`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 *δ_t* at time *t*.

1 | ```
visKernels(newpage = TRUE)
``` |

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

invisible

There are five kernels that are currently supported:

For "gaussian" kernel,

*h_{wi}(t)=e^{-d_{wi}^2/(2*δ_t^2)}*For "cutguassian" kernel,

*h_{wi}(t)=e^{-d_{wi}^2/(2*δ_t^2)}*(d_{wi} ≤ δ_t)*For "bubble" kernel,

*h_{wi}(t)=(d_{wi} ≤ δ_t)*For "ep" kernel,

*h_{wi}(t)=(1-d_{wi}^2/δ_t^2)*(d_{wi} ≤ δ_t)*For "gamma" kernel,

*h_{wi}(t)=1/Γ(d_{wi}^2/(4*δ_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.

`sTrainSeq`

, `sTrainBatch`

1 2 | ```
# visualise currently supported five kernels
visKernels()
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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