# 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)
# 3) visualise supported mapping items within a supra-hexagonal grid
# 3a) for indexes of hexagons
visHexMapping(sMap,mappingType="indexes")
# 3b) for the number of input data vectors hitting the hexagons
visHexMapping(sMap,mappingType="hits")
# 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")
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