Nothing
gpSample <-
function (kernType, numSamps=10, params=NULL, lims=c(-3,3), path=getwd(), png=FALSE) {
## GPSAMPLE Create a plot of samples from a GP.
## FORMAT
## DESC creates a plot of samples from a kernel with the given parameters and variance.
## ARG kernType : the type of kernel to sample from.
## ARG numSamps : the number of samples to take.
## ARG params : parameter vector for the kernel.
## ARG lims : limits of the x axis.
##
## COPYRIGHT : Neil D. Lawrence, 2008; Alfredo Kalaitzis, 2010
t_star = matrix(seq(lims[1], lims[2], length=200), ncol=1)
kern = kernCreate(t_star, kernType)
if (!is.null(params)) {
feval = get(paste(kernType,'KernExpandParam',sep=''), mode="function")
kern = feval(kern, params)
}
feval = get(paste(kernType,'KernExtractParam',sep=''), mode="function")
params = feval(kern, only.values=FALSE)
paramStr = c()
for (i in 1:length(params)) {
Name = strsplit(names(params)[i], split='')[[1]]
Name[1] = toupper(Name[1])
Name = paste(Name, collapse='')
ind = grep(' ', strsplit(Name, split='')[[1]])
Name[ind+1] = toupper(Name[ind+1])
Name[ind] = ''
paramStr = paste(paramStr, Name, as.character(params[i]), sep='')
}
infoStr = paste('Samples', as.character(numSamps), sep='')
## Covariance of the prior.
K_starStar = kernCompute(kern, t_star, t_star)
## Sample from the prior.
fsamp = gaussSamp(Sigma=K_starStar, numSamps=numSamps)
## Plot and save.
graphics.off() ## kill all devices
dev.new(width=5,height=4); plot.new()
matplot(t_star, t(fsamp), ylim=range(fsamp),type='l', xlab='x',ylab='f')
zeroAxes()
if (png) {
KernType = strsplit(kernType, split='')[[1]]
KernType[1] = toupper(KernType[1])
KernType = paste(KernType, collapse='')
fileName = paste('gpSample', KernType, infoStr, paramStr, sep='')
dev.copy2eps(file = paste(path,'/',fileName, '.eps', sep='')) ## Save plot as eps.
## Convert to png. Needs the 'eps2png' facility. If not already installed: 'sudo apt-get install eps2png'
system(paste('eps2png ',path,'/',fileName, '.eps',sep=''))
}
}
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