril(mat) # lower triangular
mat = lower_tri + lower_tri.transpose()
# Now clobber diagonal with other values
for i in range(num_features):
mat[i,i] = uniform(var_diag_interval[0], var_diag_interval[1])
return mat
# Build covar matrices
covars = tuple(buildOneCovarMatrix() for i in range(num_phonemes))
# Create a dictionary from _labels to SimpleGaussianModels
self._models = dict()
for i,label in enumerate(self._labels):
m = SimpleGaussianModel(num_features, SimpleGaussianModel.FULL_COVARIANCE)
m.set_model(self._targets[i], covars[i])
self._models[label] = m
def test8_helper(num_obs, num_passes):
"""
This tests mimics a run ChrisW did with HTK. The models are 2-D single-mode Gaussians embedded
in a 3-state Hmm. Each observation is a sequence of length 11, taken by sampling 2, 3, and 6
times, respectively, from three target distributions. This is identical to test5 except that
here I have built the Hmm with only one Gmm, which is shared by all three states.
"""
import pprint
num_states = 3
dimension = 2
# Data generator setup
target_means = ((1,1), (2,2), (3,3))
target_vars = ((0.1,0.1), (0.2,0.2), (0.3,0.3))
target_durations = (2, 3, 6)
num_steps = sum(target_durations)
generators = [SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE) for i in xrange(num_states)]
[m.set_model(tm, tv) for (m, tm, tv) in izip(generators, target_means, target_vars)]
SimpleGaussianModel.seed(0)
# Gmm setup
num_states = 3
gmm = GaussianMixtureModel(dimension, GaussianMixtureModel.DIAGONAL_COVARIANCE, 1)
gmm.set_weights(array((1.0,)))
mu = array(((0.0,0.0),))
v = array(((1.0,1.0),))
gmm.set_model(mu, v)
models = (gmm,)
mm = GmmMgr(models)
# Here's where we're using the same Gmm in all three states of this Hmm.
models = (0, 0, 0)
# Hmm setup
trans = array(((0.0, 1.0, 0.0, 0.0, 0.0),
(0.0, 0.5, 0.5, 0.0, 0.0),
(0.0, 0.0, 0.5, 0.5, 0.0),
(0.0, 0.0, 0.0, 0.5, 0.5),
(0.0, 0.0, 0.0, 0.0, 0.0)))
hmm0 = Hmm(num_states, log_domain=True)
hmm0.build_model(mm, models, 1, 1, trans)
print hmm0.to_string(True)
for p in xrange(num_passes):
# Reseeding here ensures we are repeating the same observations in each pass
SimpleGaussianModel.seed(0)
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
obs_gen = obs_generator(generators, target_durations)
for i in xrange(num_obs):
obs = obs_gen.next()
hmm0.adapt_one_sequence(obs)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
print hmm0.to_string(True)
def main(args):
if not args:
return
with open(args[0], 'rt') as infile:
SimpleGaussianModel.seed(0)
for sample in text_utils.generate_samples(text_utils.make_model_samplers((0, 1), (1, 0.75)), text_utils.deterministic_labels(infile)):
print sample, 'X' if sample[0][0] == sample[-1][-1] else ''
def main(args):
if not args:
return
with open(args[0], 'rt') as infile:
SimpleGaussianModel.seed(0)
for sample in text_utils.generate_samples(
text_utils.make_model_samplers((0, 1), (1, 0.75)),
text_utils.deterministic_labels(infile)):
print sample, 'X' if sample[0][0] == sample[-1][-1] else ''
def test6():
"""
This test builds an Hmm with dummy models which always give a score of 1, but
with a somewhat unusual topology in which there are 6 actual states chained together
with 2 virtual inputs and 3 virtual outputs. The point is to make sure we can handle
this asymetric case correctly.
