simpeg.utils.GaussianMixtureWithNonlinearRelationshipsWithPrior#

class simpeg.utils.GaussianMixtureWithNonlinearRelationshipsWithPrior(gmmref, kappa=0.0, nu=0.0, zeta=0.0, prior_type='semi', cluster_mapping=None, init_params='kmeans', max_iter=100, means_init=None, n_init=10, precisions_init=None, random_state=None, reg_covar=1e-06, tol=0.001, verbose=0, verbose_interval=10, warm_start=False, weights_init=None, update_covariances=True, fixed_membership=None)[source]#

Bases: GaussianMixtureWithPrior

Gaussian mixture class for non-linear relationships with priors.

This class built upon the GaussianMixtureWithPrior, which itself built upon from the WeightedGaussianMixture, built up from the mixture.gaussian_mixture.GaussianMixture class from Scikit-Learn.

In addition to weights samples/observations by the cells volume of the mesh (from WeightedGaussianMixture), and nonlinear relationships for each cluster (from GaussianMixtureWithNonlinearRelationships), this class uses a posterior approach to fit the GMM parameters (from GaussianMixtureWithPrior). It takes prior parameters, passed through WeightedGaussianMixture gmmref. The prior distribution for each parameters (proportions, means, covariances) is defined through a conjugate or semi-conjugate approach (prior_type), to the choice of the user. See Astic & Oldenburg 2019: A framework for petrophysically and geologically guided geophysical inversion (https://doi.org/10.1093/gji/ggz389) for more information.

Disclaimer: this class built upon the GaussianMixture class from Scikit-Learn. New functionalitie are added, as well as modifications to existing functions, to serve the purposes pursued within SimPEG. This use is allowed by the Scikit-Learn licensing (BSD-3-Clause License) and we are grateful for their contributions to the open-source community.

Addtional parameters to provide, compared to GaussianMixtureWithPrior:

Parameters:
cluster_mapping(n_components) list

List of mapping describing a nonlinear relationships between physical properties; one per cluster/unit.

Methods

aic(X)

Akaike information criterion for the current model on the input X.

bic(X)

Bayesian information criterion for the current model on the input X.

compute_clusters_covariances()

Compute the precisions matrices and their Cholesky decomposition.

compute_clusters_precisions()

Compute and set the precisions matrices and their Cholesky decomposition.

fit(X[, y, debug])

Estimate model parameters with the EM algorithm.

fit_predict(X[, y, debug])

Estimate model parameters using X and predict the labels for X.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

order_cluster([outputindex])

Order cluster

order_clusters_GM_weight([outputindex])

Order clusters by decreasing weights

plot_pdf([ax, flag2d, x_component, ...])

Utils to plot the marginal PDFs of a GMM, either in 1D or 2D (1 or 2 physical properties at the time).

predict(X)

Predict the labels for the data samples in X using trained model.

predict_proba(X)

Evaluate the components' density for each sample.

sample([n_samples])

Generate random samples from the fitted Gaussian distribution.

score(X[, y])

Compute the per-sample average log-likelihood

score_samples(X)

Compute the log-likelihood of each sample.

score_samples_with_sensW(X, sensW)

Compute the weighted log probabilities for each sample.

set_fit_request(*[, debug])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

update_gmm_with_priors([debug])

Update GMM with priors