The initSOM
function returns a paramSOM
class object that
contains the parameters needed to run the SOM algorithm.
initSOM( dimension = c(5, 5), topo = c("square", "hexagonal"), radius.type = c("gaussian", "letremy"), dist.type = switch(match.arg(radius.type), letremy = "letremy", gaussian = "euclidean"), type = c("numeric", "relational", "korresp"), mode = c("online"), affectation = c("standard", "heskes"), maxit = 500, nb.save = 0, verbose = FALSE, proto0 = NULL, init.proto = switch(type, numeric = "random", relational = "obs", korresp = "random"), scaling = switch(type, numeric = "unitvar", relational = "none", korresp = "chi2"), eps0 = 1 ) # S3 method for paramSOM print(x, ...) # S3 method for paramSOM summary(object, ...)
dimension  Vector of two integer points corresponding to the x
dimension and the y dimension of the 

topo  The topology to be used to build the grid of the 
radius.type  The neighbourhood type. Default value is

dist.type  The neighborhood relationship on the grid. One of

type  The SOM algorithm type. Possible values are: 
mode  The SOM algorithm mode. Default value is 
affectation  The SOM affectation type. Default value is 
maxit  The maximum number of iterations to be done during the SOM
algorithm process. Default value is 
nb.save  The number of intermediate backups to be done during the
algorithm process. Default value is 
verbose  The boolean value which activates the verbose mode during the
SOM algorithm process. Default value is 
proto0  The initial prototypes. Default value is 
init.proto  The method to be used to initialize the prototypes, which
may be 
scaling  The type of data preprocessing. For 
eps0  The scaling value for the stochastic gradient descent step in the prototypes' update. The scaling value for the stochastic gradient descent step is equal to \(\frac{0.3\epsilon_0}{1+0.2t/\textrm{dim}}\) where \(t\) is the current step number and \(\textrm{dim}\) is the grid dimension (width multiplied by height). 
x  an object of class 
...  not used 
object  an object of class 
The initSOM
function returns an object of class
paramSOM
which is a list of the parameters passed to the
initSOM
function, plus the default parameters for the ones not
specified by the user.
BenHur A., Weston J. (2010) A user's guide to support vector machine. In: Data Mining Techniques for the Life Sciences, SpringerVerlag, 223239.
Heskes T. (1999) Energy functions for selforganizing maps. In: Kohonen Maps, Oja E., Kaski S. (Eds.), Elsevier, 303315.
Lee J., Verleysen M. (2007) Nonlinear Dimensionality Reduction. Information Science and Statistics series, Springer.
Letrémy P. (2005) Programmes basés sur l'algorithme de Kohonen et dediés à l'analyse des données. SAS/IML programs for 'korresp'. http://samm.univparis1.fr/ProgrammesSASdecartesauto.
Rossi F. (2013) yasomi: Yet Another SelfOrganising Map Implementation. R package, version 0.3. https://github.com/fabricerossi/yasomi
See initGrid
for creating a SOM prior structure
(grid).
#> #> Summary #> #> Class : paramSOM #> #> Parameters of the SOM #> #> SOM mode : online #> SOM type : numeric #> Affectation type : standard #> Grid : #> SelfOrganizing Map structure #> #> Features : #> topology : square #> x dimension : 5 #> y dimension : 5 #> distance type: euclidean #> #> Number of iterations : 500 #> Number of intermediate backups : 0 #> Initializing prototypes method : random #> Data preprocessing type : unitvar #> Neighbourhood type : gaussian #>