visHexMulComp
is supposed to visualise multiple component planes
of a supra-hexagonal grid
visHexMulComp(sMap, which.components = NULL, rect.grid = NULL, margin = rep(0.1, 4), height = 7, title.rotate = 0, title.xy = c(0.45, 1), colormap = c("bwr", "jet", "gbr", "wyr", "br", "yr", "rainbow", "wb"), ncolors = 40, zlim = NULL, border.color = "transparent", gp = grid::gpar(), newpage = TRUE)
invisible
none
# 1) generate data with an iid matrix of 1000 x 3 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 16:56:31 First, define topology of a map grid (2018-01-18 16:56:31)... Second, initialise the codebook matrix (169 X 9) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:31)... Third, get training at the rough stage (2018-01-18 16:56:31)... 1 out of 2 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) 2 out of 2 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) Fourth, get training at the finetune stage (2018-01-18 16:56:31)... 1 out of 7 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) 2 out of 7 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) 3 out of 7 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) 4 out of 7 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) 5 out of 7 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) 6 out of 7 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) 7 out of 7 (2018-01-18 16:56:31) updated (2018-01-18 16:56:31) Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:31)... Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:31)... 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: 4.24649360190788 Below 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 1 End at 2018-01-18 16:56:31 Runtime in total is: 0 secs# 3) visualise multiple component planes of a supra-hexagonal grid visHexMulComp(sMap, colormap="jet", ncolors=20, zlim=c(-1,1), gp=grid::gpar(cex=0.8)) # 3a) visualise only the first 6 component planes visHexMulComp(sMap, which.components=1:6, colormap="jet", ncolors=20, zlim=c(-1,1), gp=grid::gpar(cex=0.8)) # 3b) visualise only the first 6 component planes within the rectangle grid of 3 X 2 visHexMulComp(sMap, which.components=1:6, rect.grid=c(3,2), colormap="jet", ncolors=20, zlim=c(-1,1), gp=grid::gpar(cex=0.8))
visHexMulComp.r
visHexMulComp.Rd
visHexMulComp.pdf
visVp
, visHexComp
,
visColorbar