Nothing
kernel="gauss"
for (kernel in c("exp","matern3_2","matern5_2","gauss")) {
context(paste0("Check LogLikelihood for kernel ",kernel))
f = function(x) 1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7)
plot(f)
n <- 5
set.seed(123)
X <- as.matrix(runif(n))
y = f(X)
points(X,y)
k = DiceKriging::km(design=X,response=y,covtype = kernel,control = list(trace=F),nugget=0, nugget.estim=TRUE)
alpha0 = k@covariance@sd2/(k@covariance@sd2+k@covariance@nugget)
ll_theta = function(theta) DiceKriging::logLikFun(c(theta,alpha0),k)
plot(Vectorize(ll_theta),ylab="LL",xlab="theta",xlim=c(0.01,1))
for (x in seq(0.01,1,,21)){
envx = new.env()
llx = DiceKriging::logLikFun(c(x,alpha0),k,envx)
gllx = DiceKriging::logLikGrad(c(x,alpha0),k,envx)[1,]
arrows(x,llx,x+.1,llx+.1*gllx)
}
library(rlibkriging)
r <- NuggetKriging(y, X, kernel, parameters=list(nugget=0,is_nugget_estim=TRUE))
ll2_theta = function(theta) logLikelihoodFun(r,c(theta,alpha0))$logLikelihood
# second arg is alpha=1 for nugget=0
# plot(Vectorize(ll2),col='red'), add=T)
for (x in seq(0.01,1,,21)){
envx = new.env()
ll2x = logLikelihoodFun(r,c(x,alpha0))$logLikelihood
gll2x = logLikelihoodFun(r,c(x,alpha0),grad = T)$logLikelihoodGrad[,1]
arrows(x,ll2x,x+.1,ll2x+.1*gll2x,col='red')
}
theta0 = k@covariance@range.val
ll_alpha = function(alpha) DiceKriging::logLikFun(c(theta0,alpha),k)
plot(Vectorize(ll_alpha),ylab="LL",xlab="alpha",xlim=c(0.01,1))
for (x in seq(0.01,1,,21)){
envx = new.env()
llx = DiceKriging::logLikFun(c(theta0,x),k,envx)
gllx = DiceKriging::logLikGrad(c(theta0,x),k,envx)[2,]
arrows(x,llx,x+.1,llx+.1*gllx)
}
ll2_alpha = function(alpha) logLikelihoodFun(r,c(theta0,alpha))$logLikelihood
#plot(Vectorize(ll2_alpha),col='red',add=T)
for (x in seq(0.01,1,,21)){
envx = new.env()
ll2x = logLikelihoodFun(r,c(theta0,x))$logLikelihood
gll2x = logLikelihoodFun(r,c(theta0,x),grad = T)$logLikelihoodGrad[,2]
arrows(x,ll2x,x+.1,ll2x+.1*gll2x,col='red')
}
precision <- 1e-8 # the following tests should work with it, since the computations are analytical
x=.25
xenv=new.env()
test_that(desc="logLik is the same that DiceKriging one /alpha",
expect_equal(
logLikelihoodFun(r,c(theta0,x))$logLikelihood[1]
,
DiceKriging::logLikFun(c(theta0,x),k,xenv)
,tolerance = precision))
test_that(desc="logLik Grad is just good /alpha",
expect_equal(
logLikelihoodFun(r,c(theta0,x),grad=T)$logLikelihoodGrad[,2]
,
-(logLikelihoodFun(r,c(theta0,x-1e-5))$logLikelihood[1]-logLikelihoodFun(r,c(theta0,x))$logLikelihood[1])/1e-5,
,tolerance= 1e-5))
test_that(desc="logLik Grad is the same that DiceKriging one /alpha",
expect_equal(
logLikelihoodFun(r,c(theta0,x),grad=T)$logLikelihoodGrad[,2]
,
DiceKriging::logLikGrad(c(theta0,x),k,xenv)[2,]
,tolerance= precision))
xenv=new.env()
test_that(desc="logLik is the same that DiceKriging one /theta",
expect_equal(
logLikelihoodFun(r,c(x,alpha0))$logLikelihood[1]
,
DiceKriging::logLikFun(c(x,alpha0),k,xenv)
,tolerance = precision))
test_that(desc="logLik Grad is just good /theta",
expect_equal(
logLikelihoodFun(r,c(x,alpha0),grad=T)$logLikelihoodGrad[,1]
,
(logLikelihoodFun(r,c(x+1e-5,alpha0))$logLikelihood[1]-logLikelihoodFun(r,c(x,alpha0))$logLikelihood[1])/1e-5
,tolerance= 1e-3))
test_that(desc="logLik Grad is the same that DiceKriging one /theta",
expect_equal(
logLikelihoodFun(r,c(x,alpha0),grad=T)$logLikelihoodGrad[,1]
,
DiceKriging::logLikGrad(c(x,alpha0),k,xenv)[1,]
,tolerance= precision))
}
########################## 2D
for (kernel in c("matern3_2","matern5_2","gauss","exp")) {
context(paste0("Check LogLikelihood for kernel ",kernel))
f <- function(X) apply(X, 1, function(x) prod(sin((x-.5)^2)))
n <- 100
set.seed(123)
X <- cbind(runif(n),runif(n),runif(n))
y <- f(X)
k = DiceKriging::km(design=X,response=y,covtype = kernel,control = list(trace=F),nugget=0, nugget.estim=TRUE)
#library(rlibkriging)
r <- NuggetKriging(y, X, kernel, parameters=list(nugget=0,is_nugget_estim=TRUE))
precision <- 1e-8 # the following tests should work with it, since the computations are analytical
#x = c(.2,.5,.7,0.01)
#x=c(.5,.5,.5,.9999995)
x = c(r$theta(),r$sigma2()/(r$sigma2()+r$nugget()))
#x = c(k@covariance@range.val,k@covariance@sd2/(k@covariance@sd2+k@covariance@nugget))
xenv=new.env()
test_that(desc="logLik is the same that DiceKriging one",
expect_equal(
logLikelihoodFun(r,x)$logLikelihood[1]
,
DiceKriging::logLikFun(x,k,xenv)
,tolerance = precision))
test_that(desc="logLik Grad is the same that DiceKriging one",
expect_equal(
logLikelihoodFun(r,x,grad=T)$logLikelihoodGrad[1,]
,
t(DiceKriging::logLikGrad(x,k,xenv))[1,]
,tolerance= precision))
eps=0.000001
test_that(desc="logLik Grad is just good",
expect_equal(
-logLikelihoodFun(r,x,grad=T)$logLikelihoodGrad[1,]
,
c( # finite-diff grad
(logLikelihoodFun(r,x-c(eps,0,0,0))$logLikelihood[1]-logLikelihoodFun(r,x)$logLikelihood[1])/eps,
(logLikelihoodFun(r,x-c(0,eps,0,0))$logLikelihood[1]-logLikelihoodFun(r,x)$logLikelihood[1])/eps,
(logLikelihoodFun(r,x-c(0,0,eps,0))$logLikelihood[1]-logLikelihoodFun(r,x)$logLikelihood[1])/eps,
(logLikelihoodFun(r,x-c(0,0,0,eps))$logLikelihood[1]-logLikelihoodFun(r,x)$logLikelihood[1])/eps
)
,tolerance= 1e-2))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.