Nothing
## ----setup, include = FALSE---------------------------------------------------
library(calibrar)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----echo=TRUE, eval=FALSE, results='markup'----------------------------------
# optim2(
# par,
# fn,
# gr = NULL,
# ...,
# method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent", "nlm", "nlminb",
# "Rcgmin", "Rvmmin", "hjn", "spg", "LBFGSB3", "AHR-ES"),
# lower = -Inf,
# upper = +Inf,
# active = NULL,
# control = list(),
# hessian = FALSE,
# parallel = FALSE
# )
## ----echo=TRUE, results='markup'----------------------------------------------
library(calibrar)
optim(par=rep(1, 5), fn=function(x) sum(x^2))
## ----echo=TRUE, results='markup'----------------------------------------------
optim2(par=rep(1, 5), fn=function(x) sum(x^2))
## ----echo=TRUE, results='markup'----------------------------------------------
optim2(par=rep(1, 5), fn=function(x) sum(x^2), method="nlm")
## ----echo=TRUE, results='markup'----------------------------------------------
set.seed(880820) # for reproducibility
optim2(par=rep(1, 5), fn=function(x) sum(x^2), method="AHR-ES")
## ----echo=TRUE, results='markup'----------------------------------------------
optim2(par=rep(1, 5), fn=function(x) sum(x^2),
active=c(TRUE, TRUE, FALSE, FALSE, TRUE))
## ----echo=TRUE, results='markup'----------------------------------------------
optim2(par=rep(1, 5), fn=function(x) sum(x^2), parallel=TRUE)
## ----echo=TRUE, results='hide', eval=FALSE------------------------------------
# optim2(par=rep(0.5, 5), fn=function(x) sum(2*x^(3.1*x)), control=list(gr.method="richardson"))
# optim2(par=rep(0.5, 5), fn=function(x) sum(2*x^(3.1*x)), control=list(gr.method="central"))
# optim2(par=rep(0.5, 5), fn=function(x) sum(2*x^(3.1*x)), control=list(gr.method="forward"))
#
## ----echo=TRUE, eval=FALSE, results='markup'----------------------------------
# optimh(
# par,
# fn,
# gr = NULL,
# ...,
# method = c("AHR-ES", "Nelder-Mead", "SANN", "hjn", "CMA-ES", "genSA", "DE", "soma",
# "genoud", "PSO", "hybridPSO", "mads", "hjk", "hjkb", "nmk", "nmkb"),
# lower = -Inf,
# upper = +Inf,
# active = NULL,
# control = list(),
# hessian = FALSE,
# parallel = FALSE
# )
## ----echo=TRUE, results='markup'----------------------------------------------
# Covariance Matrix Adaptation Evolutionary Strategy
set.seed(880820) # for reproducibility
optimh(par=rep(1, 5), fn=function(x) sum(x^2), method="CMA-ES",
control=list(maxit=200))
## ----echo=TRUE, results='markup'----------------------------------------------
# Generalized Simulated Anneling
set.seed(880820) # for reproducibility
optimh(par=rep(1, 5), fn=function(x) sum(x^2), method="genSA",
lower=rep(-100, 5), upper=rep(100, 5),
control=list(maxit=200, temperature=6000))
## ----echo=TRUE, results='markup'----------------------------------------------
# Self-Organising Migrating Algorithm
set.seed(880820) # for reproducibility
optimh(par=rep(1, 5), fn=function(x) sum(x^2), method="soma",
lower=rep(-100, 5), upper=rep(100, 5),
control=list(maxit=200))
## ----echo=FALSE, results='hide', eval=TRUE, message=FALSE---------------------
library(parallel)
## ----echo=TRUE, results='markup', eval=FALSE----------------------------------
# library(parallel)
# ncores = detectCores() - 1 # number of cores to be used
# cl = makeCluster(ncores)
# # this is slower than sequential for very fast models (like this one)
# optim2(par=rep(0.5, 5), fn=function(x) sum(x^2),
# control=list(ncores=ncores), parallel=TRUE)
# stopCluster(cl) # close the parallel connections
## ----echo=TRUE, eval=FALSE, results='markup'----------------------------------
# calibrate(
# par,
# fn,
# gr = NULL,
# ...,
# method = NULL,
# lower = NULL,
# upper = NULL,
# phases = NULL,
# control = list(),
# hessian = FALSE,
# replicates = 1,
# parallel = FALSE
# )
## ----echo=TRUE, results='markup'----------------------------------------------
calibrate(par=c(1,2,3,NA,NA), fn=function(x) sum(x^2))
## ----echo=TRUE, results='markup'----------------------------------------------
calibrate(par=c(1,2,3,NA,5), fn=function(x) sum(x^2),
lower=rep(-100, 5), upper=rep(100, 5))
## ----echo=TRUE, results='markup'----------------------------------------------
calibrate(par=c(1,2,3,NA,5), fn=function(x) sum(x^2),
lower=rep(-100, 5), upper=rep(100, 5),
phases=c(1,2,3,2,1))
## ----echo=TRUE, results='markup'----------------------------------------------
calibrate(par=c(1,2,3,NA,5), fn=function(x) sum(x^2),
lower=rep(-100, 5), upper=rep(100, 5),
phases=c(1,2,-1,2,1))
## ----echo=TRUE, results='markup'----------------------------------------------
calibrate(par=c(1,2,3,NA,5), fn=sphereN,
lower=rep(-100, 5), upper=rep(100, 5),
phases=c(1,2,3,2,1), replicates=3, control=list(maxit=1000))
## ----echo=TRUE, results='markup'----------------------------------------------
calibrate(par=c(1,2,3,NA,5), fn=sphereN,
lower=rep(-100, 5), upper=rep(100, 5),
phases=c(1,2,3,2,1), replicates=c(1,1,5), control=list(maxit=1000))
## ----echo=TRUE, results='markup'----------------------------------------------
calibrate(par=list(par1=c(1,2,3), par2=NA, par3=5), fn=sphereN,
lower=rep(-100, 5), upper=rep(100, 5),
phases=c(1,2,-3,2,1), replicates=c(1,5), control=list(maxit=1000))
## ----echo=TRUE, results='hide', eval=FALSE------------------------------------
# library(parallel)
# ncores = detectCores() - 1 # number of cores to be used
# cl = makeCluster(ncores)
# # this is slower than sequential for very fast models (like this one)
# calib = calibrate(par=rep(0.5, 5), fn=sphereN,
# replicates=3,
# lower=rep(-5, 5),
# upper=rep(+5, 5),
# phases=c(1,1,1,2,3),
# control=list(parallel=TRUE, ncores=ncores))
# stopCluster(cl) # close the parallel connections
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