initSOM {SOMbrero}

Initialize parameters for the SOM algorithm

Description

The initSOM function returns a paramSOM class object that contains the parameters needed to run the SOM algorithm.

Usage

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, ...)

Arguments

dimension

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.

topo

The topology to be used to build the grid of the myGrid class object. Accept values "square" (Default) or "hexagonal".

radius.type

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.

dist.type

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.

type

The SOM algorithm type. Possible values are: numeric (default value), korresp and relational.

mode

The SOM algorithm mode. Default value is online.

affectation

The SOM affectation type. Default value is standard which corresponds to a hard affectation. Alternative is heskes which corresponds to Heskes's soft affectation.

maxit

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.

nb.save

The number of intermediate back-ups to be done during the algorithm process. Default value is 0.

verbose

The boolean value which activates the verbose mode during the SOM algorithm process. Default value is FALSE.

proto0

The initial prototypes. Default value is NULL.

init.proto

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.

scaling

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.

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 paramSOM.

...

not used

object

an object of class paramSOM.

Value

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.

Author(s)

Élise Maigné [e-mail]
Madalina Olteanu [e-mail]
Nathalie Vialaneix [e-mail]

References

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 Also

See initGrid for creating a SOM prior structure (grid).

Examples

# create a default 'paramSOM' class object
default.paramSOM <- initSOM()
summary(default.paramSOM)