def read_gmm(stream, *_):
v, covariance_type = stream.read_singleton("covariance_type")
v, dimension = stream.read_scalar("dimension", int)
v, num_components = stream.read_scalar("num_components", int)
v, relevances = stream.read_list("relevances", float)
v, gmm_weights = stream.read_array("weights", rtype=float, dim=1, shape=(num_components,))
v, smms = stream.read_indexed_collection(read_smm, (covariance_type, num_components, dimension), name="Gaussians")
gmm_means = numpy.zeros((num_components, dimension), dtype=float)
if covariance_type is GaussianMixtureModel.FULL_COVARIANCE:
var_shape = (num_components, dimension, dimension)
else:
assert covariance_type is GaussianMixtureModel.DIAGONAL_COVARIANCE
var_shape = (num_components, dimension)
gmm_vars = numpy.zeros(var_shape, dtype=float)
assert len(smms) == num_components
for i in xrange(num_components):
gmm_means[i] = smms[i].means
gmm_vars[i] = smms[i].vars
# Construct and return Gmm object
ret = GaussianMixtureModel(dimension, covariance_type, num_components)
ret.set_weights(gmm_weights)
ret.set_means(gmm_means)
ret.set_vars(gmm_vars)
ret.set_relevances(relevances)
return ret
def read_gmm(stream, *_):
v, covariance_type = stream.read_singleton("covariance_type")
v, dimension = stream.read_scalar("dimension", int)
v, num_components = stream.read_scalar("num_components", int)
v, relevances = stream.read_list("relevances", float)
v, gmm_weights = stream.read_array("weights",
rtype=float,
dim=1,
shape=(num_components, ))
v, smms = stream.read_indexed_collection(
read_smm, (covariance_type, num_components, dimension),
name="Gaussians")
gmm_means = numpy.zeros((num_components, dimension), dtype=float)
if covariance_type is GaussianMixtureModel.FULL_COVARIANCE:
var_shape = (num_components, dimension, dimension)
else:
assert (covariance_type is GaussianMixtureModel.DIAGONAL_COVARIANCE)
var_shape = (num_components, dimension)
gmm_vars = numpy.zeros(var_shape, dtype=float)
assert (len(smms) == num_components)
for i in xrange(num_components):
gmm_means[i] = smms[i].means
gmm_vars[i] = smms[i].vars
# Construct and return Gmm object
ret = GaussianMixtureModel(dimension, covariance_type, num_components)
ret.set_weights(gmm_weights)
ret.set_means(gmm_means)
ret.set_vars(gmm_vars)
ret.set_relevances(relevances)
return ret
def make_target(dimension, num_comps, weights, means, vars):
ret = GaussianMixtureModel(dimension,
GaussianModelBase.DIAGONAL_COVARIANCE,
num_comps)
ret.set_weights(numpy.array(weights))
ret.set_means(numpy.array(means))
ret.set_vars(numpy.array(vars))
return ret
def make_target(dimension, num_comps, weights, means, vars):
ret = GaussianMixtureModel(dimension, GaussianModelBase.DIAGONAL_COVARIANCE, num_comps)
ret.set_weights(numpy.array(weights))
ret.set_means(numpy.array(means))
ret.set_vars(numpy.array(vars))
return ret
