rsc3/doc-schelp/HelpSource/Classes/Pprob.scrbl

#lang scribble/manual
@(require (for-label racket))

@title{Pprob}
 random values with arbitrary probability distribution@section{related}
  Classes/Ppoisson
@section{categories}
  Streams-Patterns-Events>Patterns>Random

@section{description}


Creates an integral table on instantiation (cpu intensive) which is then used by the streams to generate random values efficiently.

@section{ClassMethods}
 

@section{method}
 new

@section{argument}
 distribution
desired probability distribution (histogram).

@section{argument}
 lo
lower bound of the resulting values.

@section{argument}
 hi
upper bound of the resulting values.

@section{argument}
 length
number of values to repeat.

@section{argument}
 tableSize
resample table to this size. If the size of the distribution is smaller than 64, it is (linearly) resampled to this minimum size.

@section{argument}
 distribution
set the distribution, the table is recalculated.

@section{argument}
 tableSize
set the resample size, the table is recalculated.

@section{Examples}
 


@racketblock[
// a consistency test
(
var a = Pprob([0,0,0,0,1,1,1,1,3,3,6,6,9].scramble);
var b = a.asStream;
b.nextN(800).sort.plot("sorted distribution");
b.nextN(800).sort.plot("sorted distribution, again");
)


// comparison: emulate a linrand
(
var a, b, x, y;
a = Pprob([1, 0]);
x = Pfunc({ 1.0.linrand });

b = a.asStream;
y = x.asStream;

postf("Pprob mean: % linrand mean: % \n", b.nextN(800).mean, y.nextN(800).mean);

b.nextN(800).sort.plot("this is Pprob");
y.nextN(800).sort.plot("this is linrand");
)


// compare efficiency

bench { Pprob([0, 1]) } // this is fairly expensive
bench { 16.do { Pseq([0, 1] ! 32) } }

x = Pprob([0, 1]).asStream;
y = Pseq([0, 1], inf).asStream;

bench { 100.do { x.next } }; // this very efficient
bench { 100.do { y.next } };



// sound example
(
SynthDef(\help_sinegrain,
	{ arg out=0, freq=440, sustain=0.05;
		var env;
		env = EnvGen.kr(Env.perc(0.01, sustain, 0.2), doneAction: Done.freeSelf);
		Out.ar(out, SinOsc.ar(freq, 0, env))
	}).add;
)


(
var t;
a = Pprob([0, 0, 1, 0, 1, 1, 0, 0], 60, 80);
t = a.asStream;
Routine({
	loop({
	Synth(\help_sinegrain, [\freq, t.next.midicps]);
	0.01.wait;
	})
}).play;
)

a.distribution = [0, 1];
a.distribution = [1, 0];
a.distribution = [0, 0, 0, 0, 1, 0];
a.distribution = [0, 1, 0, 0, 0, 0];

// higher resolution results in a more accurate distribution:
a.tableSize = 512;
a.tableSize = 2048;
::
]