# sBMH: Function to identify the best-matching hexagons/rectangles... In supraHex: supraHex: a supra-hexagonal map for analysing tabular omics data

## Description

`sBMH` is supposed to identify the best-matching hexagons/rectangles (BMH) for the input data.

## Usage

 `1` ```sBMH(sMap, data, which_bmh = c("best", "worst", "all")) ```

## Arguments

 `sMap` an object of class "sMap" or a codebook matrix `data` a data frame or matrix of input data `which_bmh` which BMH is requested. It can be a vector consisting of any integer values from [1, nHex]. Alternatively, it can also be one of "best", "worst" and "all" choices. Here, "best" is equivalent to 1, "worst" for nHex, and "all" for seq(1,nHex)

## Value

a list with following components:

• `bmh`: the requested BMH matrix of dlen x length(which_bmh), where dlen is the total number of rows of the input data

• `qerr`: the corresponding matrix of quantization errors (i.e., the distance between the input data and their BMH), with the same dimensions as "bmh" above

• `mqe`: the mean quantization error for the "best" BMH

• `call`: the call that produced this result

## Note

"which_bmh" upon request can be a vector consisting of any integer values from [1, nHex]

`sPipeline`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```# 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) # 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) # 8) find the best-matching hexagons/rectangles for the input data response <- sBMH(sMap=sM_finetune, data=data, which_bmh="best") ```