Function to implement training via sequential algorithm

Description

sTrainSeq is supposed to perform sequential 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, each training cycle consisting of: i) randomly choose one input vector; ii) determine the winner hexagon/rectangle (BMH) according to minimum distance of codebook matrix to the input vector; ii) update the codebook matrix of the BMH and its neighbors via updating formula (see "Note" below for details). It also returns an object of class "sMap".

Usage

sTrainSeq(sMap, data, sTrain, verbose = TRUE)

Arguments

sMap
an object of class "sMap" or "sInit"
data
a data frame or matrix of input data
sTrain
an object of class "sTrain"
verbose
logical to indicate whether the messages will be displayed in the screen. By default, it sets to TRUE for display

Value

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

Note

Updating formula is: m_i(t+1) = m_i(t) + \alpha(t)*h_{wi}(t)*[x(t)-m_i(t)], where

  • t denotes the training time/step
  • i and w stand for the hexagon/rectangle i and the winner BMH w, respectively
  • x(t) is an input vector randomly choosen (from the input data) at time t
  • m_i(t) and m_i(t+1) are respectively the prototype vectors of the hexagon i at time t and t+1
  • \alpha(t) is the learning rate at time t. There are three types of learning rate functions:
    • For "linear" function, \alpha(t)=\alpha_0*(1-t/T)
    • For "power" function, \alpha(t)=\alpha_0*(0.005/\alpha_0)^{t/T}
    • For "invert" function, \alpha(t)=\alpha_0/(1+100*t/T)
    • Where \alpha_0 is the initial learing rate (typically, \alpha_0=0.5 at "rough" stage, \alpha_0=0.05 at "finetune" stage), T is the length of training time/step (often being set to input data length, i.e., the total number of rows)

  • 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:
    • For "gaussian" kernel, h_{wi}(t)=e^{-d_{wi}^2/(2*\delta_t^2)}
    • For "cutguassian" kernel, h_{wi}(t)=e^{-d_{wi}^2/(2*\delta_t^2)}*(d_{wi} \le \delta_t)
    • For "bubble" kernel, h_{wi}(t)=(d_{wi} \le \delta_t)
    • For "ep" kernel, h_{wi}(t)=(1-d_{wi}^2/\delta_t^2)*(d_{wi} \le \delta_t)
    • For "gamma" kernel, h_{wi}(t)=1/\Gamma(d_{wi}^2/(4*\delta_t^2)+2)

Examples

# 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, algorithm="sequential", stage="rough") # 5) training at "rough" stage sM_rough <- sTrainSeq(sMap=sI, data=data, sTrain=sT_rough)
1 out of 700 (2017-03-27 18:55:03) 70 out of 700 (2017-03-27 18:55:03) 140 out of 700 (2017-03-27 18:55:03) 210 out of 700 (2017-03-27 18:55:03) 280 out of 700 (2017-03-27 18:55:03) 350 out of 700 (2017-03-27 18:55:03) 420 out of 700 (2017-03-27 18:55:03) 490 out of 700 (2017-03-27 18:55:03) 560 out of 700 (2017-03-27 18:55:03) 630 out of 700 (2017-03-27 18:55:03) 700 out of 700 (2017-03-27 18:55:03)
# 6) define trainology at "finetune" stage sT_finetune <- sTrainology(sMap=sI, data=data, algorithm="sequential", stage="finetune") # 7) training at "finetune" stage sM_finetune <- sTrainSeq(sMap=sM_rough, data=data, sTrain=sT_rough)
1 out of 700 (2017-03-27 18:55:03) 70 out of 700 (2017-03-27 18:55:03) 140 out of 700 (2017-03-27 18:55:03) 210 out of 700 (2017-03-27 18:55:03) 280 out of 700 (2017-03-27 18:55:03) 350 out of 700 (2017-03-27 18:55:03) 420 out of 700 (2017-03-27 18:55:03) 490 out of 700 (2017-03-27 18:55:03) 560 out of 700 (2017-03-27 18:55:03) 630 out of 700 (2017-03-27 18:55:03) 700 out of 700 (2017-03-27 18:55:03)

Source code

sTrainSeq.r

Source man

sTrainSeq.Rd sTrainSeq.pdf

See also

sTrainology, visKernels