Package Exports
- @stdlib/constants-float64-eps
- @stdlib/constants-float64-eps/lib/index.js
This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@stdlib/constants-float64-eps) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.
Readme
EPS
Difference between one and the smallest value greater than one that can be represented as a double-precision floating-point number.
Epsilon is defined as
where b
is the radix (base) and p
is the precision (number of radix bits in the significand). For double-precision floating-point numbers, b
is 2
and p
is 53
.
Installation
npm install @stdlib/constants-float64-eps
Usage
var EPS = require( '@stdlib/constants-float64-eps' );
EPS
Difference between one and the smallest value greater than one that can be represented as a double-precision floating-point number.
var bool = ( EPS === 2.220446049250313e-16 );
// returns true
Examples
var abs = require( '@stdlib/math-base-special-abs' );
var max = require( '@stdlib/math-base-special-max' );
var randu = require( '@stdlib/random-base-randu' );
var EPS = require( '@stdlib/constants-float64-eps' );
var bool;
var a;
var b;
var i;
function isApprox( a, b ) {
var delta;
var tol;
delta = abs( a - b );
tol = EPS * max( abs( a ), abs( b ) );
return ( delta <= tol );
}
for ( i = 0; i < 100; i++ ) {
a = randu() * 10.0;
b = a + (randu()*5.0e-15) - 2.5e-15;
bool = isApprox( a, b );
console.log( '%d %s approximately equal to %d. Delta: %d.', a, ( bool ) ? 'is' : 'is not', b, abs( a - b ) );
}
C APIs
Usage
#include "stdlib/constants/float64/eps.h"
STDLIB_CONSTANT_FLOAT64_EPS
Macro for the difference between one and the smallest value greater than one that can be represented as a double-precision floating-point number.
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.
Community
License
See LICENSE.
Copyright
Copyright © 2016-2022. The Stdlib Authors.