Press n or j to go to the next uncovered block, b, p or k for the previous block.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 15x 15x 15x 2x 2x 2x 2x 2x | /** * @license Apache-2.0 * * 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. * 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 stride2offset = require( '@stdlib/strided/base/stride2offset' ); var ndarray = require( './ndarray.js' ); // MAIN // /** * Computes the variance of a single-precision floating-point strided array using Welford's algorithm. * * ## Method * * - This implementation uses Welford's algorithm for efficient computation, which can be derived as follows. Let * * ```tex * \begin{align*} * S_n &= n \sigma_n^2 \\ * &= \sum_{i=1}^{n} (x_i - \mu_n)^2 \\ * &= \biggl(\sum_{i=1}^{n} x_i^2 \biggr) - n\mu_n^2 * \end{align*} * ``` * * Accordingly, * * ```tex * \begin{align*} * S_n - S_{n-1} &= \sum_{i=1}^{n} x_i^2 - n\mu_n^2 - \sum_{i=1}^{n-1} x_i^2 + (n-1)\mu_{n-1}^2 \\ * &= x_n^2 - n\mu_n^2 + (n-1)\mu_{n-1}^2 \\ * &= x_n^2 - \mu_{n-1}^2 + n(\mu_{n-1}^2 - \mu_n^2) \\ * &= x_n^2 - \mu_{n-1}^2 + n(\mu_{n-1} - \mu_n)(\mu_{n-1} + \mu_n) \\ * &= x_n^2 - \mu_{n-1}^2 + (\mu_{n-1} - x_n)(\mu_{n-1} + \mu_n) \\ * &= x_n^2 - \mu_{n-1}^2 + \mu_{n-1}^2 - x_n\mu_n - x_n\mu_{n-1} + \mu_n\mu_{n-1} \\ * &= x_n^2 - x_n\mu_n - x_n\mu_{n-1} + \mu_n\mu_{n-1} \\ * &= (x_n - \mu_{n-1})(x_n - \mu_n) \\ * &= S_{n-1} + (x_n - \mu_{n-1})(x_n - \mu_n) * \end{align*} * ``` * * where we use the identity * * ```tex * x_n - \mu_{n-1} = n (\mu_n - \mu_{n-1}) * ``` * * ## References * * - Welford, B. P. 1962. "Note on a Method for Calculating Corrected Sums of Squares and Products." _Technometrics_ 4 (3). Taylor & Francis: 419–20. doi:[10.1080/00401706.1962.10490022](https://doi.org/10.1080/00401706.1962.10490022). * - van Reeken, A. J. 1968. "Letters to the Editor: Dealing with Neely's Algorithms." _Communications of the ACM_ 11 (3): 149–50. doi:[10.1145/362929.362961](https://doi.org/10.1145/362929.362961). * * @param {PositiveInteger} N - number of indexed elements * @param {number} correction - degrees of freedom adjustment * @param {Float32Array} x - input array * @param {integer} strideX - stride length * @returns {number} variance * * @example * var Float32Array = require( '@stdlib/array/float32' ); * * var x = new Float32Array( [ 1.0, -2.0, 2.0 ] ); * * var v = svariancewd( x.length, 1, x, 1 ); * // returns ~4.3333 */ function svariancewd( N, correction, x, strideX ) { return ndarray( N, correction, x, strideX, stride2offset( N, strideX ) ); } // EXPORTS // module.exports = svariancewd; |