"""
import pprint
num_states = 6
dimension = 2
# GmmMgr setup
models = []
for i in xrange(num_states):
dm = DummyModel(dimension, 1.0)
models.append(dm)
mm = GmmMgr(models)
models = range(num_states)
# Hmm setup T0: i1 i2 1 2 3 4 5 6 o1 o2 o3 FROM:
trans = array(((0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # i1
(0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # i2
(0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 1
(0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 2
(0.0, 0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0), # 3
(0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.4, 0.0, 0.1, 0.0, 0.0), # 4
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.4, 0.0, 0.1, 0.0), # 5
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.5), # 6
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # o1
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # o2
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0))) # o3
hmm0 = Hmm(num_states, log_domain=True)
hmm0.build_model(mm, models, 2, 3, trans)
print hmm0.to_string(True)
num_passes = 1
for p in xrange(num_passes):
# Reseeding here ensures we are repeating the same observations in each pass
SimpleGaussianModel.seed(0)
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
obs = [array((0,0))] * 11 # Dummy sequence of length 11
hmm0.adapt_one_sequence(obs)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
print hmm0.to_string(True)
def __init__(self, primary_classifier, variant_labels):
self._variant_labels = variant_labels
self._variant_models = {}
for label in variant_labels:
model = SimpleGaussianModel()
# KJB - this guy shouldn't have to know so much about models -
# maybe do some kind of cloning from models in the classifier?
model.set_model(primary_classifier.num_features, 'diagonal')
self._variant_models[label] = model
self._primary = primary_classifier
self._variant_classifier = None
def __init__(self, primary_classifier, variant_labels):
self._variant_labels = variant_labels
self._variant_models = {}
for label in variant_labels:
model = SimpleGaussianModel()
# KJB - this guy shouldn't have to know so much about models -
# maybe do some kind of cloning from models in the classifier?
model.set_model(primary_classifier.num_features, 'diagonal')
self._variant_models[label] = model
self._primary = primary_classifier
self._variant_classifier = None
def read_smm(stream, params, _):
covariance_type, num_components, dimension = params
v, smm_means = stream.read_array("means", rtype=float, dim=1, shape=(dimension,))
if covariance_type is GaussianMixtureModel.FULL_COVARIANCE:
var_dim = 2
var_shape = (dimension, dimension)
else:
assert covariance_type is GaussianMixtureModel.DIAGONAL_COVARIANCE
var_dim = 1
var_shape = (dimension,)
v, smm_vars = stream.read_array("vars", rtype=float, dim=var_dim, shape=var_shape)
ret = SimpleGaussianModel(dimension, covariance_type)
ret.set_model(smm_means, smm_vars)
return ret
def test9(num_obs):
"""
Test sequence scoring interface.
"""
num_states = 3
dimension = 2
# Data generator setup
target_means = ((1,1), (2,2), (3,3))
target_vars = ((0.1,0.1), (0.2,0.2), (0.3,0.3))
target_durations = (2, 3, 6)
num_steps = sum(target_durations)
generators = [SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE) for i in xrange(num_states)]
[m.set_model(tm, tv) for (m, tm, tv) in izip(generators, target_means, target_vars)]
SimpleGaussianModel.seed(0)
obs_gen = obs_generator(generators, target_durations)
# Gmm setup
num_states = 3
models = []
for i in xrange(num_states):
