@stdlib/assert-is-unity-probability-array NPM

isUnityProbabilityArray

Test if a value is an array of probabilities that sum to one.

Installation

npm install @stdlib/assert-is-unity-probability-array

Usage

var isUnityProbabilityArray = require( '@stdlib/assert-is-unity-probability-array' );

isUnityProbabilityArray( value )

Tests if a value is an array of probabilities that sum to one.

var Uint8Array = require( '@stdlib/array-uint8' );

var bool = isUnityProbabilityArray( [ 0.25, 0.5, 0.25 ] );
// returns true

bool = isUnityProbabilityArray( new Uint8Array( [ 0, 1 ] ) );
// returns true

bool = isUnityProbabilityArray( [ 3.14, 0.0 ] );
// returns false

Notes

Summation of finite-precision floating-point numbers often has numerical error. For example,
```
var arr = [ 0.1, 0.2, 0.1, 0.1, 0.2, 0.2, 0.1 ]; // => 1.0
var sum = 0.0;
var i;
for ( i = 0; i < arr.length; i++ ) {
    sum += arr[ i ];
}
console.log( sum );
// => 0.9999999999999999
```
To account for numerical error, the function tests if array elements sum to approximately one; specifically,
```
1.0 - sqrt(eps) <= sum(A) <= 1.0 + sqrt(eps)
```
where eps is double-precision floating-point epsilon (~2.22e-16) and sqrt(eps) ~ 1.49e-8. The above comparison ensures equality for approximately half the significand bits.

Examples

var Uint8Array = require( '@stdlib/array-uint8' );
var isUnityProbabilityArray = require( '@stdlib/assert-is-unity-probability-array' );

var arr = [ 0.0, 1.0 ];
var bool = isUnityProbabilityArray( arr );
// returns true

arr = [ 0.5, 0.25, 0.25 ];
bool = isUnityProbabilityArray( arr );
// returns true

arr = new Uint8Array( [ 0, 0, 1, 0 ] );
bool = isUnityProbabilityArray( arr );
// returns true

arr = [ 0.4, 0.4, 0.4 ];
bool = isUnityProbabilityArray( arr );
// returns false

arr = [ 3.14, -1.0 ];
bool = isUnityProbabilityArray( arr );
// returns false

bool = isUnityProbabilityArray( [] );
// returns false

bool = isUnityProbabilityArray( null );
// returns false

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.