107 lines
2.2 KiB
Text
107 lines
2.2 KiB
Text
class:: Pprob
|
|
summary:: random values with arbitrary probability distribution
|
|
related:: Classes/Ppoisson
|
|
categories:: Streams-Patterns-Events>Patterns>Random
|
|
|
|
description::
|
|
|
|
Creates an integral table on instantiation (cpu intensive) which is then used by the streams to generate random values efficiently.
|
|
|
|
ClassMethods::
|
|
|
|
method::new
|
|
|
|
argument::distribution
|
|
desired probability distribution (histogram).
|
|
|
|
argument::lo
|
|
lower bound of the resulting values.
|
|
|
|
argument::hi
|
|
upper bound of the resulting values.
|
|
|
|
argument::length
|
|
number of values to repeat.
|
|
|
|
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.
|
|
|
|
argument::distribution
|
|
set the distribution, the table is recalculated.
|
|
|
|
argument::tableSize
|
|
set the resample size, the table is recalculated.
|
|
|
|
Examples::
|
|
|
|
code::
|
|
// 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;
|
|
::
|