gmm = GaussianMixtureModel(dimension, GaussianMixtureModel.DIAGONAL_COVARIANCE, 1)
gmm.set_weights(array((1.0,)))
mu = array(((0.0,0.0),))
v = array(((1.0,1.0),))
gmm.set_model(mu, v)
models.append(gmm)
mm = GmmMgr(models)
models = range(num_states)
# Hmm setup
trans = array(((0.0, 1.0, 0.0, 0.0, 0.0),
(0.0, 0.5, 0.5, 0.0, 0.0),
(0.0, 0.0, 0.5, 0.5, 0.0),
(0.0, 0.0, 0.0, 0.5, 0.5),
(0.0, 0.0, 0.0, 0.0, 0.0)))
hmm0 = Hmm(num_states)
hmm0.build_model(mm, models, 1, 1, trans)
print hmm0.to_string(full=True)
for i in xrange(num_obs):
obs = obs_gen.next()
scores = hmm0.forward_score_sequence(obs)
print scores
def make_data_generator(dimension):
# Data generator setup and data generation
target_means = ((1, 1), (2, 2), (3, 3))
target_vars = ((0.1, 0.1), (0.2, 0.2), (0.3, 0.3))
target_durations = (2, 6, 6)
assert len(target_means) == len(target_vars) == len(target_durations)
num_generators = len(target_means)
generators = [
SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE) for i in xrange(num_generators)
]
for (m, tm, tv) in izip(generators, target_means, target_vars):
m.set_model(tm, tv)
SimpleGaussianModel.seed(0)
def obs_generator():
while True:
yield [m.sample() for m, d in izip(generators, target_durations) for i in xrange(d)]
return obs_generator()
def read_smm(stream, params, _):
covariance_type, num_components, dimension = params
v, smm_means = stream.read_array("means",
rtype=float,
dim=1,
shape=(dimension, ))
if covariance_type is GaussianMixtureModel.FULL_COVARIANCE:
var_dim = 2
var_shape = (dimension, dimension)
else:
assert (covariance_type is GaussianMixtureModel.DIAGONAL_COVARIANCE)
var_dim = 1
var_shape = (dimension, )
v, smm_vars = stream.read_array("vars",
rtype=float,
dim=var_dim,
shape=var_shape)
ret = SimpleGaussianModel(dimension, covariance_type)
ret.set_model(smm_means, smm_vars)
return ret
def make_data_generator(dimension):
# Data generator setup and data generation
target_means = ((1, 1), (2, 2), (3, 3))
target_vars = ((0.1, 0.1), (0.2, 0.2), (0.3, 0.3))
target_durations = (2, 6, 6)
assert len(target_means) == len(target_vars) == len(target_durations)
num_generators = len(target_means)
generators = [
SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE)
for i in xrange(num_generators)
]
for (m, tm, tv) in izip(generators, target_means, target_vars):
m.set_model(tm, tv)
SimpleGaussianModel.seed(0)
def obs_generator():
while True:
yield [
m.sample() for m, d in izip(generators, target_durations)
for i in xrange(d)
]
return obs_generator()
def make_model_samplers(means, vars):
"""
>>> SimpleGaussianModel.seed(0)
>>> tuple(int(128 * sampler()) for sampler in text_utils.make_model_samplers((0, 1), (1, 0.5)))
(-23, 130)
>>> tuple(int(128 * sampler()) for sampler in text_utils.make_model_samplers((0, 1, 2), (1, 0.5, 2)))
(89, 119, 511)
"""
assert len(means) == len(vars)
num_classes = len(means)
models = tuple(
SimpleGaussianModel(1, SimpleGaussianModel.DIAGONAL_COVARIANCE)
for i in xrange(num_classes))
for model, mean, var in izip(models, means, vars):
model.set_model((mean, ), (var, ))
samplers = tuple(model.sample for model in models)
assert len(samplers) == num_classes
return samplers
def test2a(num_obs, num_passes):
dimension = 2
# Data generator setup
target_means = (1,1)
target_vars = (0.1,0.1)
generator = SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE)
generator.set_model(target_means, target_vars)
SimpleGaussianModel.seed(0)
data = [generator.sample() for i in xrange(num_obs)]
# Gmm setup
num_mixtures = 2
gmm0 = make_gmm_diag(dimension, num_mixtures)
gmm1 = make_gmm_diag(dimension, num_mixtures)
mm = GmmMgr((gmm1,))
