learnBatch: Train the network (Batch algorithm).

In harrysouthworth/kohonen: Self-organizing Maps for Data Classification

Description

Perform the learning process of the network with Batch algorithm.

Usage

learnBatch( object , number.iter , max.rayon = 3 , min.rayon = 0 )

Arguments

object	a som object
number.iter	number of learning process' step. It won't have effect if order is specified.
max.rayon	the maximum radius during learning process.
min.rayon	the minimum radii during learning process.

Details

Be carefull with number.iter, it should be small. alpha decrease linearly from max.alpha to min.alpha over number.iter updates. rayon decrease linearly from max.rayon to min.rayon over number.iter updates.

Value

a som object.

Author(s)

David Gohel

References

Kohonen, T. (1995). Self-Organizing Maps

See Also

som learn plot.som summary.som predict.som biplot.som getWeights setWeights

Examples

library(MASS)
lcrabs <- log(crabs[, 4:8])

lcrabs.som <- som ( formula = ~ . , data = lcrabs
	, neighborhood = "uniform"
	, grid = grid ( xdim = 10 , ydim = 10 , type = "hexagonal" ) 
	, weights.min = min (lcrabs), weights.max = max (lcrabs)
	)

# train the network
lcrabs.som <- learnBatch( lcrabs.som , number.iter = 10 )
# plot energy function and print a summary
plot( lcrabs.som , "energy")
summary( lcrabs.som )

harrysouthworth/kohonen documentation built on May 17, 2019, 3:03 p.m.