lib accessors.js

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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';
 
/**
 * Computes the standard deviation of a single-precision floating-point strided array using an accessor function and a one-pass algorithm proposed by Youngs and Cramer.
 *
 * @param {PositiveInteger} N - number of indexed elements
 * @param {Function} accessor - accessor function for accessing array elements
 * @param {Object} x - input array-like object supporting the accessor protocol
 * @param {integer} stride - stride length
 * @param {number} correction - degrees of freedom adjustment (e.g., 0 for population, 1 for sample)
 * @returns {number} standard deviation
 *
 * @example
 * var Float32Array = require( '@stdlib/array/float32' );
 *
 * function accessor( idx ) {
 *     return this.values[ idx ];
 * }
 *
 * var x = {
 *     'values': new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] ),
 *     'get': accessor
 * };
 *
 * var v = accessor( 5, x.get, x, 1, 1 );
 * // returns ~1.4142
 */
function accessor( N, accessor, x, stride, correction ) {
    if (typeof accessor !== 'function') {
        throw new TypeError( 'invalid argument. Second argument must be a function.' );
    }
    if (!x || typeof x !== 'object') {
        return NaN;
    }
    if (!Number.isInteger(N) || N <= 0) {
        return NaN;
    }
    if (!Number.isFinite(correction) || correction < 0 || correction >= N) {
        return NaN;
    }
    if (!Number.isInteger(stride)) {
        return NaN;
    }
    if (N === 1) {
        return 0.0;
    }
 
    var mean = 0.0;
    var M2 = 0.0;
    var ix = stride < 0 ? (1 - N) * stride : 0;
 
    for (var i = 0; i < N; i++, ix += stride) {
        var v = accessor.call( x, ix );
        if (typeof v !== 'number' || isNaN(v)) {
            return NaN;
        }
        var delta = v - mean;
        mean += delta / (i + 1);
        M2 += delta * (v - mean);
    }
 
    return Math.sqrt( M2 / (N - correction) );
}
 
module.exports = accessor;