Whomever, I recently sent the "bug report" below toR-core at r-project.org and have just been asked to instead submit it to you. Although I am basically not an R user, I have installed version 3.3.1 and am also the author of a statistics program written in Visual Basic that contains a component which correctly implements the Mersenne Twister (MT) algorithm. I believe that it is not possible to generate the correct stream of pseudorandom numbers using the MT default random number generator in R, and am not the first person to notice this. Here is a posted 2013 entry (www.r-bloggers.com/reproducibility-and-randomness/) on an R website that asserts that the SAS computer program implementation of the MT algorithm produces different numbers than R does when using the same starting seed number. The author of this post didn?t get anyone to respond to his query about the reason for this SAS vs. R discrepancy. There are two ways of initializing the original MT computer program (written in C) so that an identical stream of numbers can be repeatedly generated: 1) with a particular integer seed number, and 2) with a particular array of integers. In the 'compilation and usage' section of this webpage (https://github.com/cslarsen/mersenne-twister) there is a listing of the first 200 random numbers the MT algorithm should produce for seed number = 1. The inventors of the Mersenne Twister random number generator provided two different sets of the first 1000 numbers produced by a correctly coded 32-bit implementation of the MT algorithm when initializing it with a particular array of integers at: www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.out. [There is a link to this output at: www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html.] My statistics program obtains exactly those 200 numbers from the first site mentioned in the previous paragraph and also obtains those same numbers from the second website (though I didn't check all 2000 values). Assuming that the MT code within R uses the 32-bit MT algorithm, I suspect that the current version of R can't do that. If you (i.e., anyone who might knowledgeably respond to this report) is able to duplicate those reference test-values, then please send me the R code to initialize the MT code within R to successfully do that, and I apologize for having wasted your time. If you (collectively) can't do that, then R is very likely using incorrectly implemented MT code. And if this latter possibility is true, it seems to me that this is something that should be fixed. Mark Roberts, Ph.D. [[alternative HTML version deleted]]
Try comparing the streams for when the 625-integer versions of the seeds are identical. (R's seed is 626 integers: omit the first value, which indicates which random number generator the seed is for.). I find the the MKL Mersenne Twister results match R's (with occassional differences in the last bit) when the 625-integer seeds the same. I believe R fiddles with the single-integer seed to spread it out a bit. S's seed was taken modulo 1024 so old users tended not use use single-seeds bigger than 1023. Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Aug 30, 2016 at 2:45 PM, Mark Roberts <ersatz.too at gmail.com> wrote:> Whomever, > > I recently sent the "bug report" below toR-core at r-project.org and have > just been asked to instead submit it to you. > > Although I am basically not an R user, I have installed version 3.3.1 > and am also the author of a statistics program written in Visual Basic > that contains a component which correctly implements the Mersenne > Twister (MT) algorithm. I believe that it is not possible to generate > the correct stream of pseudorandom numbers using the MT default random > number generator in R, and am not the first person to notice this. Here > is a posted 2013 entry > (www.r-bloggers.com/reproducibility-and-randomness/) on an R website > that asserts that the SAS computer program implementation of the MT > algorithm produces different numbers than R does when using the same > starting seed number. The author of this post didn?t get anyone to > respond to his query about the reason for this SAS vs. R discrepancy. > > There are two ways of initializing the original MT computer program > (written in C) so that an identical stream of numbers can be repeatedly > generated: 1) with a particular integer seed number, and 2) with a > particular array of integers. In the 'compilation and usage' section > of this webpage (https://github.com/cslarsen/mersenne-twister) there is > a listing of the first 200 random numbers the MT algorithm should > produce for seed number = 1. The inventors of the Mersenne Twister > random number generator provided two different sets of the first 1000 > numbers produced by a correctly coded 32-bit implementation of the MT > algorithm when initializing it with a particular array of integers at: > www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.out. > [There is a link to this output at: > www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html.] > > My statistics program obtains exactly those 200 numbers from the first > site mentioned in the previous paragraph and also obtains those same > numbers from the second website (though I didn't check all 2000 values). > Assuming that the MT code within R uses the 32-bit MT algorithm, I > suspect that the current version of R can't do that. If you (i.e., > anyone who might knowledgeably respond to this report) is able to > duplicate those reference test-values, then please send me the R code to > initialize the MT code within R to successfully do that, and I apologize > for having wasted your time. If you (collectively) can't do that, then R > is very likely using incorrectly implemented MT code. And if this > latter possibility is true, it seems to me that this is something that > should be fixed. > > Mark Roberts, Ph.D. > > [[alternative HTML version deleted]] > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel[[alternative HTML version deleted]]
