initialize-methods: Constructor of SOMnn Class

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

Description

The constructor creates a new object of type SOMnn.

Usage

## S4 method for signature 'SOMnn'
initialize(.Object, name, codes, qerror, class.idx, classes,
  class.counts, class.freqs, confusion, measures, predict, xdim, ydim,
  len.total, toroidal, norm, norm.center, norm.scale, dist.fun, max.dist)

Arguments

.Object	SOMnn object
name	optional name of the model.
codes	data.frame with codebook vectors of the som.
qerror	sum of the mapping errors of the training data.
class.idx	numeric index of column with categories.
classes	character vector with names of categories.
class.counts	data.frame with class hits for each neuron.
class.freqs	data.frame with class frequencies for each neuron (freqs sum up to 1).
confusion	data.frame with confusion matrix for training data.
measures	data.frame with classes as rows and the columns sensitivity, specificity and accuracy for each class.
predict	predict function to be used to predict unknowm samples x. Can be called as object@predict(x).
xdim	number of neurons in x-direction of the som.
ydim	number of neurons in y-direction of the som.
len.total	total number of training steps, performed to create the model.
toroidal	logical; if TRUE, the map is toroidal (i.e. borderless).
norm	logical; if TRUE, data is normalised before training and mapping. Parameters for normalisation of training data is stored in the model and applied before mapping of test data.
norm.center	vector of centers for each column of training data.
norm.scale	vector of scale factors for each column of training data.
dist.fun	function; kernel for the kNN classifier.
max.dist	maximum distance σ for the kNN classifier.

Details

The constructor needs not to be called directly, because the normal way to create a SOMnn object is to use som.nn.train.

Examples

## Not run: 
new.som <- new("SOMnn", name = name,
              codes = codes,
              qerror = qerror,
              classes = classes, 
              class.idx = class.idx,
              class.counts = class.counts, 
              class.freqs = class.freqs,
              confusion = confusion, 
              measures = measures,
              predict = som.nn.predict,
              xdim = xdim, 
              ydim = ydim, 
              len.total = len.total, 
              toroidal = toroidal,
              norm = norm, 
              norm.center = norm.center, 
              norm.scale = norm.scale,
              dist.fun = dist.fun, 
              max.dist = max.dist)

## End(Not run)

som.nn documentation built on May 2, 2019, 8:26 a.m.