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‎lib/node_modules/@stdlib/math/base/special/gammainc/README.md‎

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<!-- /.examples -->
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<!-- C interface documentation. -->
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* * *
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<section class="c">
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## C APIs
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<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
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<section class="intro">
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</section>
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<!-- /.intro -->
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<!-- C usage documentation. -->
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<section class="usage">
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### Usage
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```c
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#include "stdlib/math/base/special/gammainc.h"
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```
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#### stdlib_base_gammainc( x, a, regularized, upper )
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Computes the regularized lower [incomplete gamma function][incomplete-gamma-function] for inputs `x` and `s`. .
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```c
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double out = stdlib_base_gammainc( 1.0, 2.0, 1, 1 );
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// returns ~0.7358
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```
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The function accepts the following arguments:
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- **x**: `[in] double` input value.
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- **a**: `[in] double` input value.
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- **regularized**: `[in] CBLAS_INT` indicating if the function should evaluate the regularized or non-regularized incomplete gamma functions.
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- **upper**: `[in] CBLAS_INT` indicating if the function should return the upper tail of the incomplete gamma function
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```c
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double stdlib_base_gammainc( double x, double a, CBLAS_INT regularized, CBLAS_INT upper );
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```
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</section>
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<!-- /.usage -->
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<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<!-- C API usage examples. -->
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<section class="examples">
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### Examples
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```c
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#include "stdlib/math/base/special/gammainc.h"
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#include <stdio.h>
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int main( void ) {
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const double x[] = { 6.0, 1.0, 7.0, 7.0, 0.0/0.0, 6.0 };
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const double a[] = { 2.0, 2.0, 5.0, 5.0, 2.0, 0.0/0.0 };
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const int regularized[] = { 1, 1, 1, 0, 1, 1 };
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const int upper[] = { 0, 1, 0, 0, 0, 0 };
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double y;
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int i;
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for ( i = 0; i <6; i++ ) {
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y = stdlib_base_gammainc( x[ i ], a[ i ], regularized[ i ], upper[ i ] );
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printf( "gammainc( %lf, %lf, %lf, %lf ) = %lf\n", x[ i ], a[ i ], regularized[ i ], upper[ i ], y );
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}
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}
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```
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</section>
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<!-- /.examples -->
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</section>
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<!-- /.c -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">

‎lib/node_modules/@stdlib/math/base/special/gammainc/benchmark/benchmark.native.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2018 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var resolve = require( 'path' ).resolve;
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var bench = require( '@stdlib/bench' );
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var randu = require( '@stdlib/random/base/randu' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var EPS = require( '@stdlib/constants/float64/eps' );
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var tryRequire = require( '@stdlib/utils/try-require' );
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var pkg = require( './../package.json' ).name;
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// VARIABLES //
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var gammainc = tryRequire( resolve( __dirname, './../lib/native.js' ) );
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var opts = {
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'skip': ( gammainc instanceof Error )
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};
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// MAIN //
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bench( pkg+'::native', opts, function benchmark( b ) {
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var x;
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var y;
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*1000.0 ) - 0.0;
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y = ( randu()*1000.0 ) + EPS;
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z = gammainc( x, y, 1, 0 );
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});
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bench( pkg+'::native:regularized=true', opts, function benchmark( b ) {
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var x;
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var y;
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*1000.0 ) - 0.0;
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y = ( randu()*1000.0 ) + EPS;
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z = gammainc( x, y, 1, 0 );
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});
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bench( pkg+'::native:regularized=false', opts, function benchmark( b ) {
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var x;
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var y;
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*1000.0 ) - 0.0;
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y = ( randu()*1000.0 ) + EPS;
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z = gammainc( x, y, 0, 0 );
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});
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bench( pkg+'::native:regularized=true,upper=true', opts, function benchmark( b ) {
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var x;
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var y;
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*1000.0 ) - 0.0;
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y = ( randu()*1000.0 ) + EPS;
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z = gammainc( x, y, 1, 1 );
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});
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bench( pkg+'::native:regularized=true,upper=false', opts, function benchmark( b ) {
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var x;
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var y;
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*1000.0 ) - 0.0;
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y = ( randu()*1000.0 ) + EPS;
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z = gammainc( x, y, 1, 0 );
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});
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bench( pkg+'::native:regularized=false,upper=true', opts, function benchmark( b ) {
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var x;
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var y;
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*1000.0 ) - 0.0;
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y = ( randu()*1000.0 ) + EPS;
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z = gammainc( x, y, 0, 1 );
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});
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bench( pkg+'::native:regularized=false,upper=false', opts, function benchmark( b ) {
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var x;
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var y;
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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x = ( randu()*1000.0 ) - 0.0;
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y = ( randu()*1000.0 ) + EPS;
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z = gammainc( x, y, 0, 0 );
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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});

