## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", echo = TRUE, results = 'hold', warning=F, cache=F, #dev = 'pdf', message=F, fig.width=5, fig.height=5, tidy.opts=list(width.cutoff=75), tidy=FALSE ) old <- options(scipen = 1, digits = 4) ## ----setup-------------------------------------------------------------------- library(GPFDA) require(MASS) ## ----------------------------------------------------------------------------- set.seed(123) nrep <- 30 n <- 15 input <- seq(0, 1, length.out=n) hp <- list('linear.a'=log(40), 'linear.i'=log(10), 'pow.ex.v'=log(5), 'pow.ex.w'=log(15), 'vv'=log(0.3)) Sigma <- cov.linear(hyper=hp, input=input) + cov.pow.ex(hyper=hp, input=input, gamma=2) + diag(exp(hp$vv), n, n) Y <- t(mvrnorm(n=nrep, mu=rep(0,n), Sigma=Sigma)) ## ----------------------------------------------------------------------------- set.seed(111) fitNoGrad <- gpr(input=input, response=Y, Cov=c('linear','pow.ex'), gamma=2, trace=4, nInitCandidates = 1, useGradient = F) ## ----------------------------------------------------------------------------- set.seed(111) fit <- gpr(input=input, response=Y, Cov=c('linear','pow.ex'), gamma=2, trace=4, nInitCandidates = 1, useGradient = T) ## ----------------------------------------------------------------------------- sapply(fit$hyper, exp) ## ----------------------------------------------------------------------------- plot(fit, realisation=10) ## ----------------------------------------------------------------------------- inputNew <- seq(0, 1, length.out = 1000) pred1 <- gprPredict(train=fit, inputNew=inputNew, noiseFreePred=T) plot(pred1, realisation=10) ## ----------------------------------------------------------------------------- pred2 <- gprPredict(train=fit, inputNew=inputNew, noiseFreePred=F) plot(pred2, realisation=10) ## ---- include = FALSE--------------------------------------------------------- options(old)