https://github.com/cran/fOptions
Tip revision: 9c58bb98a62fa8226a1f41c6fb1f98485b101250 authored by Rmetrics Core Team on 08 August 1977, 00:00:00 UTC
version 270.74
version 270.74
Tip revision: 9c58bb9
LowDiscrepancy.R
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
# Copyrights (C)
# for this R-port:
# 1999 - 2004, Diethelm Wuertz, GPL
# Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
# info@rmetrics.org
# www.rmetrics.org
# for the code accessed (or partly included) from other R-ports:
# see R's copyright and license files
# for the code accessed (or partly included) from contributed R-ports
# and other sources
# see Rmetrics's copyright file
################################################################################
# FUNCTION: DESCRIPTION:
# runif.pseudo Uniform Pseudo Random number sequence
# rnorm.pseudo Normal Pseudo Random number sequence
# runif.halton Uniform Halton low discrepancy sequence
# rnorm.halton Normal Halton low discrepancy sequence
# runif.sobol Uniform Sobol low discrepancy sequence
# rnorm.sobol Normal Sobol low discrepancy sequence
################################################################################
runif.pseudo =
function(n, dimension, init = NULL)
{ # A function implemented by Diethelm Wuertz
# Description:
# Uniform Pseudo Random number sequence
# FUNCTION:
# Deviates:
result = matrix(runif(n*dimension), ncol = dimension)
# Return Value:
result
}
# ------------------------------------------------------------------------------
rnorm.pseudo =
function(n, dimension, init = TRUE)
{ # A function implemented by Diethelm Wuertz
# Description:
# Normal Pseudo Random number sequence
# FUNCTION:
# Deviates:
result = matrix(rnorm(n*dimension), ncol = dimension)
# Return Value:
result
}
# -----------------------------------------------------------------------------
runif.halton =
function (n, dimension, init = TRUE)
{ # A function implemented by Diethelm Wuertz
# Description:
# Uniform Halton Low Discrepancy Sequence
# Details:
# DIMENSION : dimension <= 200
# N : LD numbers to create
# FUNCTION:
# Restart Settings:
if (init) {
## YC : this code should not needed since we have now global Env
# .warn = options()$warn
# options(warn = -1)
# rm(".runif.halton.seed")
# options(warn = .warn)
.setfOptionsEnv(.runif.halton.seed = list(base = rep(0, dimension), offset = 0))
}
# Generate:
qn = rep(0, n*dimension)
# SUBROUTINE HALTON(QN, N, DIMEN, BASE, OFFSET, INIT, TRANSFORM)
result = .Fortran("halton",
as.double(qn),
as.integer(n),
as.integer(dimension),
as.integer(.getfOptionsEnv(".runif.halton.seed")$base),
as.integer(.getfOptionsEnv(".runif.halton.seed")$offset),
as.integer(init),
as.integer(0),
PACKAGE = "fOptions")
# For the next numbers save:
### .warn = options()$warn
### options(warn = -1)
### rm(".runif.halton.seed")
### options(warn = .warn)
.setfOptionsEnv(.runif.halton.seed = list(base = result[[4]], offset = result[[5]]))
# Deviates:
result = matrix(result[[1]], ncol = dimension)
# Return Value:
result
}
# ------------------------------------------------------------------------------
rnorm.halton =
function (n, dimension, init = TRUE)
{ # A function implemented by Diethelm Wuertz
# Description:
# Normal Halton Low Discrepancy Sequence
# Details:
# DIMENSION : dimension <= 200
# N : LD numbers to create
# FUNCTION:
# Restart Settings:
if (init) {
### .warn = options()$warn
### options(warn = -1)
### rm(".rnorm.halton.seed")
### options(warn = .warn)
.setfOptionsEnv(.rnorm.halton.seed = list(base = rep(0, dimension), offset = 0))
}
# Generate:
qn = rep(0, n*dimension)
# SUBROUTINE HALTON(QN, N, DIMEN, BASE, OFFSET, INIT, TRANSFORM)
result = .Fortran("halton",
as.double(qn),
