chore: updated double with int32_t

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Author: 0PrashantYadav0

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/README.md

@@ -187,12 +187,12 @@ double out = stdlib_base_dists_binomial_pmf( 3.0, 20, 0.2 );
 
 The function accepts the following arguments:
 
--   **x**: `[in] int` input value.
--   **n**: `[in] int` number of trials.
+-   **x**: `[in] int32_t` input value.
+-   **n**: `[in] int32_t` number of trials.
 -   **p**: `[in] double` success probability.
 
 ```c
-double stdlib_base_dists_binomial_pmf( const double x, const double n, const double p );
+double stdlib_base_dists_binomial_pmf( const int32_t x, const int32_t n, const double p );
 ```
 
 </section>
@@ -217,6 +217,7 @@ double stdlib_base_dists_binomial_pmf( const double x, const double n, const dou
 #include "stdlib/stats/base/dists/binomial/pmf.h"
 #include "stdlib/math/base/special/ceil.h"
 #include <stdlib.h>
+#include <stdint.h>
 #include <stdio.h>
 
 static double random_uniform( const double min, const double max ) {
@@ -225,18 +226,18 @@ static double random_uniform( const double min, const double max ) {
 }
 
 int main( void ) {
-    double n;
+    int32_t n;
     double p;
-    double x;
+    int32_t x;
     double y;
     int i;
 
     for ( i = 0; i < 25; i++ ) {
-        n = stdlib_base_ceil( random_uniform( 0, 100 ) );
-        p = random_uniform( 0, 1 );
-        x = random_uniform( 0, 50 );
+        n = stdlib_base_ceil( random_uniform( 0.0, 100.0 ) );
+        p = random_uniform( 0.0, 1.0 );
+        x = stdlib_base_ceil( random_uniform( 0.0, 50.0 ) );
         y = stdlib_base_dists_binomial_pmf( x, n, p );
-        printf( "x: %lf, n: %lf, p: %lf, P(X=x;n,p): %lf\n", x, n, p, y );
+        printf( "x: %d, n: %d, p: %lf, P(X=x;n,p): %lf\n", x, n, p, y );
     }
 
     return 0;

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/benchmark/benchmark.js

@@ -21,6 +21,7 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
+var Int32Array = require( '@stdlib/array/int32' );
 var Float64Array = require( '@stdlib/array/float64' );
 var ceil = require( '@stdlib/math/base/special/ceil' );
 var randu = require( '@stdlib/random/base/randu' );
@@ -40,13 +41,13 @@ bench( pkg, function benchmark( b ) {
 	var i;
 
 	len = 100;
-	n = new Float64Array( len );
+	n = new Int32Array( len );
 	p = new Float64Array( len );
-	x = new Float64Array( len );
+	x = new Int32Array( len );
 	for ( i = 0; i < len; i++ ) {
 		n[ i ] = ceil( randu()*100.0 );
 		p[ i ] = randu();
-		x[ i ] = randu()*50.0;
+		x[ i ] = ceil( randu()*50.0 );
 	}
 
 	b.tic();

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/benchmark/benchmark.native.js

@@ -22,6 +22,7 @@
 
 var resolve = require( 'path' ).resolve;
 var bench = require( '@stdlib/bench' );
+var Int32Array = require( '@stdlib/array/int32' );
 var Float64Array = require( '@stdlib/array/float64' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var ceil = require( '@stdlib/math/base/special/ceil' );
@@ -49,13 +50,13 @@ bench( pkg+'::native', opts, function benchmark( b ) {
 	var i;
 
 	len = 100;
-	n = new Float64Array( len );
+	n = new Int32Array( len );
 	p = new Float64Array( len );
-	x = new Float64Array( len );
+	x = new Int32Array( len );
 	for ( i = 0; i < len; i++ ) {
 		n[ i ] = ceil( randu()*100.0 );
 		p[ i ] = randu();
-		x[ i ] = randu()*50.0;
+		x[ i ] = ceil( randu()*50.0 );
 	}
 
 	b.tic();

