GQTSOM: Train a Growing Quadtree Self-Organizing Map

View source: R/som.R

GQTSOMR Documentation

Train a Growing Quadtree Self-Organizing Map

Description

Train a Growing Quadtree Self-Organizing Map

Usage

GQTSOM(
  data,
  init.dim = c(3, 3),
  target_codes = 100,
  rlen = 10,
  radius = c(sqrt(sum(init.dim^2)), 0.5),
  epochRadii = seq(radius[1], radius[2], length.out = rlen),
  coords = NULL,
  codes = NULL,
  coordsFn = NULL,
  importance = NULL,
  distf = 2,
  nhbr.distf = 2,
  noMapping = F,
  parallel = F,
  threads = if (parallel) 0 else 1
)

Arguments

data

Input data matrix

init.dim

Initial size of the SOM, default c(3,3)

target_codes

Make the SOM grow linearly to at most this amount of nodes (default 100)

rlen

Number of training iterations

radius

Start and end training radius, as in SOM()

epochRadii

Precise radii for each epoch (must be of length rlen)

coords

Quadtree coordinates of the initial SOM nodes.

codes

Initial codebook

coordsFn

Function to generate/transform grid coordinates (e.g. tSNECoords()). If NULL (default), the grid is the grid is the 2D coordinates of GQTSOM map.

importance

Weights of input data dimensions

distf

Distance measure to use in input data space (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)

nhbr.distf

Distance measure to use in output space (as in distf)

noMapping

If TRUE, do not compute the assignment of input data to SOM nodes

parallel

Parallelize the training by setting appropriate threads. Defaults to FALSE.

threads

Number of threads to use for training. Defaults to 0 (chooses maximum available hardware threads) if parallel=TRUE or 1 (single thread) if parallel=FALSE.


EmbedSOM documentation built on July 5, 2022, 5:14 p.m.