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dgetrans

Convert a matrix from row-major layout to column-major layout or vice versa.

Usage

var dgetrans = require( '@stdlib/lapack-base-dge-trans' );

dgetrans( order, M, N, A, LDA, out, LDO )

Converts a matrix from row-major layout to column-major layout or vice versa.

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

var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var out = new Float64Array( 6 );

out = dgetrans( 'row-major', 2, 3, A, 3, out, 2 );
// returns <Float64Array>[ 1.0, 4.0, 2.0, 5.0, 3.0, 6.0 ]

The function has the following parameters:

• order: storage layout.
• M: number of rows in A.
• N: number of columns in A.
• A: input Float64Array.
• LDA: stride of the first dimension of A (a.k.a., leading dimension of the matrix A).
• out: output Float64Array.
• LDO: stride of the first dimension of out (a.k.a., leading dimension of the matrix out).

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

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

// Initial arrays...
var A0 = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0 ] );
var Out0 = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0 ] );

// Create offset views...
var A1 = new Float64Array( A0.buffer, A0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var Out1 = new Float64Array( Out0.buffer, Out0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

dgetrans( 'row-major', 2, 2, A1, 2, Out1, 2 );
// Out0 => <Float64Array>[ 0.0, 1.0, 3.0, 2.0, 4.0 ]

dgetrans.ndarray( M, N, A, sa1, sa2, oa, out, so1, so2, oo )

Converts a matrix from row-major layout to column-major layout or vice versa using alternative indexing semantics.

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

var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var out = new Float64Array( 6 );

out = dgetrans.ndarray( 2, 3, A, 3, 1, 0, out, 2, 1, 0 );
// returns <Float64Array>[ 1.0, 4.0, 2.0, 5.0, 3.0, 6.0 ]

The function has the following parameters:

• M: number of rows in A.
• N: number of columns in A.
• A: input Float64Array.
• sa1: stride of the first dimension of A.
• sa2: stride of the second dimension of A.
• oa: starting index for A.
• out: output Float64Array.
• so1: stride of the first dimension of out.
• so2: stride of the second dimension of out.
• oo: starting index for out.

While typed array views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example,

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

var A = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0 ] );
var out = new Float64Array( [ 0.0, 0.0, 11.0, 312.0, 53.0, 412.0 ] );

dgetrans.ndarray( 2, 2, A, 2, 1, 1, out, 2, 1, 2 );
// out => <Float64Array>[ 0.0, 0.0, 1.0, 3.0, 2.0, 4.0 ]

Notes

• dgetrans() corresponds to the LAPACK utility routine dge_trans.

Examples

var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var numel = require( '@stdlib/ndarray-base-numel' );
var Float64Array = require( '@stdlib/array-float64' );
var dgetrans = require( '@stdlib/lapack-base-dge-trans' );

var shapeA = [ 2, 3 ];
var shapeOut = [ 3, 2 ];

// Row-major layout...
var order = 'row-major';

var stridesA = shape2strides( shapeA, order );
var stridesOut = shape2strides( shapeOut, order );

var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
console.log( ndarray2array( A, shapeA, stridesA, 0, order ) );

var out = new Float64Array( numel( shapeA ) );

out = dgetrans( order, shapeA[0], shapeA[1], A, stridesA[0], out, stridesOut[0] );
console.log( ndarray2array( out, shapeOut, stridesOut, 0, order ) );

// Column-major layout...
order = 'column-major';

stridesA = shape2strides( shapeA, order );
stridesOut = shape2strides( shapeOut, order );

A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
console.log( ndarray2array( A, shapeA, stridesA, 0, order ) );

out = new Float64Array( numel( shapeA ) );

out = dgetrans( order, shapeA[0], shapeA[1], A, stridesA[1], out, stridesOut[1] );
console.log( ndarray2array( out, shapeOut, stridesOut, 0, order ) );

// Input and output arrays have different layouts...
stridesA = shape2strides( shapeA, 'row-major' );
stridesOut = shape2strides( shapeOut, 'column-major' );

A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
console.log( ndarray2array( A, shapeA, stridesA, 0, 'row-major' ) );

out = new Float64Array( numel( shapeA ) );

out = dgetrans.ndarray( shapeA[0], shapeA[1], A, stridesA[0], stridesA[1], 0, out, stridesOut[0], stridesOut[1], 0 );
console.log( ndarray2array( out, shapeOut, stridesOut, 0, 'column-major' ) );

// Input and output arrays have different layouts...
stridesA = shape2strides( shapeA, 'column-major' );
stridesOut = shape2strides( shapeOut, 'row-major' );

A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
console.log( ndarray2array( A, shapeA, stridesA, 0, 'column-major' ) );

out = new Float64Array( numel( shapeA ) );

out = dgetrans.ndarray( shapeA[0], shapeA[1], A, stridesA[0], stridesA[1], 0, out, stridesOut[0], stridesOut[1], 0 );
console.log( ndarray2array( out, shapeOut, stridesOut, 0, 'row-major' ) );

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C APIs

Installation

npm install @stdlib/lapack-base-dge-trans

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

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Examples

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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

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License

See LICENSE.

Copyright

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