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Sort a double-precision floating-point strided array using insertion sort.
npm install @stdlib/blas-ext-base-dsortins
var dsortins = require( '@stdlib/blas-ext-base-dsortins' );
Sorts a double-precision floating-point strided array x
using insertion sort.
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );
dsortins( x.length, 1.0, x, 1 );
// x => <Float64Array>[ -4.0, -2.0, 1.0, 3.0 ]
The function has the following parameters:
- N: number of indexed elements.
-
order: sort order. If
order < 0.0
, the input strided array is sorted in decreasing order. Iforder > 0.0
, the input strided array is sorted in increasing order. Iforder == 0.0
, the input strided array is left unchanged. -
x: input
Float64Array
. - stride: index increment.
The N
and stride
parameters determine which elements in x
are accessed at runtime. For example, to sort every other element
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );
dsortins( 2, -1.0, x, 2 );
// x => <Float64Array>[ 3.0, -2.0, 1.0, -4.0 ]
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float64Array = require( '@stdlib/array-float64' );
// Initial array...
var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
// Create an offset view...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
// Sort every other element...
dsortins( 2, -1.0, x1, 2 );
// x0 => <Float64Array>[ 1.0, 4.0, 3.0, 2.0 ]
Sorts a double-precision floating-point strided array x
using insertion sort and alternative indexing semantics.
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );
dsortins.ndarray( x.length, 1.0, x, 1, 0 );
// x => <Float64Array>[ -4.0, -2.0, 1.0, 3.0 ]
The function has the following additional parameters:
- offset: starting index.
While typed array
views mandate a view offset based on the underlying buffer
, the offset
parameter supports indexing semantics based on a starting index. For example, to access only the last three elements of x
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );
dsortins.ndarray( 3, 1.0, x, 1, x.length-3 );
// x => <Float64Array>[ 1.0, -2.0, 3.0, -6.0, -4.0, 5.0 ]
- If
N <= 0
ororder == 0.0
, both functions returnx
unchanged. - The algorithm distinguishes between
-0
and+0
. When sorted in increasing order,-0
is sorted before+0
. When sorted in decreasing order,-0
is sorted after+0
. - The algorithm sorts
NaN
values to the end. When sorted in increasing order,NaN
values are sorted last. When sorted in decreasing order,NaN
values are sorted first. - The algorithm has space complexity
O(1)
and worst case time complexityO(N^2)
. - The algorithm is efficient for small strided arrays (typically
N <= 20
) and is particularly efficient for sorting strided arrays which are already substantially sorted. - The algorithm is stable, meaning that the algorithm does not change the order of strided array elements which are equal or equivalent (e.g.,
NaN
values). - The input strided array is sorted in-place (i.e., the input strided array is mutated).
var filledarrayBy = require( '@stdlib/array-filled-by' );
var uniform = require( '@stdlib/random-base-uniform' ).factory;
var dsortins = require( '@stdlib/blas-ext-base-dsortins' );
var x = filledarrayBy( 100, 'float64', uniform( -100.0, 100.0 ) );
console.log( x );
dsortins( x.length, -1.0, x, -1 );
console.log( x );
-
@stdlib/blas-ext/base/dsort2ins
: simultaneously sort two double-precision floating-point strided arrays based on the sort order of the first array using insertion sort. -
@stdlib/blas-ext/base/gsortins
: sort a strided array using insertion sort. -
@stdlib/blas-ext/base/ssortins
: sort a single-precision floating-point strided array using insertion sort.
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.
See LICENSE.
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