100 lines
3.7 KiB
JavaScript
100 lines
3.7 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", {
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value: true
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});
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exports.createStd = void 0;
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var _factory = require("../../utils/factory.js");
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var name = 'std';
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var dependencies = ['typed', 'sqrt', 'variance'];
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var createStd = /* #__PURE__ */(0, _factory.factory)(name, dependencies, function (_ref) {
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var typed = _ref.typed,
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sqrt = _ref.sqrt,
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variance = _ref.variance;
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/**
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* Compute the standard deviation of a matrix or a list with values.
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* The standard deviations is defined as the square root of the variance:
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* `std(A) = sqrt(variance(A))`.
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* In case of a (multi dimensional) array or matrix, the standard deviation
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* over all elements will be calculated by default, unless an axis is specified
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* in which case the standard deviation will be computed along that axis.
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*
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* Additionally, it is possible to compute the standard deviation along the rows
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* or columns of a matrix by specifying the dimension as the second argument.
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*
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* Optionally, the type of normalization can be specified as the final
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* parameter. The parameter `normalization` can be one of the following values:
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*
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* - 'unbiased' (default) The sum of squared errors is divided by (n - 1)
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* - 'uncorrected' The sum of squared errors is divided by n
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* - 'biased' The sum of squared errors is divided by (n + 1)
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*
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*
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* Syntax:
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*
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* math.std(a, b, c, ...)
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* math.std(A)
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* math.std(A, normalization)
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* math.std(A, dimension)
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* math.std(A, dimension, normalization)
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*
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* Examples:
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*
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* math.std(2, 4, 6) // returns 2
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* math.std([2, 4, 6, 8]) // returns 2.581988897471611
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* math.std([2, 4, 6, 8], 'uncorrected') // returns 2.23606797749979
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* math.std([2, 4, 6, 8], 'biased') // returns 2
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*
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* math.std([[1, 2, 3], [4, 5, 6]]) // returns 1.8708286933869707
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* math.std([[1, 2, 3], [4, 6, 8]], 0) // returns [2.1213203435596424, 2.8284271247461903, 3.5355339059327378]
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* math.std([[1, 2, 3], [4, 6, 8]], 1) // returns [1, 2]
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* math.std([[1, 2, 3], [4, 6, 8]], 1, 'biased') // returns [0.7071067811865476, 1.4142135623730951]
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*
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* See also:
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*
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* mean, median, max, min, prod, sum, variance
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*
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* @param {Array | Matrix} array
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* A single matrix or or multiple scalar values
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* @param {string} [normalization='unbiased']
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* Determines how to normalize the variance.
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* Choose 'unbiased' (default), 'uncorrected', or 'biased'.
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* @param dimension {number | BigNumber}
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* Determines the axis to compute the standard deviation for a matrix
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* @return {*} The standard deviation
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*/
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return typed(name, {
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// std([a, b, c, d, ...])
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'Array | Matrix': _std,
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// std([a, b, c, d, ...], normalization)
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'Array | Matrix, string': _std,
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// std([a, b, c, c, ...], dim)
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'Array | Matrix, number | BigNumber': _std,
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// std([a, b, c, c, ...], dim, normalization)
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'Array | Matrix, number | BigNumber, string': _std,
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// std(a, b, c, d, ...)
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'...': function _(args) {
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return _std(args);
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}
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});
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function _std(array, normalization) {
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if (array.length === 0) {
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throw new SyntaxError('Function std requires one or more parameters (0 provided)');
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}
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try {
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return sqrt(variance.apply(null, arguments));
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} catch (err) {
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if (err instanceof TypeError && err.message.indexOf(' variance') !== -1) {
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throw new TypeError(err.message.replace(' variance', ' std'));
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} else {
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throw err;
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}
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}
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}
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});
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exports.createStd = createStd; |