1 | import { factory } from '../../utils/factory.js';
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2 | var name = 'std';
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3 | var dependencies = ['typed', 'sqrt', 'variance'];
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4 | export var createStd = /* #__PURE__ */factory(name, dependencies, _ref => {
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5 | var {
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6 | typed,
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7 | sqrt,
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8 | variance
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9 | } = _ref;
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10 |
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11 | /**
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12 | * Compute the standard deviation of a matrix or a list with values.
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13 | * The standard deviations is defined as the square root of the variance:
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14 | * `std(A) = sqrt(variance(A))`.
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15 | * In case of a (multi dimensional) array or matrix, the standard deviation
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16 | * over all elements will be calculated by default, unless an axis is specified
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17 | * in which case the standard deviation will be computed along that axis.
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18 | *
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19 | * Additionally, it is possible to compute the standard deviation along the rows
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20 | * or columns of a matrix by specifying the dimension as the second argument.
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21 | *
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22 | * Optionally, the type of normalization can be specified as the final
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23 | * parameter. The parameter `normalization` can be one of the following values:
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24 | *
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25 | * - 'unbiased' (default) The sum of squared errors is divided by (n - 1)
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26 | * - 'uncorrected' The sum of squared errors is divided by n
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27 | * - 'biased' The sum of squared errors is divided by (n + 1)
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28 | *
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29 | *
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30 | * Syntax:
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31 | *
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32 | * math.std(a, b, c, ...)
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33 | * math.std(A)
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34 | * math.std(A, normalization)
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35 | * math.std(A, dimension)
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36 | * math.std(A, dimension, normalization)
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37 | *
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38 | * Examples:
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39 | *
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40 | * math.std(2, 4, 6) // returns 2
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41 | * math.std([2, 4, 6, 8]) // returns 2.581988897471611
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42 | * math.std([2, 4, 6, 8], 'uncorrected') // returns 2.23606797749979
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43 | * math.std([2, 4, 6, 8], 'biased') // returns 2
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44 | *
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45 | * math.std([[1, 2, 3], [4, 5, 6]]) // returns 1.8708286933869707
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46 | * math.std([[1, 2, 3], [4, 6, 8]], 0) // returns [2.1213203435596424, 2.8284271247461903, 3.5355339059327378]
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47 | * math.std([[1, 2, 3], [4, 6, 8]], 1) // returns [1, 2]
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48 | * math.std([[1, 2, 3], [4, 6, 8]], 1, 'biased') // returns [0.7071067811865476, 1.4142135623730951]
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49 | *
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50 | * See also:
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51 | *
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52 | * mean, median, max, min, prod, sum, variance
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53 | *
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54 | * @param {Array | Matrix} array
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55 | * A single matrix or or multiple scalar values
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56 | * @param {string} [normalization='unbiased']
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57 | * Determines how to normalize the variance.
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58 | * Choose 'unbiased' (default), 'uncorrected', or 'biased'.
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59 | * @param dimension {number | BigNumber}
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60 | * Determines the axis to compute the standard deviation for a matrix
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61 | * @return {*} The standard deviation
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62 | */
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63 | return typed(name, {
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64 | // std([a, b, c, d, ...])
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65 | 'Array | Matrix': _std,
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66 | // std([a, b, c, d, ...], normalization)
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67 | 'Array | Matrix, string': _std,
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68 | // std([a, b, c, c, ...], dim)
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69 | 'Array | Matrix, number | BigNumber': _std,
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70 | // std([a, b, c, c, ...], dim, normalization)
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71 | 'Array | Matrix, number | BigNumber, string': _std,
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72 | // std(a, b, c, d, ...)
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73 | '...': function _(args) {
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74 | return _std(args);
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75 | }
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76 | });
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77 |
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78 | function _std(array, normalization) {
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79 | if (array.length === 0) {
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80 | throw new SyntaxError('Function std requires one or more parameters (0 provided)');
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81 | }
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82 |
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83 | try {
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84 | return sqrt(variance.apply(null, arguments));
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85 | } catch (err) {
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86 | if (err instanceof TypeError && err.message.indexOf(' variance') !== -1) {
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87 | throw new TypeError(err.message.replace(' variance', ' std'));
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88 | } else {
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89 | throw err;
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90 | }
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91 | }
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92 | }
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93 | }); |
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