chore: minor updates

Neerajpathak07 · Neerajpathak07 · commit b0b0799c5537 · 2025-01-31T22:27:46.000+05:30
diff --git a/lib/node_modules/@stdlib/stats/base/dists/beta/pdf/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/dists/beta/pdf/benchmark/benchmark.js
@@ -55,7 +55,7 @@ bench( pkg, function benchmark( b ) {
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = pdf( x[ i%100 ], alpha[ i%100 ], beta[ i%100 ] );
+		y = pdf( x[ i % len ], alpha[ i % len ], beta[ i % len ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -89,7 +89,7 @@ bench( pkg+':factory', function benchmark( b ) {
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = mypdf( x[ i%100 ] );
+		y = mypdf( x[ i % len ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}
diff --git a/lib/node_modules/@stdlib/stats/base/dists/beta/pdf/benchmark/benchmark.native.js b/lib/node_modules/@stdlib/stats/base/dists/beta/pdf/benchmark/benchmark.native.js
@@ -64,7 +64,7 @@ bench( pkg+'::native', opts, function benchmark( b ) {
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = pdf( x[ i%100 ], alpha[ i%100 ], beta[ i%100 ] );
+		y = pdf( x[ i % len ], alpha[ i % len ], beta[ i % len ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}
diff --git a/lib/node_modules/@stdlib/stats/base/dists/beta/pdf/benchmark/c/benchmark.c b/lib/node_modules/@stdlib/stats/base/dists/beta/pdf/benchmark/c/benchmark.c
@@ -102,13 +102,13 @@ static double benchmark( void ) {
 	int i;
 
 	for ( i = 0; i < 100; i++ ) {
-		alpha[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 2.0 );
+		alpha[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 100.0 );
 	}
 	for ( i = 0; i < 100; i++ ) {
 		beta[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 100.0 );
 	}
 	for ( i = 0; i < 100; i++ ) {
-		x[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 100.0 );
+		x[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 2.0 );
 	}
 
 	t = tic();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/beta/pdf/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/beta/pdf/src/main.c
@@ -25,7 +25,7 @@
 #include "stdlib/constants/float64/pinf.h"
 
 /**
-* Evaluates the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta` at a value `x`.
+* Evaluates the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta`.
 *
 * @param x        input value
 * @param alpha    first shape parameter

Original file line number	Diff line number	Diff line change
`@@ -64,7 +64,7 @@ bench( pkg+'::native', opts, function benchmark( b ) {`
`64`	`64`
`65`	`65`	`b.tic();`
`66`	`66`	`for ( i = 0; i < b.iterations; i++ ) {`
`67`		`- y = pdf( x[ i%100 ], alpha[ i%100 ], beta[ i%100 ] );`
	`67`	`+ y = pdf( x[ i % len ], alpha[ i % len ], beta[ i % len ] );`
`68`	`68`	`if ( isnan( y ) ) {`
`69`	`69`	`b.fail( 'should not return NaN' );`
`70`	`70`	`}`
Original file line number	Diff line number	Diff line change
`@@ -102,13 +102,13 @@ static double benchmark( void ) {`
`102`	`102`	`int i;`
`103`	`103`
`104`	`104`	`for ( i = 0; i < 100; i++ ) {`
`105`		`- alpha[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 2.0 );`
	`105`	`+ alpha[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 100.0 );`
`106`	`106`	`}`
`107`	`107`	`for ( i = 0; i < 100; i++ ) {`
`108`	`108`	`beta[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 100.0 );`
`109`	`109`	`}`
`110`	`110`	`for ( i = 0; i < 100; i++ ) {`
`111`		`- x[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 100.0 );`
	`111`	`+ x[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 2.0 );`
`112`	`112`	`}`
`113`	`113`
`114`	`114`	`t = tic();`
Original file line number	Diff line number	Diff line change
`@@ -25,7 +25,7 @@`
`25`	`25`	`#include "stdlib/constants/float64/pinf.h"`
`26`	`26`
`27`	`27`	`/**`
`28`		-* Evaluates the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta` at a value `x`.
	`28`	+* Evaluates the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta`.
`29`	`29`	`*`
`30`	`30`	`* @param x input value`
`31`	`31`	`* @param alpha first shape parameter`