RSNNS (version 0.3-1)

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

Description

Create and train a self-organizing map (som).

Usage

som(x, ...)
## S3 method for class 'default':
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,
    ...)

Arguments

x

a matrix with training inputs for the network

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

...

additional function parameters (currently not used)

Value

• som.default: an rsnns object. Depending on which calculation flags are switched on, the som generates some special members:
• map
  the som. For each unit, the amount of patterns where this unit won is given.
• componentMaps
  a map for every input component, showing where in the map this component leads to high activation.
• actMaps
  a list containing for each pattern its activation map, i.e. all unit activations. The actMaps are an intermediary result, from which all other results can be computed. This list can be very long, so normally it won't be saved.
• winnersPerPattern
  a vector where for each pattern the number of the winning unit is given. Also, an intermediary result that normally won't be saved.
• labeledUnits
  a matrix which -- if the targets parameter is given -- contains for each unit (rows) and each class present in the targets (columns), the amount of patterns of the class where the unit has won. From the labeledUnits, the labeledMap can be computed, e.g. by voting of the class labels for the final label of the unit.
• labeledMap
  a labeled som that is computed from labeledUnits using decodeClassLabels.
• spanningTree
  the result of the original SNNS function to calculate the map. For each unit, the last pattern where this unit won is present. As the other results are more informative, the spanning tree is only interesting, if the other functions are too slow or if the original SNNS implementation is needed.

Details

som: This function creates and trains a self-organizing map. As the computation might be slow if many patterns are involved, much of its output is made switchable (see comments on return values).

Examples

demo(som_iris)
demo(som_cubeSnnsR)