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### Re^7: Randomly biased, random numbers.

by educated_foo (Vicar)
 on Dec 06, 2013 at 07:04 UTC ( #1065911=note: print w/replies, xml ) Need Help??

in reply to Re^6: Randomly biased, random numbers.
in thread Randomly biased, random numbers.

Sorry -- it's much terser than Perl for math.
```unifrnd(MIN, MAX, ROWS, COLUMNS); # uniform random numbers in [MIN,MAX
+)
cumsum(VECTOR) = #cumulative sum of vector
# .OP = element-wise application of OP to operands

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Re^8: Randomly biased, random numbers.
by BrowserUk (Pope) on Dec 06, 2013 at 09:13 UTC

Is this fx(ix) (roughly) equivalent to this: @fx[ @ix ]?

If it is, then I don't understand how you can index the 10-element fx with the 1e5-element ix?

With the rise and rise of 'Social' network sites: 'Computers are making people easier to use everyday'
Examine what is said, not who speaks -- Silence betokens consent -- Love the truth but pardon error.
"Science is about questioning the status quo. Questioning authority".
In the absence of evidence, opinion is indistinguishable from prejudice.
Is this fx(ix) (roughly) equivalent to this: @fx[ @ix ]?
Yes. @ix contains 1e5 numbers all between 1 and 10 (Octave/Matlab is 1-based), so it chooses 1e5 elements from fx.

Okay. This is my brute force conversion from MatLab to perl, and a simple test harness.:

```#! perl -slw
use strict;
use Data::Dump qw[ pp ];
use List::Util qw[ reduce ]; \$a = \$b;
use GD;
use constant { X => 0, Y=> 1, R => 2 };

sub rgb2n{  unpack 'N', pack 'CCCC', 0, @_ }

my \$RED     = rgb2n( 255, 0, 0 );
my \$GREEN   = rgb2n( 0, 255, 0 );
my \$BLUE    = rgb2n( 0, 0, 255 );
my \$YELLOW  = rgb2n( 255, 255, 0 );
my \$MAGENTA = rgb2n( 255, 0, 255 );
my \$CYAN    = rgb2n( 0, 255, 255 );
my \$WHITE   = rgb2n( 255,255,255 );

## Brute force from MatLab code node:1065900
## fx = [0,cumsum(unifrnd(0,1,1,10))];
my @fx = @{ reduce( sub{ push @\$a, \$a->[-1]+\$b; \$a }, [ 0 ], map{ 1+ra
+nd 10 } 1..10 ) };

## tmp=unifrnd(1,10,1,1e5);
my @tmp = map{ 1+ rand 10 } 1 .. 1e5;

## ix=floor(tmp);
my @ix = map int, @tmp;

## dx=rem(tmp,1);
my @dx = map \$_-int(\$_), @tmp;

## values = (fx(ix) + (fx(ix+1)-fx(ix)).*dx)./fx(end-1);
my @values = map{
( \$fx[ \$ix[\$_] ] + ( \$fx[ \$ix[\$_+1] ] - \$fx[ \$ix[\$_] ] ) * \$dx[\$_]
+ ) / \$fx[-1]
} 0 .. \$#ix-1;

our \$N //= 100;
our \$X = our \$Y //= 800;

## pick points from values
my @points = map[
int( \$values[ rand @values ]*\$X ), int( \$values[ rand @values ]*\$Y
+ )
], 1 .. \$N;

my \$im = GD::Image->new( 1000, 1000, 1 );
\$im->fill( 0, 0, \$WHITE );
\$im->rectangle( 100, 100, 900, 900, 0 );

\$im->filledArc( 100+\$_->[X], 100+\$_->[Y], 5, 5, 0, 360, \$RED ) for @po
+ints;

open PNG, '>:raw', "\$0.png" or die \$!;
print PNG \$im->png;
close PNG;
system "\$0.png";

Does the conversion look right? Am I using the values correctly?

It produces datasets like this which doesn't appear to demonstrate much in the way of clumping. What did I do wrong?

With the rise and rise of 'Social' network sites: 'Computers are making people easier to use everyday'
Examine what is said, not who speaks -- Silence betokens consent -- Love the truth but pardon error.
"Science is about questioning the status quo. Questioning authority".
In the absence of evidence, opinion is indistinguishable from prejudice.

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