@stdlib/math-base-tools-evalpoly-compile NPM

evalpoly

$NPM version$ $Build Status$ $Coverage Status$

Compile a module for evaluating a polynomial.

Installation

npm install @stdlib/math-base-tools-evalpoly-compile

Usage

var compile = require( '@stdlib/math-base-tools-evalpoly-compile' );

compile( c )

Compiles a module string containing an exported function which evaluates a polynomial having coefficients c.

var str = compile( [ 3.0, 2.0, 1.0 ] );
// returns <string>

In the example above, the output string would correspond to the following module:

'use strict';

// MAIN //

/**
* Evaluates a polynomial.
*
* ## Notes
*
* -   The implementation uses [Horner's rule][horners-method] for efficient computation.
*
* [horners-method]: https://en.wikipedia.org/wiki/Horner%27s_method
*
* @private
* @param {number} x - value at which to evaluate the polynomial
* @returns {number} evaluated polynomial
*/
function evalpoly( x ) {
    if ( x === 0.0 ) {
        return 3.0;
    }
    return 3.0 + (x * (2.0 + (x * 1.0))); // eslint-disable-line max-len
}


// EXPORTS //

module.exports = evalpoly;

The coefficients should be ordered in ascending degree, thus matching summation notation.

Notes

The function is intended for non-browser environments for the purpose of generating module files.

Examples

var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var compile = require( '@stdlib/math-base-tools-evalpoly-compile' );

var coef;
var sign;
var str;
var i;

// Create an array of random coefficients...
coef = new Float64Array( 10 );
for ( i = 0; i < coef.length; i++ ) {
    if ( randu() < 0.5 ) {
        sign = -1.0;
    } else {
        sign = 1.0;
    }
    coef[ i ] = sign * round( randu()*100.0 );
}

// Compile a module for evaluating a polynomial:
str = compile( coef );
console.log( str );

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.