      1 /*
      2     Copyright 2008-2024
      3         Matthias Ehmann,
      4         Michael Gerhaeuser,
      5         Carsten Miller,
      6         Bianca Valentin,
      7         Alfred Wassermann,
      8         Peter Wilfahrt
      9
     10     This file is part of JSXGraph.
     11
     12     JSXGraph is free software dual licensed under the GNU LGPL or MIT License.
     13
     14     You can redistribute it and/or modify it under the terms of the
     15
     16       * GNU Lesser General Public License as published by
     17         the Free Software Foundation, either version 3 of the License, or
     18         (at your option) any later version
     19       OR
     20       * MIT License: https://github.com/jsxgraph/jsxgraph/blob/master/LICENSE.MIT
     21
     22     JSXGraph is distributed in the hope that it will be useful,
     23     but WITHOUT ANY WARRANTY; without even the implied warranty of
     24     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
     25     GNU Lesser General Public License for more details.
     26
     27     You should have received a copy of the GNU Lesser General Public License and
     28     the MIT License along with JSXGraph. If not, see <https://www.gnu.org/licenses/>
     29     and <https://opensource.org/licenses/MIT/>.
     30  */
     31
     32 /*global JXG: true, define: true*/
     33 /*jslint nomen: true, plusplus: true*/
     34
     35 import JXG from "../jxg.js";
     36 import Mat from "./math.js";
     37 import Type from "../utils/type.js";
     38
     39 /**
     40  * Functions for mathematical statistics. Most functions are like in the statistics package R.
     41  * @name JXG.Math.Statistics
     42  * @exports Mat.Statistics as JXG.Math.Statistics
     43  * @namespace
     44  */
     45 Mat.Statistics = {
     46     /**
     47      * Sums up all elements of the given array.
     48      * @param {Array} arr An array of numbers.
     49      * @returns {Number}
     50      * @memberof JXG.Math.Statistics
     51      */
     52     sum: function (arr) {
     53         var i,
     54             len = arr.length,
     55             res = 0;
     56
     57         for (i = 0; i < len; i++) {
     58             res += arr[i];
     59         }
     60         return res;
     61     },
     62
     63     /**
     64      * Multiplies all elements of the given array.
     65      * @param {Array} arr An array of numbers.
     66      * @returns {Number}
     67      * @memberof JXG.Math.Statistics
     68      */
     69     prod: function (arr) {
     70         var i,
     71             len = arr.length,
     72             res = 1;
     73
     74         for (i = 0; i < len; i++) {
     75             res *= arr[i];
     76         }
     77         return res;
     78     },
     79
     80     /**
     81      * Determines the mean value of the values given in an array.
     82      * @param {Array} arr
     83      * @returns {Number}
     84      * @memberof JXG.Math.Statistics
     85      */
     86     mean: function (arr) {
     87         if (arr.length > 0) {
     88             return this.sum(arr) / arr.length;
     89         }
     90
     91         return 0.0;
     92     },
     93
     94     /**
     95      * The median of a finite set of values is the value that divides the set
     96      * into two equal sized subsets.
     97      * @param {Array} arr The set of values.
     98      * @returns {Number}
     99      * @memberof JXG.Math.Statistics
    100      */
    101     median: function (arr) {
    102         var tmp, len;
    103
    104         if (arr.length > 0) {
    105             if (ArrayBuffer.isView(arr)) {
    106                 tmp = new Float64Array(arr);
    107                 tmp.sort();
    108             } else {
    109                 tmp = arr.slice(0);
    110                 tmp.sort(function (a, b) {
    111                     return a - b;
    112                 });
    113             }
    114             len = tmp.length;
    115
    116             if (len & 1) {
    117                 // odd
    118                 return tmp[parseInt(len * 0.5, 10)];
    119             }
    120
    121             return (tmp[len * 0.5 - 1] + tmp[len * 0.5]) * 0.5;
    122         }
    123
    124         return 0.0;
    125     },
    126
    127     /**
    128      * The P-th percentile ( <i>0 < P ≤ 100</i> ) of a list of <i>N</i> ordered values (sorted from least to greatest)
    129      * is the smallest value in the list such that no more than <i>P</i> percent of the data is strictly less
    130      * than the value and at least <i>P</i> percent of the data is less than or equal to that value.
