Uses of Class
edu.cmu.tetrad.search.unmix.EmUnmix.Config

Packages that use EmUnmix.Config

Package	Description
edu.cmu.tetrad.search.unmix

Uses of EmUnmix.Config in edu.cmu.tetrad.search.unmix

Methods in edu.cmu.tetrad.search.unmix that return EmUnmix.Config

Modifier and Type	Method	Description
EmUnmix.Config	EmUnmix.Config.copy()	Creates a copy of this configuration object.

Methods in edu.cmu.tetrad.search.unmix with parameters of type EmUnmix.Config

Modifier and Type	Method	Description
static UnmixDiagnostics.BicDelta	UnmixDiagnostics.computeBicDeltaK1K2(DataSet data, EmUnmix.Config baseCfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster)	Computes the Bayesian Information Criterion (BIC) for mixture models with K=1 and K=2 clusters, and calculates the BIC difference (delta) between them.
static UnmixResult	EmUnmix.run(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor)	Runs the unmixing process on the provided dataset using the specified configuration and regressor.
static UnmixResult	EmUnmix.run(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch)	Executes the unmixing process on the given dataset using the specified configuration, residual regressor, and optional graph search functions.
static UnmixResult	EmUnmix.selectK(DataSet data, int Kmin, int Kmax, ResidualRegressor regressor, EmUnmix.Config base)	Selects the optimal number of clusters (K) for unmixing the provided dataset within the specified range.
static UnmixResult	EmUnmix.selectK(DataSet data, int Kmin, int Kmax, ResidualRegressor regressor, Function<DataSet,Graph> pooledSearch, Function<DataSet,Graph> perClusterSearch, EmUnmix.Config base)	Selects the optimal number of clusters (K) for unmixing a dataset within the specified range [Kmin, Kmax].
static UnmixDiagnostics.StabilityResult	UnmixDiagnostics.stabilityAcrossRestarts(DataSet data, EmUnmix.Config cfg, ResidualRegressor regressor, Function<DataSet,Graph> pooled, Function<DataSet,Graph> perCluster, int repeats, long seedBase)	Evaluates the stability of clustering results across multiple independent runs of the EM algorithm by calculating the average Adjusted Rand Index (ARI) and its standard deviation between all pairs of runs.