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/benchmark/c/benchmark.c

@@ -19,6 +19,7 @@
 #include "stdlib/stats/base/dists/binomial/pmf.h"
 #include "stdlib/math/base/special/ceil.h"
 #include <stdlib.h>
+#include <stdint.h>
 #include <stdio.h>
 #include <math.h>
 #include <time.h>
@@ -94,17 +95,17 @@ static double random_uniform( const double min, const double max ) {
 */
 static double benchmark( void ) {
 	double elapsed;
-	double n[ 100 ];
+	int32_t n[ 100 ];
 	double p[ 100 ];
-	double x[ 100 ];
+	int32_t x[ 100 ];
 	double y;
 	double t;
 	int i;
 
 	for ( i = 0; i < 100; i++ ) {
-		n[ i ] = stdlib_base_ceil( random_uniform( 0, 100 ) );
-		p[ i ] = random_uniform( 0, 1 );
-		x[ i ] = random_uniform( 0, 50);
+		n[ i ] = stdlib_base_ceil( random_uniform( 0.0, 100.0 ) );
+		p[ i ] = random_uniform( 0.0, 1.0 );
+		x[ i ] = stdlib_base_ceil( random_uniform( 0.0, 50.0) );
 	}
 
 	t = tic();

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/examples/c/example.c

@@ -19,6 +19,7 @@
 #include "stdlib/stats/base/dists/binomial/pmf.h"
 #include "stdlib/math/base/special/ceil.h"
 #include <stdlib.h>
+#include <stdint.h>
 #include <stdio.h>
 
 static double random_uniform( const double min, const double max ) {
@@ -27,18 +28,18 @@ static double random_uniform( const double min, const double max ) {
 }
 
 int main( void ) {
-	double n;
+	int32_t n;
 	double p;
-	double x;
+	int32_t x;
 	double y;
 	int i;
 
 	for ( i = 0; i < 25; i++ ) {
-		n = stdlib_base_ceil( random_uniform( 0, 100 ) );
-		p = random_uniform( 0, 1 );
-		x = random_uniform( 0, 50 );
+		n = stdlib_base_ceil( random_uniform( 0.0, 100.0 ) );
+		p = random_uniform( 0.0, 1.0 );
+		x = stdlib_base_ceil( random_uniform( 0.0, 50.0 ) );
 		y = stdlib_base_dists_binomial_pmf( x, n, p );
-		printf( "x: %lf, n: %lf, p: %lf, P(X=x;n,p): %lf\n", x, n, p, y );
+		printf( "x: %d, n: %d, p: %lf, P(X=x;n,p): %lf\n", x, n, p, y );
 	}
 
 	return 0;

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/include/stdlib/stats/base/dists/binomial/pmf.h

@@ -27,9 +27,9 @@ extern "C" {
 #endif
 
 /**
-* Returns the probability mass function (PMF) of a binomial distribution.
+* Returns the probability mass function (PMF) for a binomial distribution with number of trials `n` and success probability `p` at a value `x`.
 */
-double stdlib_base_dists_binomial_pmf( const double x, const double n, const double p );
+double stdlib_base_dists_binomial_pmf( const int32_t x, const int32_t n, const double p );
 
 #ifdef __cplusplus
 }

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/lib/native.js

@@ -51,22 +51,10 @@ var addon = require( './../src/addon.node' );
 * // returns ~0.006
 *
 * @example
-* var y = pmf( NaN, 20, 0.5 );
-* // returns NaN
-*
-* @example
-* var y = pmf( 0.0, NaN, 0.5 );
-* // returns NaN
-*
-* @example
 * var y = pmf( 0.0, 20, NaN );
 * // returns NaN
 *
 * @example
-* var y = pmf( 2.0, 1.5, 0.5 );
-* // returns NaN
-*
-* @example
 * var y = pmf( 2.0, -2.0, 0.5 );
 * // returns NaN
 *

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/src/addon.c

@@ -20,4 +20,4 @@
 #include "stdlib/math/base/napi/ternary.h"
 
 // cppcheck-suppress shadowFunction
-STDLIB_MATH_BASE_NAPI_MODULE_DDD_D( stdlib_base_dists_binomial_pmf );
+STDLIB_MATH_BASE_NAPI_MODULE_IID_D( stdlib_base_dists_binomial_pmf );

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/src/main.c

@@ -24,33 +24,30 @@
 #include "stdlib/math/base/special/log1p.h"
 #include "stdlib/math/base/special/ln.h"
 #include "stdlib/math/base/special/exp.h"
+#include <stdint.h>
 