# Hmm setup
# A transition probability matrix with a p ~= 1 self-loop for the real state.
# The entry state feeds into the real state with p=1. We use p ~= 1 for the
# second self loop since we need *some* probability of finishing.
trans = array(((0.0, 1.0, 0.0),
(0.0, 0.999999999999, 0.000000000001),
(0.0, 0.0, 0.0)))
hmm0 = Hmm(1, log_domain=True)
hmm0.build_model(mm, (0,), 1, 1, trans)
print hmm0.to_string(True) + '\n'
print gmm0
print '\n\n'
# Try some adaptation
for p in xrange(num_passes):
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
hmm0.adapt_one_sequence(data)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
really_print = False
with DebugPrint("gaussian", "gaussian_pt", "gaussian_gmm_score") if really_print else DebugPrint():
gmm0.adapt(data, max_iters = num_passes)
print hmm0.to_string(True) + '\n'
print gmm0
def test1(num_obs, num_passes):
dimension = 2
# Data generator setup
target_means = (1,1)
target_vars = (0.1,0.1)
generator = SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE)
generator.set_model(target_means, target_vars)
SimpleGaussianModel.seed(0)
GaussianMixtureModel.seed(0)
# Gmm setup
num_mixtures = 2
gmm0 = make_gmm(dimension, num_mixtures)
gmm1 = make_gmm(dimension, num_mixtures)
mm = GmmMgr((gmm1,))
# Hmm setup
hmm0 = Hmm(1, log_domain=True)
# A transition probability matrix with a p=1/2 exit for the real state.
# The entry state feeds into the real state with p=1.
trans = array(((0.0, 1.0, 0.0),
(0.0, 0.5, 0.5),
(0.0, 0.0, 0.0)))
hmm0.build_model(mm, (0,), 1, 1, trans)
print hmm0.to_string(True)
print gmm0
# Try some adaptation. Note that we are feeding the entire data set as one stream
# to the Hmm adaption call.
data = [generator.sample() for i in xrange(num_obs)]
for p in xrange(num_passes):
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
hmm0.adapt_one_sequence(data)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
gmm0.adapt(data, max_iters = num_passes)
print hmm0.to_string(True)
print gmm0
def test0(num_obs, num_passes):
dimension = 2
# Data generator setup
target_means = (1,1)
target_vars = (0.1,0.1)
generator = SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE)
generator.set_model(target_means, target_vars)
SimpleGaussianModel.seed(0)
GaussianMixtureModel.seed(0)
mm = GmmMgr(dimension)
# Hmm setup
hmm0 = Hmm(0, log_domain=True)
# A transition probability matrix with no real state.
# The entry state feeds into the exit state with p=1.
trans = array(((0.0, 1.0),
(0.0, 0.0)))
hmm0.build_model(mm, (), 1, 1, trans)
print hmm0.to_string(True)
# Try some adaptation. Note that we are feeding the entire data set as one stream
# to the Hmm adaption call.
data = [generator.sample() for i in xrange(num_obs)]
for p in xrange(num_passes):
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
with DebugPrint("hmm_gxfs", "hmm_aos") if False else DebugPrint():
hmm0.adapt_one_sequence(data)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
print hmm0.to_string(True)
def __init__(self, labels, dimension):
super(SimpleClassifier, self).__init__(labels, dimension)
for label in labels:
m = SimpleGaussianModel(dimension,
SimpleGaussianModel.DIAGONAL_COVARIANCE)
self._models[label] = m
def test7():
"""
This test builds an Hmm with dummy models which always give a score of 1, but
with a somewhat unusual topology in which there are 6 actual states chained together
with 2 virtual inputs and 3 virtual outputs. The point is to make sure we can handle
this asymetric case correctly. This is the same as test6 except that now we'll use
the network adaptation interface instead.
