The initSOM function returns a paramSOM class object which
contains the parameters needed to run the SOM algorithm.
initSOM(dimension=c(5,5), topo=c("square"),
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, ...)Vector of two integer points corresponding to the x
dimension and the y dimension of the myGrid class object. Default
values are: (5,5). Other data-driven defaults are set by function
trainSOM.
The topology to be used to build the grid of the myGrid
class object. Default value is square.
The neighbourhood type. Default value is "gaussian",
which corresponds to a Gaussian neighbourhood. The annealing of the
neighbourhood during the training step is similar to the one implemented in
yasomi. The alternative value
corresponds to an piecewise linear neighbourhood as implementated by Patrick
Letremy in his SAS programs.
The neighborhood relationship on the grid. When
radius.type is letremy, default value is letremy
which is the original implementation by Patrick Letremy. When
radius.type is gaussian, default value is euclidean. The
other possible values (maximum, manhattan, canberra,
binary, minkowski) are passed to method in function
dist. dist.type="letremy" is not permitted with
radius.type="gaussian".
The SOM algorithm type. Possible values are: numeric
(default value), korresp and relational.
The SOM algorithm mode. Default value is online.
The SOM affectation type. Default value is standard
which corresponds to a hard affectation. Alternative is heskes which
corresponds to Heskes's soft affectation.
The maximum number of iterations to be done during the SOM
algorithm process. Default value is 500. Other data-driven defaults
are set by function trainSOM.
The number of intermediate back-ups to be done during the
algorithm process. Default value is 0.
The boolean value which activates the verbose mode during the
SOM algorithm process. Default value is FALSE.
The initial prototypes. Default value is NULL.
The method to be used to initialize the prototypes, which
may be random (randomization), obs (each prototype is assigned
a random observation) or pca. In pca the prototypes are
initialized to the observations closest to a grid along the two first
principal components of the data (numeric case) or along a
two-dimensional multidimensional scaling (relational case, equivalent
to a relational PCA). Default value is random for the
numeric and korresp types, and obs for the
relational type. pca is not available for korresp SOM.
The type of data pre-processing. For numeric SOM,
possibilities are unitvar (data are centered and scaled; this
is the default value for a numeric SOM), none (no
pre-processing), and center (data are centered but not scaled).
For korresp SOM, the only available value is chi2.
For relational SOM, possibilities are none (no pre-processing,
default value for relational SOM) and cosine. This last one
first turns the dissimilarity into a similarity using the suggestion in
(Lee and Verleysen, 2007). Then, a cosine normalization as described in
(Ben-Hur and Weston, 2010) is applied to the kernel, that is finally turned
back into its induced distance. For further details on this processing, have
a look at the corresponding documentation in the directory "doc" of the
package's installation directory.
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).
an object of class paramSOM.
not used
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.
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.
Letremy P. (2005) Programmes bases sur l'algorithme de Kohonen et dedies a l'analyse des donnees. 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).
# NOT RUN {
# create a default 'paramSOM' class object
default.paramSOM <- initSOM()
summary(default.paramSOM)
# }
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