som.nn.continue: Continue hexagonal som training
In som.nn: Topological k-NN Classifier Based on Self-Organising Maps

Description Usage Arguments Details Value Examples

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.

1 2	som.nn.continue(model, x, kernel = "internal", len = 0, alpha = 0.2, radius = 0)

`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 `"internal"` == 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.

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

S4 object of type SOMnn with the trained model

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

## train with default radius = diagonal / 2:
som <- som.nn.train(iris, class.col = "Species", kernel = "internal",
                    xdim = 15, ydim = 9, alpha = 0.2, len = 10000, 
                    norm = TRUE, toroidal = FALSE)


## continue training with different alpha and radius;
som <- som.nn.continue(som, iris, alpha = 0.02, len=1000, radius = 5)
som <- som.nn.continue(som, iris, alpha = 0.02, len=1000, 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 = som@predict(setosa))
plot(som, predict = som@predict(versicolor), add = TRUE, pch.col = "magenta", pch = 17)
plot(som, predict = som@predict(virginica), add = TRUE, pch.col = "white", pch = 8)