sTrainBatch
is supposed to perform batch training algorithm. It
requires three inputs: a "sMap" or "sInit" object, input data, and a
"sTrain" object specifying training environment. The training is
implemented iteratively, but instead of choosing a single input vector,
the whole input matrix is used. In each training cycle, the whole input
matrix first land in the map through identifying the corresponding
winner hexagon/rectangle (BMH), and then the codebook matrix is updated
via updating formula (see "Note" below for details). It returns an
object of class "sMap".
sTrainBatch(sMap, data, sTrain, verbose = TRUE)
an object of class "sMap", a list with following components:
nHex
: the total number of hexagons/rectanges in the grid
xdim
: x-dimension of the grid
ydim
: y-dimension of the grid
r
: the hypothetical radius of the grid
lattice
: the grid lattice
shape
: the grid shape
coord
: a matrix of nHex x 2, with each row corresponding
to the coordinates of a hexagon/rectangle in the 2D map grid
init
: an initialisation method
neighKernel
: the training neighborhood kernel
codebook
: a codebook matrix of nHex x ncol(data), with
each row corresponding to a prototype vector in input high-dimensional
space
call
: the call that produced this result
Updating formula is: m_i(t+1) =
\frac{\sum_{j=1}^{dlen}h_{wi}(t)x_j}{\sum_{j=1}^{dlen}h_{wi}(t)}
,
where
t
denotes the training time/step
x_j
is an input vector j
from the input data matrix
(with dlen
rows in total)
i
and w
stand for the hexagon/rectangle i
and
the winner BMH w
, respectively
m_i(t+1)
is the prototype vector of the hexagon i
at
time t+1
h_{wi}(t)
is the neighborhood kernel, a non-increasing
function of i) the distance d_{wi}
between the hexagon/rectangle
i
and the winner BMH w
, and ii) the radius \delta_t
at time t
. There are five kernels available:
h_{wi}(t)=e^{-d_{wi}^2/(2*\delta_t^2)}
h_{wi}(t)=e^{-d_{wi}^2/(2*\delta_t^2)}*(d_{wi} \le \delta_t)
h_{wi}(t)=(d_{wi} \le \delta_t)
h_{wi}(t)=(1-d_{wi}^2/\delta_t^2)*(d_{wi}
\le \delta_t)
h_{wi}(t)=1/\Gamma(d_{wi}^2/(4*\delta_t^2)+2)
# 1) generate an iid normal random matrix of 100x10 data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10) # 2) from this input matrix, determine nHex=5*sqrt(nrow(data))=50, # but it returns nHex=61, via "sHexGrid(nHex=50)", to make sure a supra-hexagonal grid sTopol <- sTopology(data=data, lattice="hexa", shape="suprahex") # 3) initialise the codebook matrix using "uniform" method sI <- sInitial(data=data, sTopol=sTopol, init="uniform") # 4) define trainology at "rough" stage sT_rough <- sTrainology(sMap=sI, data=data, stage="rough") # 5) training at "rough" stage sM_rough <- sTrainBatch(sMap=sI, data=data, sTrain=sT_rough)1 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 2 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 3 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 4 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 5 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 6 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 7 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12)# 6) define trainology at "finetune" stage sT_finetune <- sTrainology(sMap=sI, data=data, stage="finetune") # 7) training at "finetune" stage sM_finetune <- sTrainBatch(sMap=sM_rough, data=data, sTrain=sT_rough)1 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 2 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 3 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 4 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 5 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 6 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12) 7 out of 7 (2018-01-18 16:56:12) updated (2018-01-18 16:56:12)
sTrainBatch.r
sTrainBatch.Rd
sTrainBatch.pdf
sTrainology
, visKernels