SOM {EmbedSOM} | R Documentation |
Build a self-organizing map
SOM(data, xdim = 10, ydim = 10, zdim = NULL, rlen = 10, alphaA = c(0.05, 0.01), radiusA = stats::quantile(nhbrdist, 0.67) * c(1, 0), alphaB = alphaA * c(-negAlpha, -0.01 * negAlpha), radiusB = negRadius * radiusA, init = FALSE, initf = Initialize_PCA, distf = 2, codes = NULL, importance = NULL, nhbr.method = "maximum", negRadius = 1.33, negAlpha = 0.1, noMapping = F)
data |
Matrix containing the training data |
xdim |
Width of the grid |
ydim |
Hight of the grid |
zdim |
Depth of the grid, causes grid to be 3D |
rlen |
Number of times to loop over the training data for each MST |
alphaA |
Start and end learning rate |
radiusA |
Start and end radius |
alphaB |
Start and end learning rate for the second radius |
radiusB |
Start and end radius (make sure it's larger than radiusA) |
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) |
codes |
Cluster centers to start with |
importance |
array with numeric values. Parameters will be scaled according to importance |
nhbr.method |
Way of computing grid distances, passed as method= to dist() function. Default 'maximum' (square neighborhoods); use 'euclidean' for round neighborhoods. |
negRadius |
easy way to set radiusB as a multiple of default radius (use lower value for higher dimensions) |
negAlpha |
the same for alphaB |
noMapping |
If true, do not produce mapping (default F). Useful for online/streaming use. |
A map, which is a list containing all parameter settings and results
FlowSOM::SOM