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bench: refactor random number generation in stats/base/dists/negative-binomial #5025

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bench: refactor random number generation in stats/base/dists/negative-binomial #5025

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1ead8c3
feat : Refactor random number generation in JS benchmarks
yuvi-mittal Feb 2, 2025
bbc9cdf
linting errors
yuvi-mittal Feb 4, 2025
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30 changes: 22 additions & 8 deletions lib/node_modules/@stdlib/stats/base/dists/negative-binomial/cdf/benchmark/benchmark.js
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Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,9 @@
// MODULES //

var bench = require( '@stdlib/bench' );
var ceil = require( '@stdlib/math/base/special/ceil' );
var randu = require( '@stdlib/random/base/randu' );
var discreteUniform = require( '@stdlib/random/base/discrete-uniform' );
var uniform = require( '@stdlib/random/base/uniform' );
var Float64Array = require( '@stdlib/array/float64' );
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var EPS = require( '@stdlib/constants/float64/eps' );
var pkg = require( './../package.json' ).name;
Expand All @@ -32,18 +33,26 @@ var cdf = require( './../lib' );
// MAIN //

bench( pkg, function benchmark( b ) {
var len;
var r;
var p;
var x;
var y;
var i;

len = 100;
x = new Float64Array( len );
r = new Float64Array( len );
p = new Float64Array( len );
for ( i = 0; i < len; i++ ) {
x[ i ] = uniform( 0, 100.0 );
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r[ i ] = discreteUniform( 1.0, 100 );
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Suggested change
r[ i ] = discreteUniform( 1.0, 100 );
r[ i ] = discreteUniform( 1, 100 );

p[ i ] = uniform( EPS, 1.0 );
}

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
x = randu()*100.0;
r = ceil( randu()*100.0 );
p = ( randu()*1.0 ) + EPS;
y = cdf( x, r, p );
y = cdf( x[ i % len ], r[ i % len ], p[ i % len ] );
if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
Expand All @@ -58,6 +67,7 @@ bench( pkg, function benchmark( b ) {

bench( pkg+':factory', function benchmark( b ) {
var mycdf;
var len;
var r;
var p;
var x;
Expand All @@ -67,11 +77,15 @@ bench( pkg+':factory', function benchmark( b ) {
r = 80;
p = 0.4;
mycdf = cdf.factory( r, p );
len = 100;
x = new Float64Array( len );
for ( i = 0; i < len; i++ ) {
x[ i ] = uniform( 0.0, 100.0 );
}

b.tic();
for ( i = 0; i < b.iterations; i++ ) {
x = ( randu()*100 );
y = mycdf( x );
y = mycdf( x[ i % len ] );
if ( isnan( y ) ) {
b.fail( 'should not return NaN' );
}
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