dsort2hp

Simultaneously sort two double-precision floating-point strided arrays based on the sort order of the first array using heapsort.

Installation

npm install @stdlib/blas-ext-base-dsort2hp

Usage

var dsort2hp = require( '@stdlib/blas-ext-base-dsort2hp' );

dsort2hp( N, order, x, strideX, y, strideY )

Simultaneously sorts two double-precision floating-point strided arrays based on the sort order of the first array x using heapsort.

var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );
var y = new Float64Array( [ 0.0, 1.0, 2.0, 3.0 ] );

dsort2hp( x.length, 1.0, x, 1, y, 1 );

console.log( x );
// => <Float64Array>[ -4.0, -2.0, 1.0, 3.0 ]

console.log( y );
// => <Float64Array>[ 3.0, 1.0, 0.0, 2.0 ]

The function has the following parameters:

• N: number of indexed elements.
• order: sort order. If order < 0.0, the input strided array x is sorted in decreasing order. If order > 0.0, the input strided array x is sorted in increasing order. If order == 0.0, the input strided arrays are left unchanged.
• x: first input Float64Array.
• strideX: x index increment.
• y: second input Float64Array.
• strideY: y index increment.

The N and stride parameters determine which elements in x and y are accessed at runtime. For example, to sort every other element

var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );
var y = new Float64Array( [ 0.0, 1.0, 2.0, 3.0 ] );
var N = floor( x.length / 2 );

dsort2hp( N, -1.0, x, 2, y, 2 );

console.log( x );
// => <Float64Array>[ 3.0, -2.0, 1.0, -4.0 ]

console.log( y );
// => <Float64Array>[ 2.0, 1.0, 0.0, 3.0 ]

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

// Initial arrays...
var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
var y0 = new Float64Array( [ 0.0, 1.0, 2.0, 3.0 ] );

// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var N = floor( x0.length/2 );

// Sort every other element...
dsort2hp( N, -1.0, x1, 2, y1, 2 );

console.log( x0 );
// => <Float64Array>[ 1.0, 4.0, 3.0, 2.0 ]

console.log( y0 );
// => <Float64Array>[ 0.0, 3.0, 2.0, 1.0 ]

dsort2hp.ndarray( N, order, x, strideX, offsetX, y, strideY, offsetY )

Simultaneously sorts two double-precision floating-point strided arrays based on the sort order of the first array x using heapsort and alternative indexing semantics.

var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0 ] );
var y = new Float64Array( [ 0.0, 1.0, 2.0, 3.0 ] );

dsort2hp.ndarray( x.length, 1.0, x, 1, 0, y, 1, 0 );

console.log( x );
// => <Float64Array>[ -4.0, -2.0, 1.0, 3.0 ]

console.log( y );
// => <Float64Array>[ 3.0, 1.0, 0.0, 2.0 ]

The function has the following additional parameters:

• offsetX: x starting index.
• offsetY: y 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 ] );
var y = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ] );

dsort2hp.ndarray( 3, 1.0, x, 1, x.length-3, y, 1, y.length-3 );

console.log( x );
// => <Float64Array>[ 1.0, -2.0, 3.0, -6.0, -4.0, 5.0 ]

console.log( y );
// => <Float64Array>[ 0.0, 1.0, 2.0, 5.0, 3.0, 4.0 ]

Notes

• If N <= 0 or order == 0.0, both functions leave x and y 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 time complexity O(N log2 N).
• The algorithm is unstable, meaning that the algorithm may change the order of strided array elements which are equal or equivalent (e.g., NaN values).
• The input strided arrays are sorted in-place (i.e., the input strided arrays are mutated).

Examples

var round = require( '@stdlib/math-base-special-round' );
var randu = require( '@stdlib/random-base-randu' );
var Float64Array = require( '@stdlib/array-float64' );
var dsort2hp = require( '@stdlib/blas-ext-base-dsort2hp' );

var rand;
var sign;
var x;
var y;
var i;

x = new Float64Array( 10 );
y = new Float64Array( 10 ); // index array
for ( i = 0; i < x.length; i++ ) {
    rand = round( randu()*100.0 );
    sign = randu();
    if ( sign < 0.5 ) {
        sign = -1.0;
    } else {
        sign = 1.0;
    }
    x[ i ] = sign * rand;
    y[ i ] = i;
}
console.log( x );
console.log( y );

dsort2hp( x.length, -1.0, x, -1, y, -1 );
console.log( x );
console.log( y );

References

• Williams, John William Joseph. 1964. "Algorithm 232: Heapsort." Communications of the ACM 7 (6). New York, NY, USA: Association for Computing Machinery: 347–49. doi:10.1145/512274.512284.
• Floyd, Robert W. 1964. "Algorithm 245: Treesort." Communications of the ACM 7 (12). New York, NY, USA: Association for Computing Machinery: 701. doi:10.1145/355588.365103.

See Also

• @stdlib/blas-ext/base/dsorthp: sort a double-precision floating-point strided array using heapsort.
• @stdlib/blas-ext/base/gsort2hp: simultaneously sort two strided arrays based on the sort order of the first array using heapsort.
• @stdlib/blas-ext/base/ssort2hp: simultaneously sort two single-precision floating-point strided arrays based on the sort order of the first array using heapsort.

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.

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License

See LICENSE.

Copyright

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