vimixUniGauss: Variational Bayesian inference for unsupervised clustering,...

In acabassi/vimix: Variational Inference for MIXture models

Description

Variational Bayesian inference for unsupervised clustering, mixture of univariate Gaussians

Usage

vimixUniGauss(X, K, prior, init = "kmeans", tol = 1e-19,
  maxiter = 2000, verbose = F)

Arguments

X	NxD data matrix.
K	(Maximum) number of clusters.
prior	Prior parameters (optional).
init	Initialisation method (optional). If it is a vector, it is interpreted as the vector of initial cluster allocations. If it is a string, it is interpreted as the name of the clustering algorithm used for the initialisation (only "kmeans" and "random") available at the moment).
tol	Tolerance on lower bound. Default is 10e-20.
maxiter	Maximum number of iterations of the VB algorithm. Default is 2000.
verbose	Boolean flag which, if TRUE, prints the iteration numbers. Default is FALSE.

Value

A list containing L, the lower bound at each step of the algorithm, label, a vector containing the cluster labels, model, a list containing the trained model structure.

Author(s)

Alessandra Cabassi [e-mail]

References

Bishop, C.M., 2006. Pattern recognition and machine learning. Springer.

Examples

data <- c(rnorm(100, -4), rnorm(100, 4))
output <- vimixUniGauss(data, 3)

acabassi/vimix documentation built on May 15, 2019, 10:36 p.m.