def load_mixture_model(data, use_mgrd=True):
if use_mgrd:
mm = ExtendedMGRDMixtureModel.load_from_json({'covars': data['gmm_covars'],
'means': data['gmm_means'],
'weights': data['gmm_weights']})
else:
n_components =len(np.array(data['gmm_weights']))
mm = GaussianMixture(n_components=n_components, covariance_type='full')#weights_init=np.array(data['gmm_weights']),
#reg_covar=np.array(data['gmm_covars']),
#means_init=np.array(data['gmm_means']), covariance_type='full')
mm.weights_ = np.array(data['gmm_weights'])
mm.means_ = np.array(data['gmm_means'])
mm.converged_ = True
mm.covariances_ = np.array(data['gmm_covars'])
mm.precisions_cholesky_ = _compute_precision_cholesky(mm.covariances_, covariance_type='full')
mm.n_dims =len(mm.means_[0])
# if 'gmm_precisions_cholesky' not in data.keys():
write_message_to_log("Initialize scipy GMM", LOG_MODE_DEBUG)
return mm
def _init_gmm_from_json(self, data):
""" Initialize the Gaussian Mixture model.
Parameters
----------
* data: dictionary
\tThe dictionary must contain all parameters for the Gaussian Mixture Model.
"""
n_components = len(np.array(data['gmm_weights']))
gmm = GaussianMixture(n_components=n_components,
covariance_type='full')
gmm.weights_ = np.array(data['gmm_weights'])
gmm.means_ = np.array(data['gmm_means'])
gmm.converged_ = True
gmm.covariances_ = np.array(data['gmm_covars'])
gmm.precisions_cholesky_ = _compute_precision_cholesky(
gmm.covariances_, covariance_type='full')
gmm.n_dims = len(gmm.means_[0])
self.gaussian_mixture_model = gmm