as.integer(n),
as.integer(dimension),
as.integer(.getfOptionsEnv(".rnorm.halton.seed")$base),
as.integer(.getfOptionsEnv(".rnorm.halton.seed")$offset),
as.integer(init),
as.integer(1),
PACKAGE = "fOptions")
# For the next numbers save:
### .warn = options()$warn
### options(warn = -1)
### rm(".rnorm.halton.seed")
### options(warn = .warn)
.setfOptionsEnv(.rnorm.halton.seed = list(base = result[[4]], offset = result[[5]]))
# Deviates:
result = matrix(result[[1]], ncol = dimension)
# Return Value:
result
}
# -----------------------------------------------------------------------------
runif.sobol =
function (n, dimension, init = TRUE, scrambling = 0, seed = 4711)
{ # A function implemented by Diethelm Wuertz
# Description:
# Uniform Sobol Low Discrepancy Sequence
# Details:
# DIMENSION : dimension <= 200
# N : LD numbers to create
# SCRAMBLING : One of the numbers 0,1,2,3
#
# FUNCTION:
# Restart Settings:
if (init) {
### .warn = options()$warn
### options(warn = -1)
### rm(".runif.sobol.seed")
### options(warn = .warn)
.setfOptionsEnv(.runif.sobol.seed = list(quasi = rep(0, dimension), ll = 0,
count = 0, sv = rep(0, dimension*30), seed = seed))
}
# Generate:
qn = rep(0.0, n*dimension)
# SSOBOL(QN,N,DIMEN,QUASI,LL,COUNT,SV,IFLAG,SEED,INIT,TRANSFORM)
result = .Fortran("sobol",
as.double(qn),
as.integer(n),
as.integer(dimension),
as.double (.getfOptionsEnv(".runif.sobol.seed")$quasi),
as.integer(.getfOptionsEnv(".runif.sobol.seed")$ll),
as.integer(.getfOptionsEnv(".runif.sobol.seed")$count),
as.integer(.getfOptionsEnv(".runif.sobol.seed")$sv),
as.integer(scrambling),
as.integer(.getfOptionsEnv(".runif.sobol.seed")$seed),
as.integer(init),
as.integer(0),
PACKAGE = "fOptions")
# For the next numbers save:
### .warn = options()$warn
### options(warn = -1)
### rm(".runif.sobol.seed")
### options(warn = .warn)
.setfOptionsEnv(.runif.sobol.seed = list(quasi = result[[4]], ll = result[[5]],
count = result[[6]], sv = result[[7]], seed = result[[9]]))
# Deviates:
result = matrix(result[[1]], ncol = dimension)
# Return Value:
result
}
# ------------------------------------------------------------------------------
rnorm.sobol =
function (n, dimension, init = TRUE, scrambling = 0, seed = 4711)
{ # A function implemented by Diethelm Wuertz
# Description:
# Normal Sobol Low Discrepancy Sequence
# Details:
# DIMENSION : dimension <= 200
# N : LD numbers to create
# SCRAMBLING : One of the numbers 0,1,2,3
# FUNCTION:
# Restart Settings:
if (init) {
### .warn = options()$warn
### options(warn = -1)
### rm(".rnorm.sobol.seed")
### options(warn = .warn)
.setfOptionsEnv(.rnorm.sobol.seed = list( quasi = rep(0, dimension), ll = 0,
count = 0, sv = rep(0, dimension*30), seed = seed))
}
# Generate:
qn = rep(0.0, n*dimension)
# SSOBOL(QN,N,DIMEN,QUASI,LL,COUNT,SV,IFLAG,SEED,INIT,TRANSFORM)
result = .Fortran("sobol",
as.double(qn),
as.integer(n),
as.integer(dimension),
as.double (.getfOptionsEnv(".rnorm.sobol.seed")$quasi),
as.integer(.getfOptionsEnv(".rnorm.sobol.seed")$ll),
as.integer(.getfOptionsEnv(".rnorm.sobol.seed")$count),
as.integer(.getfOptionsEnv(".rnorm.sobol.seed")$sv),
as.integer(scrambling),
as.integer(.getfOptionsEnv(".rnorm.sobol.seed")$seed),
as.integer(init),
as.integer(1),
PACKAGE = "fOptions")
# For the next numbers save:
### .warn = options()$warn
### options(warn = -1)
### rm(".rnorm.sobol.seed")
### options(warn = .warn)
.setfOptionsEnv(.rnorm.sobol.seed = list(quasi = result[[4]], ll = result[[5]],
count = result[[6]], sv = result[[7]], seed = result[[9]]))
# Deviates:
result = matrix(result[[1]], ncol = dimension)
# Return Value:
result
}
################################################################################