Description Usage Arguments Value Author(s) References See Also Examples
The initSOM
function returns a paramSOM
class object that
contains the parameters needed to run the SOM algorithm.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | 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 class 'paramSOM'
print(x, ...)
## S3 method for class '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 neighborhood 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 back-ups 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 pre-processing. 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 0.3*eps0/(1+0.2*t/dim) where t is the current step number and 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.
Élise Maigné <elise.maigne@inrae.fr>
Madalina Olteanu olteanu@ceremade.dauphine.fr
Nathalie Vialaneix nathalie.vialaneix@inrae.fr
Ben-Hur A., Weston J. (2010) A user's guide to support vector machine. In: Data Mining Techniques for the Life Sciences, Springer-Verlag, 223-239.
Heskes T. (1999) Energy functions for self-organizing maps. In: Kohonen Maps, Oja E., Kaski S. (Eds.), Elsevier, 303-315.
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.univ-paris1.fr/Programmes-SAS-de-cartes-auto.
Rossi F. (2013) yasomi: Yet Another Self-Organising Map Implementation. R package, version 0.3. https://github.com/fabrice-rossi/yasomi
See initGrid
for creating a SOM prior structure
(grid).
1 2 3 |
Loading required package: knitr
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
***********************************************************
This is 'SOMbrero' package, v 1.2
Citation details with citation('SOMbrero')
Further information with help(SOMbrero)...
Use sombreroGUI() to start the Graphical Interface.
Warning! This package has been implemented (mostly) by girls.
Default colors may not be suited for men.
***********************************************************
Summary
Class : paramSOM
Parameters of the SOM
SOM mode : online
SOM type : numeric
Affectation type : standard
Grid :
Self-Organizing 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 pre-processing type : unitvar
Neighbourhood type : gaussian
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