SOM: Build a self-organizing map

In EmbedSOM: Fast Embedding Guided by Self-Organizing Map

Build a self-organizing map

Description

Build a self-organizing map

Usage

SOM(
  data,
  xdim = 10,
  ydim = 10,
  zdim = NULL,
  batch = F,
  rlen = 10,
  alphaA = c(0.05, 0.01),
  radiusA = stats::quantile(nhbrdist, 0.67) * c(1, 0),
  alphaB = alphaA * c(-negAlpha, -0.1 * negAlpha),
  radiusB = negRadius * radiusA,
  negRadius = 1.33,
  negAlpha = 0.1,
  epochRadii = seq(radiusA[1], radiusA[2], length.out = rlen),
  init = FALSE,
  initf = Initialize_PCA,
  distf = 2,
  codes = NULL,
  importance = NULL,
  coordsFn = NULL,
  nhbr.method = "maximum",
  noMapping = F,
  parallel = F,
  threads = if (parallel) 0 else 1
)

Arguments

data	Matrix containing the training data
xdim	Width of the grid
ydim	Hight of the grid
zdim	Depth of the grid, causes the grid to be 3D if set
batch	Use batch training (default FALSE chooses online training, which is more like FlowSOM)
rlen	Number of training epochs; or number of times to loop over the training data in online training
alphaA	Start and end learning rate for online learning (only for online training)
radiusA	Start and end radius
alphaB	Start and end learning rate for the second radius (only for online training)
radiusB	Start and end radius (only for online training; make sure it is larger than radiusA)
negRadius	easy way to set radiusB as a multiple of default radius (use lower value for higher dimensions)
negAlpha	the same for alphaB
epochRadii	Vector of length rlen with precise epoch radii (only for batch training)
init	Initialize cluster centers in a non-random way
initf	Use the given initialization function if init==T (default: Initialize_PCA)
distf	Distance function (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)
codes	Cluster centers to start with
importance	array with numeric values. Columns of data will be scaled according to importance.
coordsFn	Function to generate/transform grid coordinates (e.g. tSNECoords()). If NULL (default), the grid is the canonical SOM grid.
nhbr.method	Way of computing grid distances, passed as method= to stats::dist() function. Defaults to maximum (square neighborhoods); use euclidean for round neighborhoods.
noMapping	If TRUE, do not compute the mapping (default FALSE). Makes the process quicker by 1 rlen.
parallel	Parallelize the batch training by setting appropriate threads. Defaults to FALSE. Always use batch=TRUE for fully parallelized version, online training is not parallelizable. Passed to MapDataToCodes().
threads	Number of threads of the batch training (has no effect on online training). Defaults to 0 (chooses maximum available hardware threads) if parallel==TRUE or 1 (single thread) if parallel==FALSE. Passed to MapDataToCodes().

Value

A map useful for embedding (EmbedSOM() function) or further analysis, e.g. clustering.

See Also

FlowSOM::SOM

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