library(testthat) Sys.setenv('OMP_THREAD_LIMIT'=2) library(rlibkriging) ##library(testthat) Sys.setenv('OMP_THREAD_LIMIT'=2) #library(rlibkriging) library(RobustGaSP) context("RobustGaSP / Fit: 1D") 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 = RobustGaSP::rgasp(design=X,response=y, trend=cbind(matrix(1,length(y),1),X)) ##library(rlibkriging) r <- Kriging(y, X, kernel="matern5_2", regmodel = "linear", normalize = FALSE, optim = "BFGS", objective = "LMP") # m = as.list(r) # Check lmp function lmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1); # print(dim(X)); apply(X,1, function(x) { #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost y=RobustGaSP:::neg_log_marginal_post_approx_ref(param=(x),nugget=0, nugget.est=model@nugget.est, R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, CL=model@CL, a=0.2, b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]), kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha ) y})} lmp_rgasp(1) plot(lmp_rgasp,xlim=c(0.01,6)) abline(v=(log(k@beta_hat))) lmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1); # print(dim(X)); apply(X,1, function(x) { y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1))$logMargPost y})} lmp_lk(1) lines(seq(0.1,6,,5),lmp_lk(seq(0.1,6,,5)),col='red') abline(v=(log(1/as.list(r)$theta)),col='red') precision <- 1e-3 test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp is the same that lk/lmp one"), expect_equal(lmp_rgasp(1),lmp_lk(1),tol = precision)) test_that(desc=paste0("RobustGaSP / Fit: 1D / fitted theta is the same that RobustGaSP one"), expect_equal(as.list(r)$theta[1],1/k@beta_hat,tol = precision)) dlmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1); # print(dim(X)); apply(X,1, function(x) { # print(RobustGaSP:::log_marginal_lik_deriv(param=(x),nugget=0,nugget_est=model@nugget.est, # R0=model@R0,X=model@X, zero_mean=model@zero_mean, # output=model@output, # kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha)) # # print(RobustGaSP:::log_approx_ref_prior_deriv(param=(x),nugget=0, nugget_est=model@nugget.est, # CL=model@CL, # a=0.2, # b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]))) #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost y=RobustGaSP:::neg_log_marginal_post_approx_ref_deriv(param=(x),nugget=0, nugget.est=model@nugget.est, R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, CL=model@CL, a=0.2, b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]), kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha ) y})} dlmp_rgasp(1) dlmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1); apply(X,1, function(x) { y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1),TRUE)$logMargPostGrad y})} -exp(-1)*dlmp_lk(1) precision <- 1e-3 test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp deriv is the same that lk/lmp deriv"), expect_equal(dlmp_rgasp(1),-exp(-1)*dlmp_lk(1),tol = precision))