unaryStrided1d

Apply a one-dimensional strided array function to a list of specified dimensions in an input ndarray and assign results to a provided output ndarray.

Installation

npm install @stdlib/ndarray-base-unary-strided1d

Usage

var unaryStrided1d = require( '@stdlib/ndarray-base-unary-strided1d' );

unaryStrided1d( fcn, arrays, dims[, options] )

Applies a one-dimensional strided array function to a list of specified dimensions in an input ndarray and assigns results to a provided output ndarray.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var getStride = require( '@stdlib/ndarray-base-stride' );
var getOffset = require( '@stdlib/ndarray-base-offset' );
var getData = require( '@stdlib/ndarray-base-data-buffer' );
var numelDimension = require( '@stdlib/ndarray-base-numel-dimension' );
var ndarraylike2scalar = require( '@stdlib/ndarray-base-ndarraylike2scalar' );
var gcusum = require( '@stdlib/blas-ext-base-gcusum' ).ndarray;

function wrapper( arrays ) {
    var x = arrays[ 0 ];
    var y = arrays[ 1 ];
    var s = arrays[ 2 ];
    return gcusum( numelDimension( x, 0 ), ndarraylike2scalar( s ), getData( x ), getStride( x, 0 ), getOffset( x ), getData( y ), getStride( y, 0 ), getOffset( y ) );
}

// Create data buffers:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var ybuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

// Define the array shapes:
var xsh = [ 1, 3, 2, 2 ];
var ysh = [ 1, 3, 2, 2 ];

// Define the array strides:
var sx = [ 12, 4, 2, 1 ];
var sy = [ 12, 4, 2, 1 ];

// Define the index offsets:
var ox = 0;
var oy = 0;

// Create an input ndarray-like object:
var x = {
    'dtype': 'float64',
    'data': xbuf,
    'shape': xsh,
    'strides': sx,
    'offset': ox,
    'order': 'row-major'
};

// Create an ndarray-like object for the initial sum:
var initial = {
    'dtype': 'float64',
    'data': new Float64Array( [ 0.0 ] ),
    'shape': [ 1, 3 ],
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};

// Create an output ndarray-like object:
var y = {
    'dtype': 'float64',
    'data': ybuf,
    'shape': ysh,
    'strides': sy,
    'offset': oy,
    'order': 'row-major'
};

// Apply strided function:
unaryStrided1d( wrapper, [ x, y, initial ], [ 2, 3 ] );

var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
// returns [ [ [ [ 1.0, 3.0 ], [ 6.0, 10.0 ] ], [ [ 5.0, 11.0 ], [ 18.0, 26.0 ] ], [ [ 9.0, 19.0 ], [ 30.0, 42.0 ] ] ] ]

The function accepts the following arguments:

• fcn: function which will be applied to a one-dimensional input subarray and should update a one-dimensional output subarray with results.
• arrays: array-like object containing one input ndarray and one output ndarray, followed by any additional ndarray arguments.
• dims: list of dimensions to which to apply a strided array function.
• options: function options which are passed through to fcn (optional).

Each provided ndarray should be an object with the following properties:

• dtype: data type.
• data: data buffer.
• shape: dimensions.
• strides: stride lengths.
• offset: index offset.
• order: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).

TODO: document factory method

Notes

• Any additional ndarray arguments are expected to have the same dimensions as the loop dimensions of the input ndarray. When calling the strided array function, any additional ndarray arguments are provided as zero-dimensional ndarray-like objects.

• The strided array function is expected to have the following signature:

  fcn( arrays[, options] )

  where

  • arrays: array containing a one-dimensional subarray of the input ndarray, a one-dimensional subarray of the output ndarray, and any additional ndarray arguments as zero-dimensional ndarrays.
  • options: function options (optional).

• The function iterates over ndarray elements according to the memory layout of the input ndarray.

• For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before performing an operation in order to achieve better performance.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var zeros = require( '@stdlib/array-base-zeros' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var numelDimension = require( '@stdlib/ndarray-base-numel-dimension' );
var getData = require( '@stdlib/ndarray-base-data-buffer' );
var getStride = require( '@stdlib/ndarray-base-stride' );
var getOffset = require( '@stdlib/ndarray-base-offset' );
var ndarraylike2scalar = require( '@stdlib/ndarray-base-ndarraylike2scalar' );
var gcusum = require( '@stdlib/blas-ext-base-gcusum' ).ndarray;
var unaryStrided1d = require( '@stdlib/ndarray-base-unary-strided1d' );

function wrapper( arrays ) {
    var x = arrays[ 0 ];
    var y = arrays[ 1 ];
    var s = arrays[ 2 ];
    return gcusum( numelDimension( x, 0 ), ndarraylike2scalar( s ), getData( x ), getStride( x, 0 ), getOffset( x ), getData( y ), getStride( y, 0 ), getOffset( y ) );
}

var N = 10;
var x = {
    'dtype': 'generic',
    'data': discreteUniform( N, -5, 5, {
        'dtype': 'generic'
    }),
    'shape': [ 1, 5, 2 ],
    'strides': [ 10, 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};
var initial = {
    'dtype': 'generic',
    'data': [ 0.0 ],
    'shape': [ 1, 2 ],
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};
var y = {
    'dtype': 'generic',
    'data': zeros( N ),
    'shape': [ 1, 5, 2 ],
    'strides': [ 10, 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};

unaryStrided1d( wrapper, [ x, y, initial ], [ 1 ] );

console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
console.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );

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.

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