I don't see evidence of a bug. There have been several versions of the MT; we may be using a different version than you are. Ours is the 1999/10/28 version; the web page you cite uses one from 2002. Perhaps the newer version fixes some problems, and then it would be worth considering a change. But changing the default RNG definitely introduces problems in reproducibility, so it's not obvious that we would do it. Duncan Murdoch On 30/08/2016 5:45 PM, Mark Roberts wrote:> Whomever, > > I recently sent the "bug report" below toR-core at r-project.org and have > just been asked to instead submit it to you. > > Although I am basically not an R user, I have installed version 3.3.1 > and am also the author of a statistics program written in Visual Basic > that contains a component which correctly implements the Mersenne > Twister (MT) algorithm. I believe that it is not possible to generate > the correct stream of pseudorandom numbers using the MT default random > number generator in R, and am not the first person to notice this. Here > is a posted 2013 entry > (www.r-bloggers.com/reproducibility-and-randomness/) on an R website > that asserts that the SAS computer program implementation of the MT > algorithm produces different numbers than R does when using the same > starting seed number. The author of this post didn?t get anyone to > respond to his query about the reason for this SAS vs. R discrepancy. > > There are two ways of initializing the original MT computer program > (written in C) so that an identical stream of numbers can be repeatedly > generated: 1) with a particular integer seed number, and 2) with a > particular array of integers. In the 'compilation and usage' section > of this webpage (https://github.com/cslarsen/mersenne-twister) there is > a listing of the first 200 random numbers the MT algorithm should > produce for seed number = 1. The inventors of the Mersenne Twister > random number generator provided two different sets of the first 1000 > numbers produced by a correctly coded 32-bit implementation of the MT > algorithm when initializing it with a particular array of integers at: > www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.out. > [There is a link to this output at: > www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html.] > > My statistics program obtains exactly those 200 numbers from the first > site mentioned in the previous paragraph and also obtains those same > numbers from the second website (though I didn't check all 2000 values). > Assuming that the MT code within R uses the 32-bit MT algorithm, I > suspect that the current version of R can't do that. If you (i.e., > anyone who might knowledgeably respond to this report) is able to > duplicate those reference test-values, then please send me the R code to > initialize the MT code within R to successfully do that, and I apologize > for having wasted your time. If you (collectively) can't do that, then R > is very likely using incorrectly implemented MT code. And if this > latter possibility is true, it seems to me that this is something that > should be fixed. > > Mark Roberts, Ph.D. > > [[alternative HTML version deleted]] > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel >
On 30 August 2016 at 18:29, Duncan Murdoch wrote:
| I don't see evidence of a bug. There have been several versions of the
| MT; we may be using a different version than you are. Ours is the
| 1999/10/28 version; the web page you cite uses one from 2002.
|
| Perhaps the newer version fixes some problems, and then it would be
| worth considering a change. But changing the default RNG definitely
| introduces problems in reproducibility, so it's not obvious that we
| would do it.
Yep. FWIW the GNU GSL adopted the 2002 version a while ago too. Quoting from
https://www.gnu.org/software/gsl/manual/html_node/Random-number-generator-algorithms.html
Generator: gsl_rng_mt19937
The MT19937 generator of Makoto Matsumoto and Takuji Nishimura is a
variant of the twisted generalized feedback shift-register algorithm, and
is known as the ?Mersenne Twister? generator. It has a Mersenne prime
period of 2^19937 - 1 (about 10^6000) and is equi-distributed in 623
dimensions. It has passed the DIEHARD statistical tests. It uses 624 words
of state per generator and is comparable in speed to the other
generators. The original generator used a default seed of 4357 and
choosing s equal to zero in gsl_rng_set reproduces this. Later versions
switched to 5489 as the default seed, you can choose this explicitly via
gsl_rng_set instead if you require it.
