trainControl: Parameters for HMM-VB training.

In HDclust: Clustering High Dimensional Data with Hidden Markov Model on Variable Blocks

Parameters for HMM-VB training.

Description

This function creates a list with parameters for estimating an HMM-VB, which is used as an argument for hmmvbTrain.

Usage

trainControl(
  ninit0 = 1,
  ninit1 = 0,
  ninit2 = 0,
  epsilon = 1e-04,
  diagCov = FALSE
)

Arguments

ninit0	The number of initializations for default scheme 0, under which the k-means clustering for entire dataset is used to initialize the model.
ninit1	The number of initializations for default scheme 1, under which the k-means clustering for a subset of data is used to initialize the model.
ninit2	The number of initializations for default scheme 2, under which a random subset of data is used as cluster centroids to initialize the model.
epsilon	Stopping criteria for Baum-Welch algorithm. Should be a small number in range (0,1).
diagCov	A logical value indicating whether or not variable block covariance matrices will be diagonal.

Value

The named list with parameters.

See Also

hmmvbTrain

Examples

# setting up multiple initialization schemes
Vb <- vb(1, dim=4, numst=2)
set.seed(12345)
hmmvb <- hmmvbTrain(iris[,1:4], VbStructure=Vb, 
          trControl=trainControl(ninit0=2, ninit1=2, ninit2=2))
show(hmmvb)

# forcing diagonal covariance matrices
Vb <- vb(1, dim=4, numst=2)
set.seed(12345)
hmmvb <- hmmvbTrain(iris[,1:4], VbStructure=Vb, 
          trControl=trainControl(diagCov=TRUE))
show(hmmvb)

HDclust documentation built on Sept. 20, 2024, 5:09 p.m.