"""
import pprint
num_states = 6
dimension = 2
# GmmMgr setup
models = []
for i in xrange(num_states):
dm = DummyModel(dimension, 1.0)
models.append(dm)
mm = GmmMgr(models)
# Hmm setup T0: i1 i2 1 2 3 4 5 6 o1 o2 o3 FROM:
trans = array(((0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # i1
(0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # i2
(0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 1
(0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 2
(0.0, 0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0), # 3
(0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.4, 0.0, 0.1, 0.0, 0.0), # 4
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.4, 0.0, 0.1, 0.0), # 5
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.5), # 6
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # o1
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # o2
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0))) # o3
hmm0 = Hmm(num_states, log_domain=True)
models = range(num_states)
hmm0.build_model(mm, models, 2, 3, trans)
print hmm0.to_string(True)
num_passes = 1
for p in xrange(num_passes):
# Reseeding here ensures we are repeating the same observations in each pass
SimpleGaussianModel.seed(0)
mm.set_adaptation_state("INITIALIZING")
hmm0.begin_adapt("NETWORK")
mm.set_adaptation_state("ACCUMULATING")
num_obs = 11
obs = [array((0,0))] * num_obs # Dummy sequence
context = hmm0.init_for_forward_pass(obs, terminal = True)
# Add some mass into the system for the forward pass. To match the behavior of
# standalone adaptation, we divide an initial mass of 1 evenly across the inputs
hmm0.accum_input_alphas(context, array([1.0/hmm0.num_inputs] * hmm0.num_inputs))
# Actually do the forward pass. Note that we must process one more frame than the number of
# observations - this is because an extra frame is automatically added which scores 1 on the exit
# states of the Hmm (and 0 on all real states). XXX we might want clients to do this for
# themselves at some point rather than this automatic behavior:
for frame in xrange(num_obs + 1):
output_alphas = hmm0.process_one_frame_forward(context)
print output_alphas
# Likewise, we initialize and then make the backward pass:
hmm0.init_for_backward_pass(context)
hmm0.accum_input_betas(context, array([1.0] * hmm0.num_outputs))
for frame in xrange(num_obs + 1):
output_betas = hmm0.process_one_frame_backward(context)
print output_betas
# Now collect all the gamma sums; here there's only one:
norm = hmm0.get_initial_gamma_sum()
hmm0.add_to_gamma_sum(norm, context)
# Here's where the actual accumulation happens:
hmm0.do_accumulation(context, norm)
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
print hmm0.to_string(True)
labels = True, False ncomps = 7, 3 nfeatures = numceps # mfcc, with c0: gathered from about 60,000 frames of 'train' data from Ken and Hugh skyping true_means_prime = N.array((-5.5522, -1.0517, 1.0240, 3.1609, 0.5628, -122.5789), dtype=N.float32) true_vars_prime = N.array((26.2238, 26.4064, 59.7242, 35.1180, 47.2471, 3967.2240), dtype=N.float32) false_means_prime = N.array((-9.4634, -5.3991, -4.2773, 1.7494, -0.0822, -228.6211), dtype=N.float32) false_vars_prime = N.array((3.0097, 6.0277, 8.3711, 10.7198, 13.4285, 456.7074), dtype=N.float32) # mfcc, no c0: 20,000 frames of Hugh talking true_means_prime = N.array((-4.8087, 3.9863, -0.5217, 1.3076, 0.7514, -4.6497), dtype=N.float32) true_vars_prime = N.array((26.8496, 32.6631, 32.3662, 24.2963, 36.2244, 34.1555), dtype=N.float32) false_means_prime = N.array((-6.8806, -1.3424, -3.8147, 0.4520, 0.7129, -3.1560), dtype=N.float32) false_vars_prime = N.array((2.7468, 6.2286, 7.4355, 10.1530, 13.3865, 15.9309), dtype=N.float32) true_prime = SimpleGaussianModel(nfeatures, GaussianModelBase.DIAGONAL_COVARIANCE) true_prime.set_model(true_means_prime, true_vars_prime) false_prime = SimpleGaussianModel(nfeatures, GaussianModelBase.DIAGONAL_COVARIANCE) false_prime.set_model(false_means_prime, false_vars_prime) primer = (true_prime, false_prime) GaussianMixtureModel.seed(0) gmm_mgr0 = GmmMgr(ncomps, nfeatures, GaussianModelBase.DIAGONAL_COVARIANCE, primer) gmm_mgr1 = GmmMgr(ncomps, nfeatures, GaussianModelBase.DIAGONAL_COVARIANCE, primer) classify0 = AdaptingGmmClassifier(gmm_mgr0, izip(labels, count())) classify1 = AdaptingGmmClassifier(gmm_mgr1, izip(labels, count())) classify0.set_relevance(333) classify1.set_relevance(333) classify0.set_num_em_iterations(2) classify1.set_num_em_iterations(2)
def test5_helper(num_obs, num_passes):
"""
This tests mimics a run ChrisW did with HTK. The models are 2-D single-mode Gaussians
embedded in a 3-state Hmm. Each observation is a sequence of length 11, taken by sampling
2, 3, and 6 times, respectively, from three target distributions.
