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Split a double-precision floating-point number into a normalized fraction and an integer power of two.

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frexp

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Split a double-precision floating-point number into a normalized fraction and an integer power of two.

Usage

import frexp from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-frexp@esm/index.mjs';

You can also import the following named exports from the package:

import { assign } from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-frexp@esm/index.mjs';

frexp( x )

Splits a double-precision floating-point number into a normalized fraction and an integer power of two.

var out = frexp( 4.0 );
// returns [ 0.5, 3 ]

By default, the function returns the normalized fraction and the exponent as a two-element array. The normalized fraction and exponent satisfy the relation x = frac * 2^exp.

import pow from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-pow@esm/index.mjs';

var x = 4.0;
var out = frexp( x );
// returns [ 0.5, 3 ]

var frac = out[ 0 ];
var exp = out[ 1 ];

var bool = ( x === frac * pow(2.0, exp) );
// returns true

If provided positive or negative zero, NaN, or positive or negative infinity, the function returns a two-element array containing the input value and an exponent equal to 0.

var out = frexp( 0.0 );
// returns [ 0.0, 0 ]

out = frexp( -0.0 );
// returns [ -0.0, 0 ]

out = frexp( NaN );
// returns [ NaN, 0 ]

out = frexp( Infinity );
// returns [ Infinity, 0 ]

out = frexp( -Infinity );
// returns [ -Infinity, 0 ]

For all other numeric input values, the absolute value of the normalized fraction resides on the interval [0.5,1).

frexp.assign( x, out, stride, offset )

Splits a double-precision floating-point number into a normalized fraction and an integer power of two and assigns results to a provided output array.

import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';

var out = new Float64Array( 2 );

var y = frexp.assign( 4.0, out, 1, 0 );
// returns <Float64Array>[ 0.5, 3 ]

var bool = ( y === out );
// returns true

Notes

  • Care should be taken when reconstituting a double-precision floating-point number from a normalized fraction and an exponent. For example,

    import pow from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-pow@esm/index.mjs';
    
    var x = 8.988939926493918e+307; // x ~ 2^1023
    
    var out = frexp( x );
    // returns [ 0.5000263811533315, 1024 ]
    
    // Naive reconstitution:
    var y = out[ 0 ] * pow( 2.0, out[ 1 ] );
    // returns Infinity
    
    // Account for 2^1024 evaluating as infinity by recognizing 2^1024 = 2^1 * 2^1023:
    y = out[ 0 ] * pow( 2.0, out[1]-1023 ) * pow( 2.0, 1023 );
    // returns 8.988939926493918e+307

Examples

<!DOCTYPE html>
<html lang="en">
<body>
<script type="module">

import randu from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-randu@esm/index.mjs';
import round from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-round@esm/index.mjs';
import pow from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-pow@esm/index.mjs';
import BIAS from 'https://cdn.jsdelivr.net/gh/stdlib-js/constants-float64-exponent-bias@esm/index.mjs';
import frexp from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-frexp@esm/index.mjs';

var sign;
var frac;
var exp;
var x;
var f;
var v;
var i;

// Generate random numbers and break each into a normalized fraction and an integer power of two...
for ( i = 0; i < 100; i++ ) {
    if ( randu() < 0.5 ) {
        sign = -1.0;
    } else {
        sign = 1.0;
    }
    frac = randu() * 10.0;
    exp = round( randu()*616.0 ) - 308;
    x = sign * frac * pow( 10.0, exp );
    f = frexp( x );
    if ( f[ 1 ] > BIAS ) {
        v = f[ 0 ] * pow( 2.0, f[1]-BIAS ) * pow( 2.0, BIAS );
    } else {
        v = f[ 0 ] * pow( 2.0, f[ 1 ] );
    }
    console.log( '%d = %d * 2^%d = %d', x, f[ 0 ], f[ 1 ], v );
}

</script>
</body>
</html>

See Also


Notice

This package is part of stdlib, a standard library 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.

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See LICENSE.

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