## -----------------------------------------------------------------------------
## Calcul de probabilite associee a des evenemenrs rares
## * Limit State Function (LSF) :
## !!! DEFINITION DANS ESPACE NORME CENTRE : LSF DOIT COMPORTER DES TRANSFO
## ISO-PROBABILISTES SI NECESSAIRES
## * Methode : Subset
## * Reference : WAARTS
## -----------------------------------------------------------------------------
## Copyright (C) 2015
## Gilles DEFAUX
## CEA / DAM / DIF
## gilles.defaux@cea.fr
## -----------------------------------------------------------------------------
## On efface toutes les donnees
rm(list=ls(all=TRUE))
## Source Subset Simulation files
library(mistral)
## -----------------------------------
## PARAMETRES ET OPTIONS
## -----------------------------------
set.seed(123456)
p0 = 0.1 # Subset cutoff probability
NbSim = 5000 # Monte-Carlo population size for subsets estimation
## -----------------------------------
## DEFINITION DU PROBLEME
## -----------------------------------
Dim = 2
## Modele Stochastique
distX1 <- list(type='Norm', MEAN=0.0, STD=1.0, P1=NULL, P2=NULL, NAME='X1')
distX2 <- list(type='Norm', MEAN=0.0, STD=1.0, P1=NULL, P2=NULL, NAME='X2')
input.margin <- list(distX1,distX2)
input.Rho <- diag(Dim)
L0 <- chol(input.Rho)
lsf = function(U) {
# U <- as.matrix(U)
X <- UtoX(U, input.margin, L0)
G <- 5.0 - 0.2*(X[1,]-X[2,])^2.0 - (X[1,]+X[2,])/sqrt(2.0)
return(G)
}
## TEST
U0 <- c(1.0,1.0)
lsf(U0)
## -----------------------------------
## CALCUL
## -----------------------------------
SS = SubsetSimulation( dimension = Dim,
lsf = lsf,
p_0 = p0,
N = NbSim,
q = 0.0,
lower.tail = TRUE,
plot = TRUE,
output_dir = NULL,
verbose = 2)
# print(SS)
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