`visHexMapping`

is supposed to visualise various mapping items
within a supra-hexagonal grid

visHexMapping(sObj, mappingType = c("indexes", "hits", "dist", "antidist", "bases", "customized"), labels = NULL, height = 7, margin = rep(0.1, 4), area.size = 1, gp = grid::gpar(cex = 0.7, font = 1, col = "black"), border.color = NULL, fill.color = "transparent", lty = 1, lwd = 1, lineend = "round", linejoin = "round", clip = c("on", "inherit", "off"), newpage = TRUE)

- sObj
- an object of class "sMap" or "sInit" or "sTopol"
- mappingType
- the mapping type, can be "indexes", "hits", "dist", "antidist", "bases", and "customized"
- labels
- NULL or a vector with the length of nHex
- height
- a numeric value specifying the height of device
- margin
- margins as units of length 4 or 1
- area.size
- an inteter or a vector specifying the area size of each hexagon
- gp
- an object of class "gpar". It is the output from a call to the function "gpar" (i.e., a list of graphical parameter settings)
- border.color
- the border color for each hexagon
- fill.color
- the filled color for each hexagon
- lty
- the line type for each hexagon. 0 for 'blank', 1 for 'solid', 2 for 'dashed', 3 for 'dotted', 4 for 'dotdash', 5 for 'longdash', 6 for 'twodash'
- lwd
- the line width for each hexagon
- lineend
- the line end style for each hexagon. It can be one of 'round', 'butt' and 'square'
- linejoin
- the line join style for each hexagon. It can be one of 'round', 'mitre' and 'bevel'
- clip
- either "on" for clipping to the extent of this viewport, "inherit" for inheriting the clipping region from the parent viewport, or "off" to turn clipping off altogether
- newpage
- logical to indicate whether to open a new page. By default, it sets to true for opening a new page

invisible

The mappingType includes:

- "indexes": the index of hexagons in a supra-hexagonal grid
- "hits": the number of input data vectors hitting the hexagons
- "dist": distance (in high-dimensional input space) to neighbors (defined in 2D output space)
- "antidist": the oppose version of "dist"
- "bases": clusters partitioned from the sMap
- "customized": displaying input "labels"

# 1) generate data with an iid matrix of 1000 x 9 data <- cbind(matrix(rnorm(1000*3,mean=0,sd=1), nrow=1000, ncol=3), matrix(rnorm(1000*3,mean=0.5,sd=1), nrow=1000, ncol=3), matrix(rnorm(1000*3,mean=-0.5,sd=1), nrow=1000, ncol=3)) colnames(data) <- c("S1","S1","S1","S2","S2","S2","S3","S3","S3") # 2) sMap resulted from using by default setup sMap <- sPipeline(data=data)Start at 2018-01-18 16:56:29First, define topology of a map grid (2018-01-18 16:56:29)...Second, initialise the codebook matrix (169 X 9) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:29)...Third, get training at the rough stage (2018-01-18 16:56:29)...1 out of 2 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)2 out of 2 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)Fourth, get training at the finetune stage (2018-01-18 16:56:29)...1 out of 7 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)2 out of 7 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)3 out of 7 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)4 out of 7 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)5 out of 7 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)6 out of 7 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)7 out of 7 (2018-01-18 16:56:29)updated (2018-01-18 16:56:29)Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:29)...Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:29)...Below are the summaries of the training results:dimension of input data: 1000x9 xy-dimension of map grid: xdim=15, ydim=15, r=8 grid lattice: hexa grid shape: suprahex dimension of grid coord: 169x2 initialisation method: linear dimension of codebook matrix: 169x9 mean quantization error: 4.27891105682274Below are the details of trainology:training algorithm: batch alpha type: invert training neighborhood kernel: gaussian trainlength (x input data length): 2 at rough stage; 7 at finetune stage radius (at rough stage): from 4 to 1 radius (at finetune stage): from 1 to 1End at 2018-01-18 16:56:29Runtime in total is: 0 secs# 3) visualise supported mapping items within a supra-hexagonal grid # 3a) for indexes of hexagons visHexMapping(sMap, mappingType="indexes", fill.color="transparent") # 3b) for the number of input data vectors hitting the hexagons visHexMapping(sMap, mappingType="hits", fill.color=NULL) # 3c) for distance (in high-dimensional input space) to neighbors (defined in 2D output space) visHexMapping(sMap, mappingType="dist") # 3d) for clusters/bases partitioned from the sMap visHexMapping(sMap, mappingType="bases")

`visHexMapping.r`

`visHexMapping.Rd`

`visHexMapping.pdf`

`sDmat`

, `sDmatCluster`

,
`visHexGrid`