Description

visKernels is supposed to visualize a series of neighborhood kernels, each of which is a non-increasing functions of: i) the distance d_{wi} between the hexagon/rectangle i and the winner w, and ii) the radius \delta_t at time t.

Usage

visKernels(newpage = TRUE)

Arguments

newpage

logical to indicate whether to open a new page. By default, it sets to true for opening a new page

Value

invisible

Note

There are five kernels that are currently supported:

• 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)

These kernels above are displayed within a plot for each fixed radius. Three different radii (i.e., 1 and 2) are illustrated.

Examples

# visualise currently supported five kernels
visKernels()

Source code

visKernels.r

Source man

visKernels.Rd visKernels.pdf

See also

sTrainSeq, sTrainBatch