    131      * See <a href="https://en.wikipedia.org/wiki/Percentile">https://en.wikipedia.org/wiki/Percentile</a>.
    132      *
    133      * Here, the <i>linear interpolation between closest ranks</i> method is used.
    134      * @param {Array} arr The set of values, need not be ordered.
    135      * @param {Number|Array} percentile One or several percentiles
    136      * @returns {Number|Array} Depending if a number or an array is the input for percentile, a number or an array containing the percentiles
    137      * is returned.
    138      */
    139     percentile: function (arr, percentile) {
    140         var tmp,
    141             len,
    142             i,
    143             p,
    144             res = [],
    145             per;
    146
    147         if (arr.length > 0) {
    148             if (ArrayBuffer.isView(arr)) {
    149                 tmp = new Float64Array(arr);
    150                 tmp.sort();
    151             } else {
    152                 tmp = arr.slice(0);
    153                 tmp.sort(function (a, b) {
    154                     return a - b;
    155                 });
    156             }
    157             len = tmp.length;
    158
    159             if (Type.isArray(percentile)) {
    160                 p = percentile;
    161             } else {
    162                 p = [percentile];
    163             }
    164
    165             for (i = 0; i < p.length; i++) {
    166                 per = len * p[i] * 0.01;
    167                 if (parseInt(per, 10) === per) {
    168                     res.push((tmp[per - 1] + tmp[per]) * 0.5);
    169                 } else {
    170                     res.push(tmp[parseInt(per, 10)]);
    171                 }
    172             }
    173
    174             if (Type.isArray(percentile)) {
    175                 return res;
    176             } else {
    177                 return res[0];
    178             }
    179         }
    180
    181         return 0.0;
    182     },
    183
    184     /**
    185      * Bias-corrected sample variance. A variance is a measure of how far a
    186      * set of numbers are spread out from each other.
    187      * @param {Array} arr
    188      * @returns {Number}
    189      * @memberof JXG.Math.Statistics
    190      */
    191     variance: function (arr) {
    192         var m,
    193             res,
    194             i,
    195             len = arr.length;
    196
    197         if (len > 1) {
    198             m = this.mean(arr);
    199             res = 0;
    200             for (i = 0; i < len; i++) {
    201                 res += (arr[i] - m) * (arr[i] - m);
    202             }
    203             return res / (arr.length - 1);
    204         }
    205
    206         return 0.0;
    207     },
    208
    209     /**
    210      * Determines the <strong>s</strong>tandard <strong>d</strong>eviation which shows how much
    211      * variation there is from the average value of a set of numbers.
    212      * @param {Array} arr
    213      * @returns {Number}
    214      * @memberof JXG.Math.Statistics
    215      */
    216     sd: function (arr) {
    217         return Math.sqrt(this.variance(arr));
    218     },
    219
    220     /**
    221      * Weighted mean value is basically the same as {@link JXG.Math.Statistics.mean} but here the values
    222      * are weighted, i.e. multiplied with another value called <em>weight</em>. The weight values are given
    223      * as a second array with the same length as the value array..
    224      * @throws {Error} If the dimensions of the arrays don't match.
    225      * @param {Array} arr Set of alues.
    226      * @param {Array} w Weight values.
    227      * @returns {Number}
    228      * @memberof JXG.Math.Statistics
    229      */
    230     weightedMean: function (arr, w) {
    231         if (arr.length !== w.length) {
    232             throw new Error(
    233                 "JSXGraph error (Math.Statistics.weightedMean): Array dimension mismatch."
    234             );
    235         }
    236
    237         if (arr.length > 0) {
    238             return this.mean(this.multiply(arr, w));
    239         }
    240
    241         return 0.0;
    242     },
    243
    244     /**
    245      * Extracts the maximum value from the array.
