def get_3d_grid_gmm(subdivisions: Tuple[int, int, int] = (5, 5, 5),
variance: float = 0.04) -> GaussianMixture:
"""
Compute the weight, mean and covariance of a gmm placed on a 3D grid
:param subdivisions: 2 element list of number of subdivisions of the 3D space in each axes to form the grid
:param variance: scalar for spherical gmm.p
:return gmm: gmm: instance of sklearn GaussianMixture (GMM) object Gauassian mixture model
"""
n_gaussians = np.prod(np.array(subdivisions))
step = [
1.0 / (subdivisions[0]), 1.0 / (subdivisions[1]),
1.0 / (subdivisions[2])
]
means = np.mgrid[step[0] - 1:1.0 - step[0]:complex(0, subdivisions[0]),
step[1] - 1:1.0 - step[1]:complex(0, subdivisions[1]),
step[2] - 1:1.0 - step[2]:complex(0, subdivisions[2])]
means = np.reshape(means, [3, -1]).T
covariances = variance * np.ones_like(means)
weights = (1.0 / n_gaussians) * np.ones(n_gaussians)
gmm = GaussianMixture(n_components=n_gaussians, covariance_type='diag')
gmm.weights_ = weights
gmm.covariances_ = covariances
gmm.means_ = means
gmm.precisions_cholesky_ = _compute_precision_cholesky(covariances, 'diag')
return gmm
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
