RSNNS (version 0.4-9)

som: Create and train a self-organizing map (SOM)

Description

This function creates and trains a self-organizing map (SOM). SOMs are neural networks with one hidden layer. The network structure is similar to LVQ, but the method is unsupervised and uses a notion of neighborhood between the units. The general idea is that the map develops by itself a notion of similarity among the input and represents this as spatial nearness on the map. Every hidden unit represents a prototype. The goal of learning is to distribute the prototypes in the feature space such that the (probability density of the) input is represented well. SOMs are usually built with 1d, 2d quadratic, 2d hexagonal, or 3d neighborhood, so that they can be visualized straightforwardly. The SOM implemented in SNNS has a 2d quadratic neighborhood. As the computation of this function might be slow if many patterns are involved, much of its output is made switchable (see comments on return values).

Usage

som(x, ...)
"som"(x, mapX = 16, mapY = 16, maxit = 100, initFuncParams = c(1, -1), learnFuncParams = c(0.5, mapX/2, 0.8, 0.8, mapX), updateFuncParams = c(0, 0, 1), shufflePatterns = TRUE, calculateMap = TRUE, calculateActMaps = FALSE, calculateSpanningTree = FALSE, saveWinnersPerPattern = FALSE, targets = NULL, ...)

Arguments

x

a matrix with training inputs for the network

...

additional function parameters (currently not used)

mapX

the x dimension of the som

mapY

the y dimension of the som

maxit

maximum of iterations to learn

initFuncParams

the parameters for the initialization function

learnFuncParams

the parameters for the learning function

updateFuncParams

the parameters for the update function

shufflePatterns

should the patterns be shuffled?

calculateMap

should the som be calculated?

calculateActMaps

should the activation maps be calculated?

calculateSpanningTree

should the SNNS kernel algorithm for generating a spanning tree be applied?

saveWinnersPerPattern

should a list with the winners for every pattern be saved?

targets

optional target classes of the patterns

Value

an rsnns object. Depending on which calculation flags are switched on, the som generates some special members: switched on, the som generates some special members:

Details

Internally, this function uses the initialization function Kohonen_Weights_v3.2, the learning function Kohonen, and the update function Kohonen_Order of SNNS.

References

Kohonen, T. (1988), Self-organization and associative memory, Vol. 8, Springer-Verlag.

Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of Tübingen. http://www.ra.cs.uni-tuebingen.de/SNNS/

Zell, A. (1994), Simulation Neuronaler Netze, Addison-Wesley. (in German)

Examples

## Not run: demo(som_iris)
## Not run: demo(som_cubeSnnsR)


data(iris)
inputs <- normalizeData(iris[,1:4], "norm")

model <- som(inputs, mapX=16, mapY=16, maxit=500,  
                calculateActMaps=TRUE, targets=iris[,5])

par(mfrow=c(3,3))
for(i in 1:ncol(inputs)) plotActMap(model$componentMaps[[i]], 
                                       col=rev(topo.colors(12)))

plotActMap(model$map, col=rev(heat.colors(12)))
plotActMap(log(model$map+1), col=rev(heat.colors(12)))
persp(1:model$archParams$mapX, 1:model$archParams$mapY, log(model$map+1), 
     theta = 30, phi = 30, expand = 0.5, col = "lightblue")

plotActMap(model$labeledMap)

model$componentMaps
model$labeledUnits
model$map

names(model)