    246      * @param {Array} arr
    247      * @returns {Number} The highest number from the array. It returns <tt>NaN</tt> if not every element could be
    248      * interpreted as a number and <tt>-Infinity</tt> if an empty array is given or no element could be interpreted
    249      * as a number.
    250      * @memberof JXG.Math.Statistics
    251      */
    252     max: function (arr) {
    253         return Math.max.apply(this, arr);
    254     },
    255
    256     /**
    257      * Extracts the minimum value from the array.
    258      * @param {Array} arr
    259      * @returns {Number} The lowest number from the array. It returns <tt>NaN</tt> if not every element could be
    260      * interpreted as a number and <tt>Infinity</tt> if an empty array is given or no element could be interpreted
    261      * as a number.
    262      * @memberof JXG.Math.Statistics
    263      */
    264     min: function (arr) {
    265         return Math.min.apply(this, arr);
    266     },
    267
    268     /**
    269      * Determines the lowest and the highest value from the given array.
    270      * @param {Array} arr
    271      * @returns {Array} The minimum value as the first and the maximum value as the second value.
    272      * @memberof JXG.Math.Statistics
    273      */
    274     range: function (arr) {
    275         return [this.min(arr), this.max(arr)];
    276     },
    277
    278     /**
    279      * Determines the absolute value of every given value.
    280      * @param {Array|Number} arr
    281      * @returns {Array|Number}
    282      * @memberof JXG.Math.Statistics
    283      */
    284     abs: function (arr) {
    285         var i, len, res;
    286
    287         if (Type.isArray(arr)) {
    288             if (arr.map) {
    289                 res = arr.map(Math.abs);
    290             } else {
    291                 len = arr.length;
    292                 res = [];
    293
    294                 for (i = 0; i < len; i++) {
    295                     res[i] = Math.abs(arr[i]);
    296                 }
    297             }
    298         } else if (ArrayBuffer.isView(arr)) {
    299             res = arr.map(Math.abs);
    300         } else {
    301             res = Math.abs(arr);
    302         }
    303         return res;
    304     },
    305
    306     /**
    307      * Adds up two (sequences of) values. If one value is an array and the other one is a number the number
    308      * is added to every element of the array. If two arrays are given and the lengths don't match the shortest
    309      * length is taken.
    310      * @param {Array|Number} arr1
    311      * @param {Array|Number} arr2
    312      * @returns {Array|Number}
    313      * @memberof JXG.Math.Statistics
    314      */
    315     add: function (arr1, arr2) {
    316         var i,
    317             len,
    318             res = [];
    319
    320         arr1 = Type.evalSlider(arr1);
    321         arr2 = Type.evalSlider(arr2);
    322
    323         if (Type.isArray(arr1) && Type.isNumber(arr2)) {
    324             len = arr1.length;
    325
    326             for (i = 0; i < len; i++) {
    327                 res[i] = arr1[i] + arr2;
    328             }
    329         } else if (Type.isNumber(arr1) && Type.isArray(arr2)) {
    330             len = arr2.length;
    331
    332             for (i = 0; i < len; i++) {
    333                 res[i] = arr1 + arr2[i];
    334             }
    335         } else if (Type.isArray(arr1) && Type.isArray(arr2)) {
    336             len = Math.min(arr1.length, arr2.length);
    337
    338             for (i = 0; i < len; i++) {
    339                 res[i] = arr1[i] + arr2[i];
    340             }
    341         } else {
    342             res = arr1 + arr2;
    343         }
    344
    345         return res;
    346     },
    347
    348     /**
    349      * Divides two (sequences of) values. If two arrays are given and the lengths don't match the shortest length
    350      * is taken.
