som.nn.continue: Continue hexagonal som training

In som.nn: Topological k-NN Classifier Based on Self-Organising Maps

Continue hexagonal som training

Description

An existing self-organising map with hexagonal tolology is further trained and a model created for prediction of unknown samples. In contrast to a "normal" som, class-labels for all samples of the training set are required to build the model.

Usage

som.nn.continue(
  model,
  x,
  kernel = "internal",
  len = 0,
  alpha = 0.2,
  radius = 0
)

Arguments

model	model of type SOMnn.
x	data.fame with training data. Samples are requested as rows and taken randomly for the training steps. All columns except of the class lables are considered to be attributes and parts of the training vector. x must include the same columns as the data.frame with which the model have been trained originally. One column is needed as class labels. The column with class lables is selected by the slot class.idx of the model.
kernel	Kernel for som training. One of the predefined kernels "bubble" and "gaussian" == train with the R-implementation or "SOM" == train with SOM or "kohonen" == train with som (kohonen::som) or "som" == train with som (som::som). If a function is specified (as closure, not as character) the specified custom function is used for training.
len	number of steps to be trained (steps - not epochs!).
alpha	initial training rate; default 0.02.
radius	inital radius for SOM training. If Gaussian distance function is used, radius corresponds to sigma.

Details

Any specified custom kernel function is used for som training. The function must match the signature kernel(data, grid, rlen, alpha, radius, init, toroidal), with arguments:

• data numeric matrix of training data; one sample per row

• classes: optional charater vector of classes for training data

• grid somgrid, generated with somgrid

• rlen number of training steps

• alpha training rate

• radius training radius

• init numeric matrix of initial codebook vectors; one code per row

• toroidal logical; TRUE, if the topology of grid is toroidal

The returned value must be a list with at minimum one element

• codes: numeric matrix of result codebook vectors; one code per row

Value

    S4 object of type \code{\link{SOMnn}} with the trained model

Examples

## get example data and add class labels:
data(iris)
species <- iris$Species

## train with default radius = diagonal / 2:
rlen <- 500
som <- som.nn.train(iris, class.col = "Species", kernel = "internal",
                    xdim = 15, ydim = 9, alpha = 0.2, len = rlen, 
                    norm = TRUE, toroidal = FALSE)


## continue training with different alpha and radius;
som <- som.nn.continue(som, iris, alpha = 0.02, len=500, radius = 5)
som <- som.nn.continue(som, iris, alpha = 0.02, len=500, radius = 2)

## predict some samples:
unk <- iris[,!(names(iris) %in% "Species")]

setosa <- unk[species=="setosa",]
setosa <- setosa[sample(nrow(setosa), 20),]

versicolor <- unk[species=="versicolor",]
versicolor <- versicolor[sample(nrow(versicolor), 20),]

virginica <- unk[species=="virginica",]
virginica <- virginica[sample(nrow(virginica), 20),]

p <- predict(som, unk)
head(p)

## plot:
plot(som)
dev.off()
plot(som, predict = predict(som, setosa))
plot(som, predict = predict(som, versicolor), add = TRUE, pch.col = "magenta", pch = 17)
plot(som, predict = predict(som, virginica), add = TRUE, pch.col = "white", pch = 8)

som.nn documentation built on May 29, 2024, 6:50 a.m.