`sTrainology`

is supposed to define the train-ology (i.e., the
training environment/parameters). The trainology here refers to the
training algorithm, the training stage, the stage-specific parameters
(alpha type, initial alpha, initial radius, final radius and train
length), and the training neighbor kernel used. It returns an object of
class "sTrain".

1 2 3 4 5 |

`sMap` |
an object of class "sMap" or "sInit" |

`data` |
a data frame or matrix of input data |

`algorithm` |
the training algorithm. It can be one of "sequential" and "batch" algorithm |

`stage` |
the training stage. The training can be achieved using two stages (i.e., "rough" and "finetune") or one stage only (i.e., "complete") |

`alphaType` |
the alpha type. It can be one of "invert", "linear" and "power" alpha types |

`neighKernel` |
the training neighbor kernel. It can be one of "gaussian", "bubble", "cutgaussian", "ep" and "gamma" kernels |

an object of class "sTrain", a list with following components:

`algorithm`

: the training algorithm`stage`

: the training stage`alphaType`

: the alpha type`alphaInitial`

: the initial alpha`radiusInitial`

: the initial radius`radiusFinal`

: the final radius`neighKernel`

: the neighbor kernel`call`

: the call that produced this result

Training stage-specific parameters:

"radiusInitial": it depends on the grid shape and training stage

For "sheet" shape: it equals

*max(1,ceiling(max(xdim,ydim)/8))*at "rough" or "complete" stage, and*max(1,ceiling(max(xdim,ydim)/32))*at "finetune" stageFor "suprahex" shape: it equals

*max(1,ceiling(r/2))*at "rough" or "complete" stage, and*max(1,ceiling(r/8))*at "finetune" stage

"radiusFinal": it depends on the training stage

At "rough" stage, it equals

*radiusInitial/4*At "finetune" or "complete" stage, it equals

*1*

"trainLength": how many times the whole input data are set for training. It depends on the training stage and training algorithm

At "rough" stage, it equals

*max(1,10 * trainDepth)*At "finetune" stage, it equals

*max(1,40 * trainDepth)*At "complete" stage, it equals

*max(1,50 * trainDepth)*When using "batch" algorithm and the trainLength equals 1 according to the above equation, the trainLength is forced to be 2 unless

*radiusInitial*equals*radiusFinal*Where

*trainDepth*is the training depth, defined as*nHex/dlen*, i.e., how many hexagons/rectanges are used per the input data length (here*dlen*refers to the number of rows)

`sInitial`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# 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 different stages
# 4a) define trainology at "rough" stage
sT_rough <- sTrainology(sMap=sI, data=data, stage="rough")
# 4b) define trainology at "finetune" stage
sT_finetune <- sTrainology(sMap=sI, data=data, stage="finetune")
# 4c) define trainology using "complete" stage
sT_complete <- sTrainology(sMap=sI, data=data, stage="complete")
``` |

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