    351      * @param {Array|Number} arr1 Dividend
    352      * @param {Array|Number} arr2 Divisor
    353      * @returns {Array|Number}
    354      * @memberof JXG.Math.Statistics
    355      */
    356     div: function (arr1, arr2) {
    357         var i,
    358             len,
    359             res = [];
    360
    361         arr1 = Type.evalSlider(arr1);
    362         arr2 = Type.evalSlider(arr2);
    363
    364         if (Type.isArray(arr1) && Type.isNumber(arr2)) {
    365             len = arr1.length;
    366
    367             for (i = 0; i < len; i++) {
    368                 res[i] = arr1[i] / arr2;
    369             }
    370         } else if (Type.isNumber(arr1) && Type.isArray(arr2)) {
    371             len = arr2.length;
    372
    373             for (i = 0; i < len; i++) {
    374                 res[i] = arr1 / arr2[i];
    375             }
    376         } else if (Type.isArray(arr1) && Type.isArray(arr2)) {
    377             len = Math.min(arr1.length, arr2.length);
    378
    379             for (i = 0; i < len; i++) {
    380                 res[i] = arr1[i] / arr2[i];
    381             }
    382         } else {
    383             res = arr1 / arr2;
    384         }
    385
    386         return res;
    387     },
    388
    389     /**
    390      * @function
    391      * @deprecated Use {@link JXG.Math.Statistics.div} instead.
    392      * @memberof JXG.Math.Statistics
    393      */
    394     divide: function () {
    395         JXG.deprecated("Statistics.divide()", "Statistics.div()");
    396         Mat.Statistics.div.apply(Mat.Statistics, arguments);
    397     },
    398
    399     /**
    400      * Divides two (sequences of) values and returns the remainder. If two arrays are given and the lengths don't
    401      * match the shortest length is taken.
    402      * @param {Array|Number} arr1 Dividend
    403      * @param {Array|Number} arr2 Divisor
    404      * @param {Boolean} [math=false] Mathematical mod or symmetric mod? Default is symmetric, the JavaScript <tt>%</tt> operator.
    405      * @returns {Array|Number}
    406      * @memberof JXG.Math.Statistics
    407      */
    408     mod: function (arr1, arr2, math) {
    409         var i,
    410             len,
    411             res = [],
    412             mod = function (a, m) {
    413                 return a % m;
    414             };
    415
    416         math = Type.def(math, false);
    417
    418         if (math) {
    419             mod = Mat.mod;
    420         }
    421
    422         arr1 = Type.evalSlider(arr1);
    423         arr2 = Type.evalSlider(arr2);
    424
    425         if (Type.isArray(arr1) && Type.isNumber(arr2)) {
    426             len = arr1.length;
    427
    428             for (i = 0; i < len; i++) {
    429                 res[i] = mod(arr1[i], arr2);
    430             }
    431         } else if (Type.isNumber(arr1) && Type.isArray(arr2)) {
    432             len = arr2.length;
    433
    434             for (i = 0; i < len; i++) {
    435                 res[i] = mod(arr1, arr2[i]);
    436             }
    437         } else if (Type.isArray(arr1) && Type.isArray(arr2)) {
    438             len = Math.min(arr1.length, arr2.length);
    439
    440             for (i = 0; i < len; i++) {
    441                 res[i] = mod(arr1[i], arr2[i]);
    442             }
    443         } else {
    444             res = mod(arr1, arr2);
    445         }
    446
    447         return res;
    448     },
    449
    450     /**
    451      * Multiplies two (sequences of) values. If one value is an array and the other one is a number the number
    452      * is multiplied to every element of the array. If two arrays are given and the lengths don't match the shortest
    453      * length is taken.
