feat: refactor variancepn implementation

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Author: gururaj1512

lib/node_modules/@stdlib/stats/base/variancepn/lib/accessors.js

@@ -20,6 +20,9 @@
 
 // MODULES //
 
+var gsumpw = require( '@stdlib/blas/ext/base/gsumpw' ).ndarray;
+
+
 // MAIN //
 
 /**
@@ -37,68 +40,62 @@
 * @param {PositiveInteger} N - number of indexed elements
 * @param {number} correction - degrees of freedom adjustment
 * @param {Object} x - input array object
+* @param {Collection} x.data - input array data
 * @param {Array<Function>} x.accessors - array element accessors
-* @param {integer} stride - stride length
-* @param {integer} offset - starting index
+* @param {integer} strideX - stride length
+* @param {NonNegativeInteger} offsetX - starting index
 * @returns {number} variance
 *
 * @example
 * var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' );
 * var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
 *
-* var x = toAccessorArray( [ 1.0, -2.0, 2.0 ] );
+* var x = toAccessorArray( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 *
-* var v = variancepn( 3, 1, arraylike2object( x ), 1, 0 );
-* // returns ~4.3333
+* var v = variancepn( 4, 1, arraylike2object( x ), 2, 1 );
+* // returns 6.25
 */
-function variancepn( N, correction, x, stride, offset ) {
+function variancepn( N, correction, x, strideX, offsetX ) {
 	var xbuf;
 	var get;
-	var sum;
 	var mu;
-	var M2;
 	var ix;
+	var M2;
 	var M;
 	var d;
 	var n;
 	var i;
 
-	// Cache references to array data:
+	// Cache reference to array data:
 	xbuf = x.data;
 
-	// Cache references to element accessors:
+	// Cache a reference to the element accessor:
 	get = x.accessors[ 0 ];
 
-	sum = 0.0;
-	M2 = 0.0;
-	M = 0.0;
-	ix = offset;
-	n = N - correction;
-
-	if ( N <= 0 || n <= 0 ) {
-		return NaN;
-	}
-	if ( N === 1 || stride === 0 ) {
+	if ( N === 1 || strideX === 0 ) {
 		return 0.0;
 	}
 
-	// Compute the mean:
-	for ( i = 0; i < N; i++ ) {
-		sum += get( xbuf, ix );
-		ix += stride;
-	}
-	mu = sum / N;
+	// Compute an estimate for the mean:
+	mu = gsumpw( N, xbuf, strideX, offsetX ) / N;
+
+	n = N - correction;
+	ix = offsetX;
+
+	// Compute the variance...
+	M2 = 0.0;
+	M = 0.0;
 
-	// Compute the variance:
-	ix = offset;
 	for ( i = 0; i < N; i++ ) {
 		d = get( xbuf, ix ) - mu;
 		M2 += d * d;
 		M += d;
-		ix += stride;
+		ix += strideX;
 	}
-
-	return ( M2 / n ) - ( ( M / N ) * ( M / n ) );
+	return (M2/n) - ((M/N)*(M/n));
 }
 
+
+// EXPORTS //
+
 module.exports = variancepn;

lib/node_modules/@stdlib/stats/base/variancepn/lib/index.js

@@ -27,25 +27,29 @@
 * var variancepn = require( '@stdlib/stats/base/variancepn' );
 *
 * var x = [ 1.0, -2.0, 2.0 ];
-* var N = x.length;
 *
-* var v = variancepn( N, 1, x, 1 );
+* var v = variancepn( x.length, 1, x, 1 );
 * // returns ~4.3333
 *
 * @example
-* var floor = require( '@stdlib/math/base/special/floor' );
 * var variancepn = require( '@stdlib/stats/base/variancepn' );
 *
 * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
-* var N = floor( x.length / 2 );
 *
-* var v = variancepn.ndarray( N, 1, x, 2, 1 );
+* var v = variancepn.ndarray( 4, 1, x, 2, 1 );
 * // returns 6.25
 */
 
 // MODULES //
 
+var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
 var main = require( './main.js' );
+var ndarray = require( './ndarray.js' );
+
+
+// MAIN //
+
+setReadOnly( main, 'ndarray', ndarray );
 
 
 // EXPORTS //

lib/node_modules/@stdlib/stats/base/variancepn/lib/main.js

@@ -20,14 +20,41 @@
 
 // MODULES //
 
-var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
-var variancepn = require( './variancepn.js' );
+var stride2offset = require( '@stdlib/strided/base/stride2offset' );
 var ndarray = require( './ndarray.js' );
 
