simpeg.utils.WeightedGaussianMixture#

class simpeg.utils.WeightedGaussianMixture(n_components, mesh, actv=None, covariance_type='full', 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)[source]#

Bases: GaussianMixture

Weighted Gaussian mixture class

This class upon the GaussianMixture class from Scikit-Learn. Two main modifications:

1. Each sample/observation is given a weight, the volume of the corresponding discretize.BaseMesh cell, when fitting the Gaussian Mixture Model (GMM). More volume gives more importance, ensuing a mesh-free evaluation of the clusters of the geophysical model.

2. When set manually, the proportions can be set either globally (normal behavior) or cell-by-cell (improvements)

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 sklearn.mixture.gaussian_mixture:

Parameters:

n_componentsint: Number of components
meshdiscretize.BaseMesh: TensorMesh or QuadTree or Octree) mesh: the volume of the cells give each sample/observations its weight in the fitting proces
actvnumpy.ndarry, optional: Active indexes

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])	Estimate model parameters with the EM algorithm.
`fit_predict`(X[, y])	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_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_params`(**params)	Set the parameters of this estimator.

Galleries and Tutorials using `simpeg.utils.WeightedGaussianMixture`#

Petrophysically guided inversion (PGI): Linear example

Petrophysically guided inversion: Joint linear example with nonlinear relationships

Joint PGI of Gravity + Magnetic on an Octree mesh using full petrophysical information

Joint PGI of Gravity + Magnetic on an Octree mesh without petrophysical information

simpeg.utils.WeightedGaussianMixture#

Galleries and Tutorials using simpeg.utils.WeightedGaussianMixture#

Galleries and Tutorials using `simpeg.utils.WeightedGaussianMixture`#