    454      * @param {Array|Number} arr1
    455      * @param {Array|Number} arr2
    456      * @returns {Array|Number}
    457      * @memberof JXG.Math.Statistics
    458      */
    459     multiply: function (arr1, arr2) {
    460         var i,
    461             len,
    462             res = [];
    463
    464         arr1 = Type.evalSlider(arr1);
    465         arr2 = Type.evalSlider(arr2);
    466
    467         if (Type.isArray(arr1) && Type.isNumber(arr2)) {
    468             len = arr1.length;
    469
    470             for (i = 0; i < len; i++) {
    471                 res[i] = arr1[i] * arr2;
    472             }
    473         } else if (Type.isNumber(arr1) && Type.isArray(arr2)) {
    474             len = arr2.length;
    475
    476             for (i = 0; i < len; i++) {
    477                 res[i] = arr1 * arr2[i];
    478             }
    479         } else if (Type.isArray(arr1) && Type.isArray(arr2)) {
    480             len = Math.min(arr1.length, arr2.length);
    481
    482             for (i = 0; i < len; i++) {
    483                 res[i] = arr1[i] * arr2[i];
    484             }
    485         } else {
    486             res = arr1 * arr2;
    487         }
    488
    489         return res;
    490     },
    491
    492     /**
    493      * Subtracts two (sequences of) values. If two arrays are given and the lengths don't match the shortest
    494      * length is taken.
    495      * @param {Array|Number} arr1 Minuend
    496      * @param {Array|Number} arr2 Subtrahend
    497      * @returns {Array|Number}
    498      * @memberof JXG.Math.Statistics
    499      */
    500     subtract: function (arr1, arr2) {
    501         var i,
    502             len,
    503             res = [];
    504
    505         arr1 = Type.evalSlider(arr1);
    506         arr2 = Type.evalSlider(arr2);
    507
    508         if (Type.isArray(arr1) && Type.isNumber(arr2)) {
    509             len = arr1.length;
    510
    511             for (i = 0; i < len; i++) {
    512                 res[i] = arr1[i] - arr2;
    513             }
    514         } else if (Type.isNumber(arr1) && Type.isArray(arr2)) {
    515             len = arr2.length;
    516
    517             for (i = 0; i < len; i++) {
    518                 res[i] = arr1 - arr2[i];
    519             }
    520         } else if (Type.isArray(arr1) && Type.isArray(arr2)) {
    521             len = Math.min(arr1.length, arr2.length);
    522
    523             for (i = 0; i < len; i++) {
    524                 res[i] = arr1[i] - arr2[i];
    525             }
    526         } else {
    527             res = arr1 - arr2;
    528         }
    529
    530         return res;
    531     },
    532
    533     /**
    534      * The Theil-Sen estimator can be used to determine a more robust linear regression of a set of sample
    535      * points than least squares regression in {@link JXG.Math.Numerics.regressionPolynomial}.
    536      *
    537      * If the function should be applied to an array a of points, a the coords array can be generated with
    538      * JavaScript array.map:
    539      *
    540      * <pre>
    541      * JXG.Math.Statistics.TheilSenRegression(a.map(el => el.coords));
    542      * </pre>
    543      *
    544      * @param {Array} coords Array of {@link JXG.Coords}.
    545      * @returns {Array} A stdform array of the regression line.
