unaryReduceStrided1d

Perform a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function which accepts an output struct object and assign results to a provided output ndarray.

Installation

npm install @stdlib/ndarray-base-unary-reduce-strided1d-assign-struct

Usage

var unaryReduceStrided1d = require( '@stdlib/ndarray-base-unary-reduce-strided1d-assign-struct' );

unaryReduceStrided1d( fcn, arrays, dims[, options] )

Performs a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function which accepts an output struct object and assigns results to a provided output ndarray.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var Float64Results = require( '@stdlib/stats-base-ztest-one-sample-results-float64' );
var structFactory = require( '@stdlib/array-struct-factory' );
var ztest = require( '@stdlib/stats-base-ndarray-ztest' );

var ResultsArray = structFactory( Float64Results );

// 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 ResultsArray( 3 );

// 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': Float64Results,
    'data': ybuf,
    'shape': ysh,
    'strides': sy,
    'offset': oy,
    'order': 'row-major'
};

// Create additional parameter ndarray-like objects:
var alternative = {
    'dtype': 'generic',
    'data': [ 'two-sided' ],
    'shape': ysh,
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};
var alpha = {
    'dtype': 'float64',
    'data': [ 0.05 ],
    'shape': ysh,
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};
var mu = {
    'dtype': 'float64',
    'data': [ 0.0 ],
    'shape': ysh,
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};
var sigma = {
    'dtype': 'float64',
    'data': [ 1.0 ],
    'shape': ysh,
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};

// Perform a reduction:
unaryReduceStrided1d( ztest, [ x, y, alternative, alpha, mu, sigma ], [ 2, 3 ] );

var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
// returns [ [ <Float64Results>, <Float64Results>, <Float64Results> ] ]

The function accepts the following arguments:

• fcn: function which will be applied to a one-dimensional subarray and should store reduction results in an output struct object.
• 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).

TODO: document factory method

Notes

• 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, a zero-dimensional subarray of the output ndarray containing the output struct object, 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.

Examples

var normal = require( '@stdlib/random-array-normal' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var Float64Results = require( '@stdlib/stats-base-ztest-one-sample-results-float64' );
var structFactory = require( '@stdlib/array-struct-factory' );
var ztest = require( '@stdlib/stats-base-ndarray-ztest' );
var unaryReduceStrided1d = require( '@stdlib/ndarray-base-unary-reduce-strided1d-assign-struct' );

var ResultsArray = structFactory( Float64Results );

var N = 10;
var x = {
    'dtype': 'generic',
    'data': normal( N, 0.0, 1.0, {
        'dtype': 'generic'
    }),
    'shape': [ 1, 5, 2 ],
    'strides': [ 10, 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};
var y = {
    'dtype': Float64Results,
    'data': new ResultsArray( 2 ),
    'shape': [ 1, 2 ],
    'strides': [ 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};
var alternative = {
    'dtype': 'generic',
    'data': [ 'two-sided' ],
    'shape': [ 1, 2 ],
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};
var alpha = {
    'dtype': 'generic',
    'data': [ 0.05 ],
    'shape': [ 1, 2 ],
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};
var mu = {
    'dtype': 'generic',
    'data': [ 0.0 ],
    'shape': [ 1, 2 ],
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};
var sigma = {
    'dtype': 'generic',
    'data': [ 1.0 ],
    'shape': [ 1, 2 ],
    'strides': [ 0, 0 ],
    'offset': 0,
    'order': 'row-major'
};

unaryReduceStrided1d( ztest, [ x, y, alternative, alpha, mu, sigma ], [ 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

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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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