| Title: | Somoclu |
|---|---|
| Description: | Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) <doi:10.18637/jss.v078.i09>. |
| Authors: | Peter Wittek [aut], Shichao Gao [cre] |
| Maintainer: | Shichao Gao <[email protected]> |
| License: | GPL-3 |
| Version: | 1.7.6 |
| Built: | 2024-10-01 06:43:12 UTC |
| Source: | CRAN |
tiny rgbs data for testing
rgbsrgbs
matrix in plain text form
A function call to convert Somoclu train result to kohonen class for plotting.
Rsomoclu.kohonen(input_data, result, n.hood = NULL, toroidal = FALSE)Rsomoclu.kohonen(input_data, result, n.hood = NULL, toroidal = FALSE)
input_data |
input data, matrix format |
result |
The result returned by Rsomoclu.train |
n.hood |
Same as in koohonen, the shape of the neighbourhood, either "circular" or "square". The latter is the default for rectangular maps, the former for hexagonal maps. |
toroidal |
if TRUE, the edges of the map are joined. Note that in a hexagonal toroidal map, the number of rows must be even. |
An object of class kohonen for plotting.
https://www.r-bloggers.com/2014/02/self-organising-maps-for-customer-segmentation-using-r/
library('Rsomoclu') library('kohonen') data("rgbs", package = "Rsomoclu") input_data <- rgbs input_data <- data.matrix(input_data) nSomX <- 20 nSomY <- 20 nEpoch <- 10 radius0 <- 0 radiusN <- 0 radiusCooling <- "linear" scale0 <- 0 scaleN <- 0.01 scaleCooling <- "linear" kernelType <- 0 mapType <- "planar" gridType <- "rectangular" compactSupport <- FALSE codebook <- NULL neighborhood <- "gaussian" stdCoeff <- 0.5 res <- Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook) ## Convert to kohonen object for plotting sommap = Rsomoclu.kohonen(input_data, res) ## Show 'codebook' plot(sommap, type="codes", main = "Codes") ## Show 'component planes' plot(sommap, type = "property", property = sommap$codes[[1]][,1], main = colnames(sommap$codes)[1]) ## Show 'U-Matrix' plot(sommap, type="dist.neighbours")library('Rsomoclu') library('kohonen') data("rgbs", package = "Rsomoclu") input_data <- rgbs input_data <- data.matrix(input_data) nSomX <- 20 nSomY <- 20 nEpoch <- 10 radius0 <- 0 radiusN <- 0 radiusCooling <- "linear" scale0 <- 0 scaleN <- 0.01 scaleCooling <- "linear" kernelType <- 0 mapType <- "planar" gridType <- "rectangular" compactSupport <- FALSE codebook <- NULL neighborhood <- "gaussian" stdCoeff <- 0.5 res <- Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook) ## Convert to kohonen object for plotting sommap = Rsomoclu.kohonen(input_data, res) ## Show 'codebook' plot(sommap, type="codes", main = "Codes") ## Show 'component planes' plot(sommap, type = "property", property = sommap$codes[[1]][,1], main = colnames(sommap$codes)[1]) ## Show 'U-Matrix' plot(sommap, type="dist.neighbours")
A function call to Somoclu to train the Self Organizing Map.
Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook, vectDistance)Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook, vectDistance)
input_data |
input data, matrix format |
nEpoch |
Maximum number of epochs |
nSomX |
Number of columns in map (size of SOM in direction x) |
nSomY |
Number of rows in map (size of SOM in direction y) |
radius0 |
Start radius (default: half of the map in direction min(x,y)) |
radiusN |
End radius (default: 1) |
radiusCooling |
Radius cooling strategy: linear or exponential (default: linear) |
scale0 |
Starting learning rate (default: 1.0) |
scaleN |
Finishing learning rate (default: 0.01) |
scaleCooling |
Learning rate cooling strategy: linear or exponential (default: linear) |
kernelType |
Kernel type 0: Dense CPU 1: Dense GPU 2: Sparse CPU (default: 0) |
mapType |
Map type: planar or toroid (default: "planar") |
gridType |
Grid type: square or hexagonal (default: "rectangular") |
compactSupport |
Compact support for Gaussian neighborhood, (default:TRUE) |
neighborhood |
Neighborhood function: gaussian or bubble (default: "gaussian") |
codebook |
initial codebook, (default:NULL) |
stdCoeff |
The coefficient in the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)), (default:0.5) |
vectDistance |
the vector distance function "norm-3", "norm-6", "norm-2"(same as default) "norm-inf", is supported with kerneltype = 0 only , (default:euclidean) |
a list including elements
codebook |
the codebook |
globalBmus |
global Best Matching Unit matrix |
uMatrix |
uMatrix |
Peter Wittek, Shichao Gao
Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2017). Somoclu: An Efficient Parallel Library for Self-Organizing Maps. Journal of Statistical Software, 78(9), 1-21. doi:10.18637/jss.v078.i09.
library('Rsomoclu') data("rgbs", package = "Rsomoclu") input_data <- rgbs input_data <- data.matrix(input_data) nSomX <- 10 nSomY <- 10 nEpoch <- 10 radius0 <- 0 radiusN <- 0 radiusCooling <- "linear" scale0 <- 0 scaleN <- 0.01 scaleCooling <- "linear" kernelType <- 0 mapType <- "planar" gridType <- "rectangular" compactSupport <- FALSE codebook <- NULL neighborhood <- "gaussian" stdCoeff <- 0.5 vectDistance <- "euclidean" res <- Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook, vectDistance) res$codebook res$globalBmus res$uMatrix library('kohonen') sommap = Rsomoclu.kohonen(input_data, res)library('Rsomoclu') data("rgbs", package = "Rsomoclu") input_data <- rgbs input_data <- data.matrix(input_data) nSomX <- 10 nSomY <- 10 nEpoch <- 10 radius0 <- 0 radiusN <- 0 radiusCooling <- "linear" scale0 <- 0 scaleN <- 0.01 scaleCooling <- "linear" kernelType <- 0 mapType <- "planar" gridType <- "rectangular" compactSupport <- FALSE codebook <- NULL neighborhood <- "gaussian" stdCoeff <- 0.5 vectDistance <- "euclidean" res <- Rsomoclu.train(input_data, nEpoch, nSomX, nSomY, radius0, radiusN, radiusCooling, scale0, scaleN, scaleCooling, kernelType, mapType, gridType, compactSupport, neighborhood, stdCoeff, codebook, vectDistance) res$codebook res$globalBmus res$uMatrix library('kohonen') sommap = Rsomoclu.kohonen(input_data, res)