#!/usr/bin/perl
# This is a quick program to calculate pi using the Monte Carlo method
+.
# I recomend inputting  a value for $cycles greater that 1000. 
# I am working on a detailed explanation of how and why this works. 
# I will add it as soon as I'm done.
use strict;
open(PI, ">>pi.dat") || die "pi.dat";
my ($i, $j, $yespi, $pi) = 1;
my $cycles = 1;
srand;
while ($j <= 100000) {
    $cycles = $j;
    while ($i <= $cycles) {
    my ($x, $y, $cdnt) = 1;
    $x = rand;
    $y = rand;
    $cdnt = $x**2 + $y**2;
    if ($cdnt <= 1) {
        ++$yespi;
    }    
    $i=$i + 10;
    $pi = ($yespi / ($cycles / 10)) * 4; # since I add 10 every time.
    print PI "$cycles $pi\n";
    }
$j = $j + 10;
}
close(PI) || die "pi.dat";
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I'm not sure if setting srand would really help the situation. In order for the Monte Carlo method to be most effective the random numbers produced must be the most evenly spread out. In fact, you can be more accurate without random numbers and instead going through all four corners, then their mid-points, and their mid-points' mid-points, e.g. :

.   .      . . .
       ->  . . .  ->  etc.
.   .      . . .
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However, if you would like to use random numbers you need one of two types of random numbers. You either need true even-bias random numbers (pseudo-random can work but often don't :) ). Or you can use a particular kind of random numbers that are designed not to repeat and are garunteed to be spread evenly and more and more closely together (very similar to the systematic approach above).

Two examples are Hamilton and Sobol sequences. If you use them, you get a 1/N convergence, instead of a 1/N^2 (which you get for uniform numbers since there is nothing that keeps them from clumping up). I was going to give below "Numerical Recipe's in C"'s version of Antonov-Saleev's variant on Sobol's sequence. However it's simply too long. Also, I have to wake up for work tomorrow, and the first version I typed in (converting it to Perl from C) didn't work just right. Oh well.

Good luck, Hamilton sequences are much easier to do.

Ciao,
Gryn

Nope, letting perl set srand is good enough. And you are right about Monte Carlo needing a purer random base than a pseudo-random generator. Most Monte Carlo's use scads of randoms per cycle and you loop the psuedo random in 2**31 calls on most systems and 2**15 on some.

Look to CPAN and you will find some of what you need. First off, if you are going to write your own "random" sequence generator you my find PDL handy. If you want someone else to do the work on random numbers try Math::Random or Math::TrulyRandom but I would recommend you find an OS specific random source like Linux's /dev/random and /dev/urandom

#!/usr/bin/perl -w
use strict;
#use Linux; ## I wish. =) (yeah yeah, $^O, etc etc)

open UR, "</dev/urandom" or die "Oh man, your system sucks, $!";

my $pages= shift()+0 || 1;

die "Give me a number greater than 0 or nothing, bub.\n" unless $pages
+>0;

while ($pages-->0) {
        my $buf;
        read UR, $buf, 512 or die "Ouch that shouldn't happen, $!";
        for (0..31) {
                print vec($buf,$_*4,32), "\t",
                        vec($buf,$_*4+1,32), "\t",
                        vec($buf,$_*4+2,32), "\t",
                        vec($buf,$_*4+3,32), "\n";
        }
}
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--
$you = new YOU;
honk() if $you->love(perl)

A trick I have mentioned before and may again.

If you need a large supply of random looking data, one of the best approaches is to grab a large file produced out of dynamic data (/dev/mem is often a good bet, there are plenty of choices though), compress it, and then encrypt it with a good algorithm. Samples from the resulting file are for all intents and purposes, random.

I did a search on CPAN for a quasi-random number generation module, but sadly could not find one. I will try the systematic dot approch that you mentioned, but this has interested me very much. Can you please tell me how I could go about writing a quasi-random number generator? Building a module will be good practice anyway, even if I can go without it ;)