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* @license Apache-2.0
*
* Copyright (c) 2025 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
var getStride = require( '@stdlib/ndarray/base/stride' );
var getOffset = require( '@stdlib/ndarray/base/offset' );
var getData = require( '@stdlib/ndarray/base/data-buffer' );
var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' );
var strided = require( '@stdlib/stats/strided/covarmtk' ).ndarray;
// MAIN //
/**
* Computes the covariance of two one-dimensional ndarrays provided known means and using a one-pass textbook algorithm.
*
* ## Notes
*
* - The function expects the following ndarrays in order:
*
* - first one-dimensional input ndarray.
* - second one-dimensional input ndarray.
* - a zero-dimensional ndarray specifying the degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the covariance according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment and `N` corresponds to the number of elements in each input ndarray. When computing the population covariance, setting this parameter to `0` is the standard choice (i.e., the provided arrays contain data constituting entire populations). When computing the unbiased sample covariance, setting this parameter to `1` is the standard choice (i.e., the provided arrays contain data sampled from larger populations; this is commonly referred to as Bessel's correction).
* - a zero-dimensional ndarray specifying the mean of the first one-dimensional ndarray.
* - a zero-dimensional ndarray specifying the mean of the second one-dimensional ndarray.
*
* @param {ArrayLikeObject<Object>} arrays - array-like object containing input ndarrays
* @returns {number} covariance
*
* @example
* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
* var ndarray = require( '@stdlib/ndarray/base/ctor' );
*
* var opts = {
* 'dtype': 'generic'
* };
*
* var xbuf = [ 1.0, -2.0, 2.0 ];
* var x = new ndarray( opts.dtype, xbuf, [ 3 ], [ 1 ], 0, 'row-major' );
*
* var ybuf = [ 2.0, -2.0, 1.0 ];
* var y = new ndarray( opts.dtype, ybuf, [ 3 ], [ 1 ], 0, 'row-major' );
*
* var correction = scalar2ndarray( 1.0, opts );
* var meanx = scalar2ndarray( 1.0/3.0, opts );
* var meany = scalar2ndarray( 1.0/3.0, opts );
*
* var v = covarmtk( [ x, y, correction, meanx, meany ] );
* // returns ~3.8333
*/
function covarmtk( arrays ) {
var correction;
var meanx;
var meany;
var x;
var y;
x = arrays[ 0 ];
y = arrays[ 1 ];
correction = ndarraylike2scalar( arrays[ 2 ] );
meanx = ndarraylike2scalar( arrays[ 3 ] );
meany = ndarraylike2scalar( arrays[ 4 ] );
return strided( numelDimension( x, 0 ), correction, meanx, getData( x ), getStride( x, 0 ), getOffset( x ), meany, getData( y ), getStride( y, 0 ), getOffset( y ) ); // eslint-disable-line max-len
}
// EXPORTS //
module.exports = covarmtk;
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