simpeg.utils.GaussianMixtureWithNonlinearRelationships#
- class simpeg.utils.GaussianMixtureWithNonlinearRelationships(mesh, n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10, cluster_mapping=None)[source]#
Bases:
WeightedGaussianMixture
Gaussian mixture class for non-linear relationships.
This class built upon the WeightedGaussianMixture, which itself built upon from the mixture.gaussian_mixture.GaussianMixture class from Scikit-Learn.
In addition to weights samples/observations by the cells volume of the mesh, this class can be given specified nonlinear relationships between physical properties. (polynomial etc.) Those nonlinear relationships are given in the form of a list of mapping (cluster_mapping argument). Functions are added and modified to fill that purpose, in particular the fit and samples functions.
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 WeightedGaussianMixture:
- Parameters:
- cluster_mapping(
n_components
)list
List of mapping describing a nonlinear relationships between physical properties; one per cluster/unit.
- cluster_mapping(
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 the precisions matrices and their Cholesky decomposition.
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 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.
Evaluate the components' density for each sample.
sample
([n_samples])[modified from Scikit-Learn.mixture.gaussian_mixture] Generate random samples from the fitted Gaussian distribution with nonlinear relationships.
score
(X[, y])Compute the per-sample average log-likelihood
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.GaussianMixtureWithNonlinearRelationships
#
Petrophysically guided inversion: Joint linear example with nonlinear relationships