For more information see,
Makoto Matsumoto and Takuji Nishimura, ?Mersenne Twister: A
623-dimensionally equidistributed uniform pseudorandom number
generator?. ACM Transactions on Modeling and Computer Simulation,
Vol. 8, No. 1 (Jan. 1998), Pages 3?30 The generator gsl_rng_mt19937
uses the second revision of the seeding procedure published by the two
authors above in 2002. The original seeding procedures could cause
spurious artifacts for some seed values. They are still available
through the alternative generators gsl_rng_mt19937_1999 and
gsl_rng_mt19937_1998.
Note the last sentence here.
This is all somewhat technical code, so when I noticed the above I could
never figure what exactly R was doing in its implementation. But "innocent
until proven guilty" -- a sufficient number of people ought to have looked
at
this -- so I saw no need to pursue this further.
Dirk
--
http://dirk.eddelbuettel.com | @eddelbuettel | edd at debian.org
On 08/30/2016 06:29 PM, Duncan Murdoch wrote:> I don't see evidence of a bug. There have been several versions of the > MT; we may be using a different version than you are. Ours is the > 1999/10/28 version; the web page you cite uses one from 2002. > > Perhaps the newer version fixes some problems, and then it would be > worth considering a change. But changing the default RNG definitely > introduces problems in reproducibility,Well "problems in reproducibility" is a bit vague. Results would always be reproducible by specifying kind="Mersenne-Twister" or kind="Buggy Kinderman-Ramage" for older results, so there is no problem reproducing results. The only problem is that users expecting to reproduce results twenty years later will need to know what random generator they used. (BTW, they may also need to record information about the normal or other generator, as well as the seed.) Of course, these changes are recorded pretty well for R, so the history of "default" can always be found. I think it is a mistake to encourage users into thinking they do not need to keep track of some information if they want reproducibility. Perhaps the default should be changed more often in order to encourage better user habits. More seriously, I think "default" should continue to be something that is currently considered to be good. So, if there really is a known problem, then I think "default" should be changed. (And, no I did not get burned by the R 1.7.0 change in the default generator. I got burned by a much earlier, unadvertised, and more subtle change in the Splus generator.) Paul Gilbert so it's not obvious that we> would do it. > > Duncan Murdoch > > > On 30/08/2016 5:45 PM, Mark Roberts wrote: >> Whomever, >> >> I recently sent the "bug report" below toR-core at r-project.org and have >> just been asked to instead submit it to you. >> >> Although I am basically not an R user, I have installed version 3.3.1 >> and am also the author of a statistics program written in Visual Basic >> that contains a component which correctly implements the Mersenne >> Twister (MT) algorithm. I believe that it is not possible to generate >> the correct stream of pseudorandom numbers using the MT default random >> number generator in R, and am not the first person to notice this. Here >> is a posted 2013 entry >> (www.r-bloggers.com/reproducibility-and-randomness/) on an R website >> that asserts that the SAS computer program implementation of the MT >> algorithm produces different numbers than R does when using the same >> starting seed number. The author of this post didn?t get anyone to >> respond to his query about the reason for this SAS vs. R discrepancy. >> >> There are two ways of initializing the original MT computer program >> (written in C) so that an identical stream of numbers can be repeatedly >> generated: 1) with a particular integer seed number, and 2) with a >> particular array of integers. In the 'compilation and usage' section >> of this webpage (https://github.com/cslarsen/mersenne-twister) there is >> a listing of the first 200 random numbers the MT algorithm should >> produce for seed number = 1. The inventors of the Mersenne Twister >> random number generator provided two different sets of the first 1000 >> numbers produced by a correctly coded 32-bit implementation of the MT >> algorithm when initializing it with a particular array of integers at: >> www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.out. >> [There is a link to this output at: >> www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html.] >> >> My statistics program obtains exactly those 200 numbers from the first >> site mentioned in the previous paragraph and also obtains those same >> numbers from the second website (though I didn't check all 2000 values). >> Assuming that the MT code within R uses the 32-bit MT algorithm, I >> suspect that the current version of R can't do that. If you (i.e., >> anyone who might knowledgeably respond to this report) is able to >> duplicate those reference test-values, then please send me the R code to >> initialize the MT code within R to successfully do that, and I apologize >> for having wasted your time. If you (collectively) can't do that, then R >> is very likely using incorrectly implemented MT code. And if this >> latter possibility is true, it seems to me that this is something that >> should be fixed. >> >> Mark Roberts, Ph.D. >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-devel at r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel >> > > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel