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Perform a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function and assign results to a provided output ndarray.
npm install @stdlib/ndarray-base-unary-reduce-strided1dAlternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (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.
var unaryReduceStrided1d = require( '@stdlib/ndarray-base-unary-reduce-strided1d' );Performs a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function 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 gsum = require( '@stdlib/blas-ext-base-gsum' ).ndarray;
function wrapper( arrays ) {
var x = arrays[ 0 ];
return gsum( numelDimension( x, 0 ), getData( x ), getStride( x, 0 ), getOffset( x ) );
}
// 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 ] );
// Define the array shapes:
var xsh = [ 1, 3, 2, 2 ];
var ysh = [ 1, 3 ];
// Define the array strides:
var sx = [ 12, 4, 2, 1 ];
var sy = [ 3, 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 output ndarray-like object:
var y = {
'dtype': 'float64',
'data': ybuf,
'shape': ysh,
'strides': sy,
'offset': oy,
'order': 'row-major'
};
// Perform a reduction:
unaryReduceStrided1d( wrapper, [ x, y ], [ 2, 3 ] );
var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
// returns [ [ 10.0, 26.0, 42.0 ] ]The function accepts the following arguments:
- fcn: function which will be applied to a one-dimensional subarray and should reduce the subarray to a single scalar value.
- arrays: array-like object containing one input ndarray and one output ndarray, followed by any additional ndarray arguments.
- dims: list of dimensions over which to perform a reduction.
- 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).
-
The output ndarray and any additional ndarray arguments are expected to have the same dimensions as the non-reduced dimensions of the input ndarray. When calling the reduction function, any additional ndarray arguments are provided as zero-dimensional ndarray-like objects.
-
The reduction function is expected to have the following signature:
fcn( arrays[, options] )where
- arrays: array containing a one-dimensional subarray of the input ndarray and any additional ndarray arguments as zero-dimensional ndarrays.
- options: function options (optional).
-
For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before performing a reduction in order to achieve better performance.
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 gsum = require( '@stdlib/blas-ext-base-gsum' ).ndarray;
var unaryReduceStrided1d = require( '@stdlib/ndarray-base-unary-reduce-strided1d' );
function wrapper( arrays ) {
var x = arrays[ 0 ];
return gsum( numelDimension( x, 0 ), getData( x ), getStride( x, 0 ), getOffset( x ) ); // eslint-disable-line max-len
}
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 y = {
'dtype': 'generic',
'data': zeros( 2 ),
'shape': [ 1, 2 ],
'strides': [ 2, 1 ],
'offset': 0,
'order': 'row-major'
};
unaryReduceStrided1d( wrapper, [ x, y ], [ 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 ) );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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