Nothing
gpTest <-
function(q=2, d=3, N=10, k=5) {
modelRet = list()
kernType = list(type='cmpnd',comp=list('rbf', 'lin', 'rbfard', 'mlp', 'mlpard', 'white'))
kernType = list(type='cmpnd',comp=list('rbf', 'white'))
meanFunctionType = 'mlp'
learnScales = TRUE ## test learning of output scales.
# learnScales = FALSE
X = matrix(rnorm(N*q), N, q)
Yorig = matrix(rnorm(N*d), N, d)
indMissing = which(matrix(rnorm(N*q), N, q) > 0.7)
approxType = list("ftc", "dtc", "dtcvar", "fitc", "pitc")
approxType = list("ftc")
counter = 0
# for (optimiseBeta in FALSE:TRUE) {
for (optimiseBeta in FALSE) {
# for (meanFunction in FALSE:TRUE) {
for (meanFunction in FALSE) {
# for (missing in FALSE:TRUE) {
for (missing in FALSE) {
# for (fixInducing in FALSE:TRUE) {
for (fixInducing in FALSE) {
Y = Yorig
if (missing)
Y[indMissing] = NaN
if (meanFunction && missing)
next
for (a in 1:length(approxType)) {
options = gpOptions(approxType[[a]])
options$learnScales = learnScales
options$kern = kernType
options$numActive = k
options$isSpherical = !missing
options$isMissingData = missing
options$fixInducing = fixInducing
options$optimiseBeta = optimiseBeta
if (optimiseBeta && approxType[[a]]=="ftc")
options$beta = 1000
else if (missing && approxType[[a]]=="dtcvar")
next
if (meanFunction)
print(paste("Mean Function installed, with ", approxType[[a]],
" approximation.",sep=""))
else
print(paste(approxType[[a]], " approximation.",sep=""))
if (missing) {
print("Missing data used.")
}
if (fixInducing) {
print("Inducing variables fixed.")
options$fixIndices = round(seq(1, dim(Y)[1], len=k))
}
if (!optimiseBeta)
print("Beta not optimised.")
if (meanFunction) {
options$meanFunction = meanFunctionType
options$meanFunctionOptions =
get(paste(meanFunctionType, "Options",sep=""), mode="function")()
}
model = gpCreate(q, d, X, Y, options)
initParams = gpExtractParam(model)
## this creates some nasty parameters.
initParams=matrix(rnorm(prod(dim(as.matrix(initParams)))),
dim(as.matrix(initParams))[1], dim(as.matrix(initParams))[2])
# / (100*matrix(rnorm(prod(dim(as.matrix(initParams)))),
# dim(as.matrix(initParams))[1], dim(as.matrix(initParams))[2])
## This forces kernel computation.
model = gpExpandParam(model, initParams)
gpCovGradsTest(model)
modelGradientCheck(model)
counter = counter + 1
modelRet[[counter]] = model
}
}
}
}
}
return (modelRet)
}
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