def _create_custom_gmm(self, means, covs, weigths):
n_comps = len(means)
gmm = BayesianGaussianMixture(n_comps)
gmm.means_ = np.array(means).reshape(-1, 1)
gmm.covariances_ = np.array(covs).reshape(-1, 1, 1)
gmm.weights_ = np.array(weigths)
gmm._check_is_fitted = lambda: True
return gmm
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
