'use strict'; function factory (type, config, load, typed) { var sqrt = load(require('../arithmetic/sqrt')); var variance = load(require('../statistics/var')); /** * Compute the standard deviation of a matrix or a list with values. * The standard deviations is defined as the square root of the variance: * `std(A) = sqrt(var(A))`. * In case of a (multi dimensional) array or matrix, the standard deviation * over all elements will be calculated. * * Optionally, the type of normalization can be specified as second * parameter. The parameter `normalization` can be one of the following values: * * - 'unbiased' (default) The sum of squared errors is divided by (n - 1) * - 'uncorrected' The sum of squared errors is divided by n * - 'biased' The sum of squared errors is divided by (n + 1) * * Syntax: * * math.std(a, b, c, ...) * math.std(A) * math.std(A, normalization) * * Examples: * * math.std(2, 4, 6); // returns 2 * math.std([2, 4, 6, 8]); // returns 2.581988897471611 * math.std([2, 4, 6, 8], 'uncorrected'); // returns 2.23606797749979 * math.std([2, 4, 6, 8], 'biased'); // returns 2 * * math.std([[1, 2, 3], [4, 5, 6]]); // returns 1.8708286933869707 * * See also: * * mean, median, max, min, prod, sum, var * * @param {Array | Matrix} array * A single matrix or or multiple scalar values * @param {string} [normalization='unbiased'] * Determines how to normalize the variance. * Choose 'unbiased' (default), 'uncorrected', or 'biased'. * @return {*} The standard deviation */ var std = typed('std', { // std([a, b, c, d, ...]) 'Array | Matrix': _std, // std([a, b, c, d, ...], normalization) 'Array | Matrix, string': _std, // std(a, b, c, d, ...) '...': function () { return _std(Array.prototype.slice.call(arguments)); } }); std.toTex = '\\mathrm{${name}}\\left(${args}\\right)'; return std; function _std(array, normalization) { if (array.length == 0) { throw new SyntaxError('Function std requires one or more parameters (0 provided)'); } return sqrt(variance.apply(null, arguments)); } } exports.name = 'std'; exports.factory = factory;