Dear R experts,
we are preparing an R-package to compute the Oja Median which contains
some C++ code in which random numbers are needed. To generate the random
numbers we use the following Mersenne-Twister implementation:
// MersenneTwister.h
// Mersenne Twister random number generator -- a C++ class MTRand
// Based on code by Makoto Matsumoto, Takuji Nishimura, and Shawn Cokus
// Richard J. Wagner v1.0 15 May 2003 rjwag...@writeme.com
the random seed for the Mersenne-Twister is provided by our R-function
which gives an (random) integer to the C++ function srand() which in
turn sets the seed in the code.
Using the set.seed in R makes now the results reproducible, but the
results differ between windows and linux.
Does anyone know what the problem there is?
Our suspicion is that the reason is that some libraries are different
implemented on linux and windows (XP) compilers.
After the program start we set the seed in row 447(vkm.cpp) with
srand(int);
When the median will be calculated, an intern seed is set with unsigned
int seed = rand(); ( in row 100 (vkm.cpp)). This seed will be used
to calculate some random subsets and to
create a Mersenne Twister object with MTRand rr(seed); (row 156, vkm.cpp).
The MTRand Object rr is called with an unsigned Integer, so the
important function in the mersenneTwister.h class is in line 87:
MTRand( const uint32& oneSeed );
According to that the Random Number Generator uses the methods
initialize(oneSeed); and reload(); (inside the method, beginning in
line 215)
This both methods (line 283 and line 301) are using beside others
registers. Could it be that there is a different behavior between
Windows and Linux?
We do not want to use only srand() since we might need more then the
number of pseudo random numbers that algorithm can provide.
For those interested and which would like to see the code, a first
version of the package, called OjaMedian, is available as source file
and windows binary on my homepage:
http://www.uta.fi/~klaus.nordhausen/down.html
The problem is in the ojaMedian function when the evolutionary algorithm
is used. Involved C++-files are mainly vkm.cpp and MersenneTwister.h.
We would be very grateful for any advice on how to solve this problem.
(below is also a demonstration)
Thank you very much in advance,
Klaus
Results on windows XP:
Compiler used: gcc version 4.2.1-sjlj (mingw32-2)
library(OjaMedian)
set.seed(1)
testD <- rmvnorm(20,c(0,0))
summary(testD)
V1 V2
Min. :-2.2147 Min. :-1.989352
1st Qu.:-0.3844 1st Qu.:-0.399466
Median : 0.3597 Median :-0.054967
Mean : 0.1905 Mean :-0.006472
3rd Qu.: 0.7590 3rd Qu.: 0.655663
Max. : 1.5953 Max. : 1.358680
set.seed(1)
ojaMedian(testD)
[1] 0.21423705 -0.05799643
sessionInfo()
R version 2.9.0 (2009-04-17)
i386-pc-mingw32
locale:
LC_COLLATE=Finnish_Finland.1252;LC_CTYPE=Finnish_Finland.1252;LC_MONETARY=Finnish_Finland.1252;LC_NUMERIC=C;LC_TIME=Finnish_Finland.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] OjaMedian_0.0-14 ICSNP_1.0-3 ICS_1.2-1 survey_3.14
[5] mvtnorm_0.9-5
loaded via a namespace (and not attached):
[1] tools_2.9.0
Results on Linux Kubuntu 8.10
result of: cat /proc/version:
Linux version 2.6.28-11-generic (bui...@palmer) (gcc version 4.3.3
(Ubuntu 4.3.3-5ubuntu4) ) #42-Ubuntu SMP Fri Apr 17 01:57:59 UTC 2009
library(OjaMedian)
set.seed(1)
testD <- rmvnorm(20,c(0,0))
summary(testD)
V1 V2
Min. :-2.2147 Min. :-1.989352
1st Qu.:-0.3844 1st Qu.:-0.399466
Median : 0.3597 Median :-0.054967
Mean : 0.1905 Mean :-0.006472
3rd Qu.: 0.7590 3rd Qu.: 0.655663
Max. : 1.5953 Max. : 1.358680
set.seed(1)
ojaMedian(testD)
(-0.501381, 0.193929)[1] 0.119149071 0.002732100
sessionInfo()
R version 2.8.1 (2008-12-22)
i486-pc-linux-gnu
locale:
LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] OjaMedian_0.0-14 ICSNP_1.0-3 ICS_1.2-1 survey_3.14
[5] mvtnorm_0.9-5
