From fbe1c1a09f437a248e8eeee7ad379d78b71c7830 Mon Sep 17 00:00:00 2001
From: hrshya <harshyadav6078@gmail.com>
Date: Fri, 16 Jan 2026 17:44:13 +0530
Subject: [PATCH] bench: update random value generation

---
 .../lognormal/ctor/benchmark/benchmark.js     | 219 +++++++++---------
 1 file changed, 108 insertions(+), 111 deletions(-)

diff --git a/lib/node_modules/@stdlib/stats/base/dists/lognormal/ctor/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/dists/lognormal/ctor/benchmark/benchmark.js
index 99d3bf74fa48..f86b6519754f 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/lognormal/ctor/benchmark/benchmark.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/lognormal/ctor/benchmark/benchmark.js
@@ -21,8 +21,7 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var uniform = require( '@stdlib/random/base/uniform' );
-var Float64Array = require( '@stdlib/array/float64' );
+var uniform = require( '@stdlib/random/array/uniform' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var EPS = require( '@stdlib/constants/float64/eps' );
 var pkg = require( './../package.json' ).name;
@@ -34,21 +33,19 @@ var LogNormal = require( './../lib' );
 bench( pkg+'::instantiation', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var i;
 
-	len = 100;
-	mu = new Float64Array( len );
-	sigma = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		mu[ i ] = uniform( EPS, 10.0 );
-		sigma[ i ] = uniform( EPS, 10.0 );
-	}
+	opts = {
+		'dtype': 'float64'
+	};
+	mu = uniform( 100, EPS, 10.0, opts );
+	sigma = uniform( 100, EPS, 10.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist = new LogNormal( mu[ i % len ], sigma[ i % len ] );
+		dist = new LogNormal( mu[ i % mu.length ], sigma[ i % sigma.length ] );
 		if ( !( dist instanceof LogNormal ) ) {
 			b.fail( 'should return a distribution instance' );
 		}
@@ -90,7 +87,7 @@ bench( pkg+'::get:mu', function benchmark( b ) {
 bench( pkg+'::set:mu', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var y;
 	var i;
@@ -98,16 +95,16 @@ bench( pkg+'::set:mu', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	y = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		y[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	y = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = y[ i % len ];
-		if ( dist.mu !== y[ i % len ] ) {
+		dist.mu = y[ i % y.length ];
+		if ( dist.mu !== y[ i % y.length ] ) {
 			b.fail( 'should return set value' );
 		}
 	}
@@ -148,7 +145,7 @@ bench( pkg+'::get:sigma', function benchmark( b ) {
 bench( pkg+'::set:sigma', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var y;
 	var i;
@@ -156,16 +153,16 @@ bench( pkg+'::set:sigma', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	y = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		y[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	y = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.sigma = y[ i % len ];
-		if ( dist.sigma !== y[ i % len ] ) {
+		dist.sigma = y[ i % y.length ];
+		if ( dist.sigma !== y[ i % y.length ] ) {
 			b.fail( 'should return set value' );
 		}
 	}
@@ -180,7 +177,7 @@ bench( pkg+'::set:sigma', function benchmark( b ) {
 bench( pkg+':entropy', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -189,15 +186,15 @@ bench( pkg+':entropy', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = x[ i % len ];
+		dist.mu = x[ i % x.length ];
 		y = dist.entropy;
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
@@ -214,7 +211,7 @@ bench( pkg+':entropy', function benchmark( b ) {
 bench( pkg+':kurtosis', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -223,15 +220,15 @@ bench( pkg+':kurtosis', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = x[ i % len ];
+		dist.mu = x[ i % x.length ];
 		y = dist.kurtosis;
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
@@ -248,7 +245,7 @@ bench( pkg+':kurtosis', function benchmark( b ) {
 bench( pkg+':mean', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -257,15 +254,15 @@ bench( pkg+':mean', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = x[ i % len ];
+		dist.mu = x[ i % x.length ];
 		y = dist.mean;
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
@@ -282,7 +279,7 @@ bench( pkg+':mean', function benchmark( b ) {
 bench( pkg+':median', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -291,15 +288,15 @@ bench( pkg+':median', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = x[ i % len ];
+		dist.mu = x[ i % x.length ];
 		y = dist.median;
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
@@ -316,7 +313,7 @@ bench( pkg+':median', function benchmark( b ) {
 bench( pkg+':mode', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -325,15 +322,15 @@ bench( pkg+':mode', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( 1.0 + EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS + 1.0, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = x[ i % len ];
+		dist.mu = x[ i % x.length ];
 		y = dist.mode;
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
@@ -350,7 +347,7 @@ bench( pkg+':mode', function benchmark( b ) {
 bench( pkg+':skewness', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -359,15 +356,15 @@ bench( pkg+':skewness', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = x[ i % len ];
+		dist.mu = x[ i % x.length ];
 		y = dist.skewness;
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
@@ -384,7 +381,7 @@ bench( pkg+':skewness', function benchmark( b ) {
 bench( pkg+':stdev', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -393,15 +390,15 @@ bench( pkg+':stdev', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = x[ i % len ];
+		dist.mu = x[ i % x.length ];
 		y = dist.stdev;
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
@@ -418,7 +415,7 @@ bench( pkg+':stdev', function benchmark( b ) {
 bench( pkg+':variance', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -427,15 +424,15 @@ bench( pkg+':variance', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, EPS, 100.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		dist.mu = x[ i % len ];
+		dist.mu = x[ i % x.length ];
 		y = dist.variance;
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
@@ -452,7 +449,7 @@ bench( pkg+':variance', function benchmark( b ) {
 bench( pkg+':cdf', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -461,15 +458,15 @@ bench( pkg+':cdf', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( -3.0, 3.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, -3.0, 3.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = dist.cdf( x[ i % len ] );
+		y = dist.cdf( x[ i % x.length ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -485,7 +482,7 @@ bench( pkg+':cdf', function benchmark( b ) {
 bench( pkg+':logpdf', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -494,15 +491,15 @@ bench( pkg+':logpdf', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( -3.0, 3.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, -3.0, 3.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = dist.logpdf( x[ i % len ] );
+		y = dist.logpdf( x[ i % x.length ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -518,7 +515,7 @@ bench( pkg+':logpdf', function benchmark( b ) {
 bench( pkg+':pdf', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -527,15 +524,15 @@ bench( pkg+':pdf', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( -3.0, 3.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, -3.0, 3.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = dist.pdf( x[ i % len ] );
+		y = dist.pdf( x[ i % x.length ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -551,7 +548,7 @@ bench( pkg+':pdf', function benchmark( b ) {
 bench( pkg+':quantile', function benchmark( b ) {
 	var sigma;
 	var dist;
-	var len;
+	var opts;
 	var mu;
 	var x;
 	var y;
@@ -560,15 +557,15 @@ bench( pkg+':quantile', function benchmark( b ) {
 	mu = 2.0;
 	sigma = 3.0;
 	dist = new LogNormal( mu, sigma );
-	len = 100;
-	x = new Float64Array( len );
-	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( 0.0, 1.0 );
-	}
+
+	opts = {
+		'dtype': 'float64'
+	};
+	x = uniform( 100, 0.0, 1.0, opts );
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = dist.quantile( x[ i % len ] );
+		y = dist.quantile( x[ i % x.length ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}