Usage
sPipeline(data = NULL, xdim = NULL, ydim = NULL, nHex = NULL,
lattice = c("hexa", "rect"), shape = c("suprahex", "sheet"),
init = c("linear", "uniform", "sample"), algorithm = c("batch",
"sequential"), alphaType = c("invert", "linear", "power"),
neighKernel = c("gaussian", "bubble", "cutgaussian", "ep", "gamma"),
finetuneSustain = F, verbose = T)
Arguments
data
a data frame or matrix of input data
xdim
an integer specifying x-dimension of the grid
ydim
an integer specifying y-dimension of the grid
nHex
the number of hexagons/rectangles in the grid
lattice
the grid lattice, either "hexa" for a hexagon or "rect"
for a rectangle
shape
the grid shape, either "suprahex" for a supra-hexagonal
grid or "sheet" for a hexagonal/rectangle sheet
init
an initialisation method. It can be one of "uniform",
"sample" and "linear" initialisation methods
algorithm
the training algorithm. It can be one of "sequential"
and "batch" algorithm. By default, it uses 'batch' algorithm purely
because of its fast computations (probably also without the compromise
of accuracy). However, it is highly recommended not to use 'batch'
algorithm if the input data contain lots of zeros; it is because matrix
multiplication used in the 'batch' algorithm can be problematic in this
context. If much computation resource is at hand, it is alwasy safe to
use the 'sequential' algorithm
alphaType
the alpha type. It can be one of "invert", "linear"
and "power" alpha types
neighKernel
the training neighborhood kernel. It can be one of
"gaussian", "bubble", "cutgaussian", "ep" and "gamma" kernels
finetuneSustain
logical to indicate whether sustain the
"finetune" training. If true, it will repeat the "finetune" stage until
the mean quantization error does get worse. By default, it sets to
true
verbose
logical to indicate whether the messages will be
displayed in the screen. By default, it sets to false for no display