def copy_fit(bgm, method='bgm'):
n_clusters = bgm.n_components
covartype = bgm.covariance_type
n_init = bgm.n_init
max_iter = bgm.max_iter
tol = bgm.tol
verbose = True
if method == 'bgm':
wcpt = bgm.weight_concentration_prior_type
reg_covar = bgm.reg_covar
init_params = bgm.init_params
tol = bgm.tol
copy = BGM(n_components=n_clusters,
covariance_type=covartype,
n_init=n_init,
weight_concentration_prior_type=wcpt,
init_params=init_params,
max_iter=max_iter,
verbose=verbose,
reg_covar=reg_covar,
tol=tol)
copy.weight_concentration_prior_ = bgm.weight_concentration_prior_
copy.weight_concentration_ = bgm.weight_concentration_
copy.mean_precision_prior = bgm.mean_precision_prior
copy.mean_prior_ = bgm.mean_prior_
copy.mean_precision_ = bgm.mean_precision_
copy.covariance_prior_ = bgm.covariance_prior_
copy.degrees_of_freedom_prior_ = bgm.degrees_of_freedom_prior_
copy.degrees_of_freedom_ = bgm.degrees_of_freedom_
if method == 'gmm':
copy = GMM(n_components=n_clusters,
random_state=42,
covariance_type=covartype,
max_iter=max_iter,
n_init=n_init,
tol=tol,
verbose=verbose)
copy.means_ = bgm.means_
copy.covariances_ = bgm.covariances_
copy.weights_ = bgm.weights_
copy.precisions_ = bgm.precisions_
copy.precisions_cholesky_ = bgm.precisions_cholesky_
copy.converged_ = bgm.converged_
copy.n_iter_ = bgm.n_iter_
copy.lower_bound_ = bgm.lower_bound_
return copy
def fit_dirichlet_gmm_to_points(points,
n_components,
mdl,
ps=[],
num_iter=100,
covariance_type='full',
mass_multiplier=1.0):
"""fit a GMM to some points. Will return core::Gaussians.
if no particles are provided, they will be created
points: list of coordinates (python)
n_components: number of gaussians to create
mdl: IMP Model
ps: list of particles to be decorated. if empty, will add
num_iter: number of EM iterations
covariance_type: covar type for the gaussians. options: 'full', 'diagonal', 'spherical'
init_centers: initial coordinates of the GMM
force_radii: fix the radii (spheres only)
force_weight: fix the weights
mass_multiplier: multiply the weights of all the gaussians by this value
"""
new_sklearn = True
try:
from sklearn.mixture import BayesianGaussianMixture
except ImportError:
from sklearn.mixture import DPGMM
new_sklearn = False
### create and fit GMM
print('using dirichlet prior')
if new_sklearn:
gmm = BayesianGaussianMixture(
weight_concentration_prior_type='dirichlet_process',
n_components=n_components, max_iter=num_iter,
covariance_type=covariance_type)
else:
gmm = DPGMM(n_components=n_components, n_iter=num_iter,
covariance_type=covariance_type)
gmm.fit(points)
#print('>>> GMM score',gmm.score(points))
#print gmm.covars_
#print gmm.weights_
#print gmm.means_
### convert format to core::Gaussian
if not new_sklearn:
gmm.precisions_ = gmm.precs_
for ng in range(n_components):
invcovar=gmm.precisions_[ng]
covar=np.linalg.inv(invcovar)
if covar.size==3:
covar=np.diag(covar).tolist()
else:
covar=covar.tolist()
center=list(gmm.means_[ng])
weight=mass_multiplier*gmm.weights_[ng]
if ng>=len(ps):
ps.append(IMP.Particle(mdl))
shape=IMP.algebra.get_gaussian_from_covariance(covar,IMP.algebra.Vector3D(center))
g=IMP.core.Gaussian.setup_particle(ps[ng],shape)
IMP.atom.Mass.setup_particle(ps[ng],weight)
IMP.core.XYZR.setup_particle(ps[ng],sqrt(max(g.get_variances())))