 /**
 * Evaluates the probability mass function (PMF) for a binomial distribution with number of trials `n` and success probability `p` at a value `x`.
 *
 * @param x  input value
 * @param n  number of trials
 * @param p  success probability
-* @return   evaluated logPMF
+* @return   evaluated PMF
 *
 * @example
 * double y = stdlib_base_dists_binomial_pmf( 3.0, 20, 0.2 );
 * // returns ~0.205
 */
-double stdlib_base_dists_binomial_pmf( const double x, const double n, const double p ) {
+double stdlib_base_dists_binomial_pmf( const int32_t x, const int32_t n, const double p ) {
 	if (
-		stdlib_base_is_nan( x ) ||
-		stdlib_base_is_nan( n ) ||
 		stdlib_base_is_nan( p ) ||
 		n < 0.0 ||
 		p < 0.0 ||
-		p > 1.0 ||
-		!stdlib_base_is_nonnegative_integer( n ) ||
-		n == STDLIB_CONSTANT_FLOAT64_PINF
+		p > 1.0
 	) {
 		return 0.0 / 0.0;
 	}
-	if ( stdlib_base_is_nonnegative_integer( x ) ){
+	if ( x >= 0 ){
 		if ( x > n ) {
 			return 0.0;
 		}

lib/node_modules/@stdlib/stats/base/dists/binomial/pmf/test/test.native.js

@@ -55,18 +55,14 @@ tape( 'main export is a function', function test( t ) {
 });
 
 tape( 'if provided `NaN` for any parameter, the function returns `NaN`', opts, function test( t ) {
-	var y = pmf( NaN, 20, 0.5 );
-	t.equal( isnan( y ), true, 'returns NaN' );
-	y = pmf( 0.0, NaN, 0.5 );
-	t.equal( isnan( y ), true, 'returns NaN' );
+	var y;
 	y = pmf( 0.0, 20, NaN );
 	t.equal( isnan( y ), true, 'returns NaN' );
 	t.end();
 });
 
 tape( 'if provided a negative integer for `x` and a valid `n` and `p`, the function returns `0`', opts, function test( t ) {
-	var y = pmf( NINF, 20, 0.5 );
-	t.equal( y, 0.0, 'returns 0' );
+	var y;
 
 	y = pmf( -20.0, 20, 0.5 );
 	t.equal( y, 0.0, 'returns 0' );
@@ -80,37 +76,12 @@ tape( 'if provided a negative integer for `x` and a valid `n` and `p`, the funct
 	t.end();
 });
 
-tape( 'if provided a non-integer for `x` and a valid `n` and `p`, the function returns `0`', opts, function test( t ) {
-	var y = pmf( -1.5, 20, 0.5 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( -0.5, 20, 0.5 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( 1.5, 20, 0.5 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( 2.5, 20, 0.5 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	t.end();
-});
-
 tape( 'if provided `n` which is not a nonnegative integer, the function returns `NaN`', opts, function test( t ) {
 	var y;
 
-	y = pmf( 2.0, 1.5, 0.5 );
-	t.equal( isnan( y ), true, 'returns NaN' );
-
 	y = pmf( 0.0, -1.0, 0.5 );
 	t.equal( isnan( y ), true, 'returns NaN' );
 
-	y = pmf( 2.0, NINF, 0.5 );
-	t.equal( isnan( y ), true, 'returns NaN' );
-
-	y = pmf( 2.0, PINF, 0.5 );
-	t.equal( isnan( y ), true, 'returns NaN' );
-
 	t.end();
 });
 
@@ -142,31 +113,13 @@ tape( 'if `p` or `n` equals `0`, the function evaluates a degenerate distributio
 	y = pmf( 1.0, 8, 0.0 );
 	t.equal( y, 0.0, 'returns 0' );
 
-	y = pmf( PINF, 8, 0.0 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( NINF, 8, 0.0 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( NaN, 8, 0.0 );
-	t.equal( isnan( y ), true, 'returns NaN' );
-
 	// Case: n = 0, p = 0.5
 	y = pmf( 0.0, 0, 0.5 );
 	t.equal( y, 1.0, 'returns 1 for x equal to 0' );
 
 	y = pmf( 1.0, 0, 0.5 );
 	t.equal( y, 0.0, 'returns 0' );
 
-	y = pmf( PINF, 0, 0.5 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( NINF, 0, 0.5 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( NaN, 0, 0.5 );
-	t.equal( isnan( y ), true, 'returns NaN' );
-
 	t.end();
 });
 
@@ -179,15 +132,6 @@ tape( 'if `p` equals `1.0`, the function evaluates a degenerate distribution cen
 	y = pmf( 1.0, 8, 1.0 );
 	t.equal( y, 0.0, 'returns 0' );
 
-	y = pmf( PINF, 8, 1.0 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( NINF, 8, 1.0 );
-	t.equal( y, 0.0, 'returns 0' );
-
-	y = pmf( NaN, 8, 1.0 );
-	t.equal( isnan( y ), true, 'returns NaN' );
-
 	t.end();
 });