Nothing
library(rasterKernelEstimates)
set.seed(100)
cmdArgs <- commandArgs(trailingOnly = TRUE)
if( !('n' %in% ls() ))n=as.numeric(cmdArgs[1])
if( !('m' %in% ls() ))m=as.numeric(cmdArgs[2])
# check if we got something
if(is.na(n)) n <- 50
if(is.na(m)) m <- 9
print(n)
print(m)
# create a categorical raster
r <- raster::raster( matrix( sample(-4:4,size=n^2,replace=T),n,n))
# create a weight matrix
W <- matrix(1,m,m)
# calculate the weighted local mean and variance
run.time <- proc.time()
rLocalKDE1 <- rasterLocalCategoricalModes(r,W)
print(proc.time() - run.time)
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