Package edu.cmu.tetrad.search.unmix

package edu.cmu.tetrad.search.unmix

Related Packages

Package	Description
edu.cmu.tetrad.search	This package contains classes for causal graph search algorithms.

Class	Description
CausalUnmixer	The CausalUnmixer class provides functionality for extracting unmixed results from datasets using a graph-less approach.
CausalUnmixer.Config	The Config class encapsulates the configuration settings used for the unmixing or clustering process within the CausalUnmixer framework.
EmUnmix	The EmUnmix class provides functionality for applying the Expectation-Maximization (EM) algorithm on residual signatures derived from a dataset to fit Gaussian mixtures.
EmUnmix.Config	Configuration class for the EmUnmix algorithm, providing parameters and settings to control the behavior of the unmixing process.
EmUtils	A utility class for operations commonly used in the Expectation-Maximization (EM) algorithm.
GaussianMixtureEM	Lightweight Gaussian Mixture EM with full or diagonal covariance.
GaussianMixtureEM.Config	Configuration class for the Gaussian Mixture Model (GMM) Expectation-Maximization (EM) algorithm.
GaussianMixtureEM.CovarianceType	Specifies the type of covariance matrix used in the Gaussian Mixture Model (GMM) implemented by the GaussianMixtureEM class.
GaussianMixtureEM.Model	Represents the Gaussian Mixture Model (GMM) computed and used in the GaussianMixtureEM class.
KMeans	Implements the K-Means clustering algorithm using the k-means++ initialization method and iterative refinement.
KMeans.Result	Represents the result of a clustering operation using the KMeans algorithm.
LinearQRRegressor	Linear regression using EJML's QR-based solve (stable).
ParentSupersetBuilder	Builds a parent *superset* per node without assuming a single pooled DAG.
ParentSupersetBuilder.Config	Represents the configuration settings used for statistical screening and optional bagging operations in the parent superset construction process.
ParentSupersetBuilder.ScoreType	Represents the type of correlation score used for statistical calculations or comparisons.
ResidualRegressor	Interface for local regressors used to compute residual signatures: X ~ f(Pa).
ResidualUtils	Utilities for computing residual matrices given a regressor and either (a) a graph with parent sets, or (b) an explicit parent map.
RoadmapTest	RoadmapTest: EM baseline vs Residual-clustering across scenarios.
TestCausalUnmixer	Better-targeted unmixing tests with ground-truth labels + ARI.
TestCausalUnmixer.LabeledData	Represents labeled data consisting of a dataset and corresponding labels.
UnmixDiagnostics	The UnmixDiagnostics class provides utilities for evaluating and diagnosing results from unsupervised mixture models, such as Gaussian Mixture Models and related clustering techniques.
UnmixDiagnostics.BicDelta	Represents the Bayesian Information Criterion (BIC) values for mixture models with K=1 and K=2 clusters and the difference (delta) between these values.
UnmixDiagnostics.EntropyStats	Represents the entropy statistics of clustering results in the context of mixture models.
UnmixDiagnostics.GraphDiff	Represents the difference between two clustering graphs in terms of structural and adjacency metrics.
UnmixDiagnostics.StabilityResult	Represents the result of a stability analysis of clustering, typically performed by evaluating the Adjusted Rand Index (ARI) across multiple independent runs.
UnmixResult	Container for unmixing output (labels, per-cluster datasets, optional graphs).