‎lib/node_modules/@stdlib/math/base/special/gammainc/examples/c/example.c‎

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int main( void ) {
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const double x[] = { 6.0, 1.0, 7.0, 7.0, 0.0/0.0, 6.0 };
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const double a[] = { 2.0, 2.0, 5.0, 5.0, 2.0, 0.0/0.0 };
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const double regularized[] = { 1.0, 1.0, 1.0, 0.0, 1.0, 1.0 };
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const double upper[] = { 0.0, 1.0, 0.0, 0.0, 0.0, 0.0 };
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const double regularized[] = { 1, 1, 1, 0, 1, 1 };
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const double upper[] = { 0, 1, 0, 0, 0, 0 };
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double y;
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int i;

‎lib/node_modules/@stdlib/math/base/special/gammainc/include/stdlib/math/base/special/gammainc.h‎

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#ifndef STDLIB_MATH_BASE_SPECIAL_GAMMAINC_H
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#define STDLIB_MATH_BASE_SPECIAL_GAMMAINC_H
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#include "stdlib/blas/base/shared.h"
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/*
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* If C++, prevent name mangling so that the compiler emits a binary file having undecorated names, thus mirroring the behavior of a C compiler.
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*/
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/**
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* Computes the regularized incomplete gamma function. The upper tail is calculated via the modified Lentz's method for computing continued fractions, the lower tail using a power expansion.
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*/
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double stdlib_base_gammainc( double x, double a, double regularized, double upper );
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double stdlib_base_gammainc( double x, double a, CBLAS_INT regularized, CBLAS_INT upper );
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#ifdef __cplusplus
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}

‎lib/node_modules/@stdlib/math/base/special/gammainc/lib/native.js‎

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*/
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function gammainc( x, a, regularized, upper ) {
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if (regularized) {
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regularized = 1.0;
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regularized = 1;
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} else {
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regularized = 0.0;
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regularized = 0;
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}
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if (upper) {
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upper = 1.0;
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upper = 1;
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} else {
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upper = 0.0;
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upper = 0;
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}
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return addon( x, a, regularized, upper );
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}

‎lib/node_modules/@stdlib/math/base/special/gammainc/manifest.json‎

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"libraries": [],
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"libpath": [],
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"dependencies": [
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"@stdlib/napi/argv",
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"@stdlib/blas/base/shared",
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"@stdlib/napi/argv-double",
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"@stdlib/napi/argv-int32",
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"@stdlib/napi/create-double",
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"@stdlib/napi/export",
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"@stdlib/math/base/special/gamma-lanczos-sum-expg-scaled",
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"@stdlib/math/base/special/gammaln",
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"@stdlib/math/base/special/gamma",
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"@stdlib/constants/float64/max-ln",
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"@stdlib/constants/float64/min-ln",
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"@stdlib/constants/float64/gamma-lanczos-g",
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"@stdlib/constants/float64/e",
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"@stdlib/math/base/napi/quaternary"
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"@stdlib/constants/float64/e"
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]
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},
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{
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"libraries": [],
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"libpath": [],
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"dependencies": [
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"@stdlib/blas/base/shared",
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"@stdlib/math/base/special/gamma-lanczos-sum-expg-scaled",
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"@stdlib/math/base/special/gammaln",
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"@stdlib/math/base/special/gamma",
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"libraries": [],
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"libpath": [],
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"dependencies": [
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"@stdlib/blas/base/shared",
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"@stdlib/math/base/special/gamma-lanczos-sum-expg-scaled",
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"@stdlib/math/base/special/gammaln",
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"@stdlib/math/base/special/gamma",

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