"""
import pprint
num_states = 3
dimension = 2
# Data generator setup
target_means = ((1,1), (2,2), (3,3))
target_vars = ((0.1,0.1), (0.2,0.2), (0.3,0.3))
target_durations = (2, 3, 6)
num_steps = sum(target_durations)
generators = [SimpleGaussianModel(dimension, SimpleGaussianModel.DIAGONAL_COVARIANCE) for i in xrange(num_states)]
[m.set_model(tm, tv) for (m, tm, tv) in izip(generators, target_means, target_vars)]
SimpleGaussianModel.seed(0)
# Gmm setup
num_states = 3
models = []
for i in xrange(num_states):
gmm = GaussianMixtureModel(dimension, GaussianMixtureModel.DIAGONAL_COVARIANCE, 1)
gmm.set_weights(array((1.0,)))
mu = array(((0.0,0.0),))
v = array(((1.0,1.0),))
gmm.set_model(mu, v)
models.append(gmm)
mm = GmmMgr(models)
models = range(num_states)
# Hmm setup
trans = array(((0.0, 1.0, 0.0, 0.0, 0.0),
(0.0, 0.5, 0.5, 0.0, 0.0),
(0.0, 0.0, 0.5, 0.5, 0.0),
(0.0, 0.0, 0.0, 0.5, 0.5),
(0.0, 0.0, 0.0, 0.0, 0.0)))
hmm0 = Hmm(num_states, log_domain=True)
hmm0.build_model(mm, models, 1, 1, trans)
print hmm0.to_string(True)
for p in xrange(num_passes):
# Reseeding here ensures we are repeating the same observations in each pass
SimpleGaussianModel.seed(0)
mm.set_adaptation_state("INITIALIZING")
mm.clear_all_accumulators()
hmm0.begin_adapt("STANDALONE")
mm.set_adaptation_state("ACCUMULATING")
obs_gen = obs_generator(generators, target_durations)
for i in xrange(num_obs):
obs = obs_gen.next()
hmm0.adapt_one_sequence(obs)
obs2 = [tuple(a) for a in obs]
# Uncomment these lines to show observations as nicely formatted sequences; this
# is what I gave ChrisW to use with his HTK runs.
# pprint.pprint(obs2)
# print
mm.set_adaptation_state("APPLYING")
hmm0.end_adapt()
mm.apply_all_accumulators()
mm.set_adaptation_state("NOT_ADAPTING")
print hmm0.to_string(True)
def __init__(self, labels, nfeatures):
self.labels = list(labels)
self.nfeatures = nfeatures
self.models = tuple(SimpleGaussianModel(nfeatures, GaussianModelBase.DIAGONAL_COVARIANCE) for _ in self.labels)
self.samples_seen = [0] * len(self.models)