 
 // MAIN //
 
-setReadOnly( variancepn, 'ndarray', ndarray );
+/**
+* Computes the variance of a strided array using a two-pass algorithm.
+*
+* ## Method
+*
+* -   This implementation uses a two-pass approach, as suggested by Neely (1966).
+*
+* ## References
+*
+* -   Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958](https://doi.org/10.1145/365719.365958).
+* -   Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036).
+*
+* @param {PositiveInteger} N - number of indexed elements
+* @param {number} correction - degrees of freedom adjustment
+* @param {Object} x - input array object
+* @param {Array<Function>} x.accessors - array element accessors
+* @param {integer} strideX - stride length
+* @param {integer} offsetX - starting index
+* @returns {number} variance
+*
+* @example
+* var x = [ 1.0, -2.0, 2.0 ];
+*
+* var v = variancepn( x.length, 1, x, 1);
+* // returns ~4.3333
+*/
+function variancepn( N, correction, x, strideX ) {
+	return ndarray( N, correction, x, strideX, stride2offset( N, strideX ) );
+}
 
 
 // EXPORTS //

lib/node_modules/@stdlib/stats/base/variancepn/lib/ndarray.js

@@ -1,7 +1,7 @@
 /**
 * @license Apache-2.0
 *
-* Copyright (c) 2025 The Stdlib Authors.
+* Copyright (c) 2020 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.
@@ -20,8 +20,8 @@
 
 // MODULES //
 
-var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
 var gsumpw = require( '@stdlib/blas/ext/base/gsumpw' ).ndarray;
+var arraylike2object = require( '@stdlib/array/base/arraylike2object' );
 var accessors = require( './accessors.js' );
 
 
@@ -41,58 +41,56 @@ var accessors = require( './accessors.js' );
 *
 * @param {PositiveInteger} N - number of indexed elements
 * @param {number} correction - degrees of freedom adjustment
-* @param {Object} x - input array object
-* @param {Array<Function>} x.accessors - array element accessors
-* @param {integer} stride - stride length
-* @param {integer} offset - starting index
+* @param {NumericArray} x - input array
+* @param {integer} strideX - stride length
+* @param {NonNegativeInteger} offsetX - starting index
 * @returns {number} variance
 *
 * @example
-* var x = [ 1.0, -2.0, 2.0 ];
-* var N = x.length;
+* var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
 *
-* var v = variancepn( N, 1, x, 1, 0 );
-* // returns ~4.3333
+* var v = variancepn( 4, 1, x, 2, 1 );
+* // returns 6.25
 */
-function variancepn( N, correction, x, stride, offset ) {
-	var xobj;
+function variancepn( N, correction, x, strideX, offsetX ) {
 	var mu;
-	var M2;
 	var ix;
+	var M2;
+	var o;
 	var M;
 	var d;
 	var n;
 	var i;
 
-	xobj = arraylike2object( x );
-
-	if ( xobj.accessorProtocol ) {
-		return accessors( N, correction, xobj, stride, offset );
-	}
-
 	n = N - correction;
-	if ( N <= 0 || n <= 0 ) {
+	if ( N <= 0 || n <= 0.0 ) {
 		return NaN;
 	}
-	if ( N === 1 || stride === 0 ) {
+	o = arraylike2object( x );
+	if ( o.accessorProtocol ) {
+		return accessors( N, correction, o, strideX, offsetX );
+	}
+	if ( N === 1 || strideX === 0 ) {
 		return 0.0;
 	}
 
-	// Compute the mean:
-	mu = gsumpw( N, x, stride, offset ) / N;
+	// Compute an estimate for the mean:
+	mu = gsumpw( N, x, strideX, offsetX ) / N;
 
-	// Compute the variance:
-	ix = offset;
+	// Compute the variance...
+	ix = offsetX;
 	M2 = 0.0;
 	M = 0.0;
 	for ( i = 0; i < N; i++ ) {
 		d = x[ ix ] - mu;
 		M2 += d * d;
 		M += d;
-		ix += stride;
+		ix += strideX;
 	}
-
-	return ( M2 / n ) - ( ( M / N ) * ( M / n ) );
+	return (M2/n) - ((M/N)*(M/n));
 }
 
+
+// EXPORTS //
+
 module.exports = variancepn;