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, ...)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. Accept values "square" (Default) or "hexagonal".
The neighborhood type. Default value is
"gaussian", which corresponds to a Gaussian neighborhood. The
annealing of the neighborhood during the training step is similar to the one
implemented in yasomi. The
alternative value corresponds to an piecewise linear neighborhood as
implemented by Patrick Letrémy in his SAS scripts.
The neighborhood relationship on the grid. One of
c("letremy", "euclidean", "maximum", "manhattan", "canberra", "minkowski").
When radius.type is letremy, default value is letremy
which is the original implementation by Patrick Letrémy. When
radius.type is gaussian, default value is euclidean. The
other possible values are passed to method in function
dist. dist.type = "letremy" is not permitted with
radius.type = "gaussian". Only euclidian is allowed with
hexagonal topology.
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
an object of class paramSOM.
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.
Letrémy P. (2005) Programmes basés sur l'algorithme de Kohonen et dediés à l'analyse des données. SAS/IML programs for 'korresp'.
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).
# create a default 'paramSOM' class object
default.paramSOM <- initSOM()
summary(default.paramSOM)
#>
#> 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
#>