    546      * @memberof JXG.Math.Statistics
    547      *
    548      * @example
    549      * var board = JXG.JSXGraph.initBoard('jxgbox', { boundingbox: [-6,6,6,-6], axis : true });
    550      * var a=[];
    551      * a[0]=board.create('point', [0,0]);
    552      * a[1]=board.create('point', [3,0]);
    553      * a[2]=board.create('point', [0,3]);
    554      *
    555      * board.create('line', [
    556      *     () => JXG.Math.Statistics.TheilSenRegression(a.map(el => el.coords))
    557      *   ],
    558      *   {strokeWidth:1, strokeColor:'black'});
    559      *
    560      * </pre><div id="JXG0a28be85-91c5-44d3-aae6-114e81217cf0" class="jxgbox" style="width: 300px; height: 300px;"></div>
    561      * <script type="text/javascript">
    562      *     (function() {
    563      *         var board = JXG.JSXGraph.initBoard('JXG0a28be85-91c5-44d3-aae6-114e81217cf0',
    564      *             {boundingbox: [-6,6,6,-6], axis: true, showcopyright: false, shownavigation: false});
    565      *     var a=[];
    566      *     a[0]=board.create('point', [0,0]);
    567      *     a[1]=board.create('point', [3,0]);
    568      *     a[2]=board.create('point', [0,3]);
    569      *
    570      *     board.create('line', [
    571      *         () => JXG.Math.Statistics.TheilSenRegression(a.map(el => el.coords))
    572      *       ],
    573      *       {strokeWidth:1, strokeColor:'black'});
    574      *
    575      *     })();
    576      *
    577      * </script><pre>
    578      *
    579      */
    580     TheilSenRegression: function (coords) {
    581         var i,
    582             j,
    583             slopes = [],
    584             tmpslopes = [],
    585             yintercepts = [];
    586
    587         for (i = 0; i < coords.length; i++) {
    588             tmpslopes.length = 0;
    589
    590             for (j = 0; j < coords.length; j++) {
    591                 if (Math.abs(coords[j].usrCoords[1] - coords[i].usrCoords[1]) > Mat.eps) {
    592                     tmpslopes[j] =
    593                         (coords[j].usrCoords[2] - coords[i].usrCoords[2]) /
    594                         (coords[j].usrCoords[1] - coords[i].usrCoords[1]);
    595                 }
    596             }
    597
    598             slopes[i] = this.median(tmpslopes);
    599             yintercepts.push(coords[i].usrCoords[2] - slopes[i] * coords[i].usrCoords[1]);
    600         }
    601
    602         return [this.median(yintercepts), this.median(slopes), -1];
    603     },
    604
    605     /**
    606      * Generate values of a standard normal random variable with the Marsaglia polar method, see
    607      * <a href="https://en.wikipedia.org/wiki/Marsaglia_polar_method">https://en.wikipedia.org/wiki/Marsaglia_polar_method</a>.
    608      * See also D. E. Knuth, The art of computer programming, vol 2, p. 117.
    609      *
    610      * @param {Number} mean mean value of the normal distribution
    611      * @param {Number} stdDev standard deviation of the normal distribution
    612      * @returns {Number} value of a standard normal random variable
    613      * @memberof JXG.Math.Statistics
    614      */
    615     generateGaussian: function (mean, stdDev) {
    616         var u, v, s;
    617
    618         if (this.hasSpare) {
    619             this.hasSpare = false;
    620             return this.spare * stdDev + mean;
    621         }
    622
    623         do {
    624             u = Math.random() * 2 - 1;
    625             v = Math.random() * 2 - 1;
    626             s = u * u + v * v;
    627         } while (s >= 1 || s === 0);
    628
    629         s = Math.sqrt((-2.0 * Math.log(s)) / s);
    630
    631         this.spare = v * s;
    632         this.hasSpare = true;
    633         return mean + stdDev * u * s;
    634     },
    635
    636     /**
    637      * Generate value of a standard normal random variable with given mean and standard deviation.
    638      * Alias for {@link JXG.Math.Statistics#generateGaussian}
    639      *
    640      * @param {Number} mean
    641      * @param {Number} stdDev
    642      * @returns Number
    643      * @memberof JXG.Math.Statistics
    644      * @see JXG.Math.Statistics#generateGaussian
    645      * @example
    646      *  let board = JXG.JSXGraph.initBoard('JXGbox',
    647      *       { boundingbox: [-5, 1.5, 5, -.03], axis: true});
    648      *
    649      *   let runs = [
    650      *       [0, 0.2, 'blue'],
    651      *       [0, 1.0, 'red'],
    652      *       [0, 5.0, 'orange'],
    653      *       [-2,0.5, 'green'],
    654      *   ]
    655      *
    656      *   let labelY = 1.2
    657      *   runs.forEach((run,i) => {
    658      *       board.create('segment',[[1.0,labelY-(i/20)],[2.0,labelY-(i/20)]],{strokeColor:run[2]})
    659      *       board.create('text',[2.5,labelY-(i/20),`μ=${run[0]}, σ<sup>2</sup>=${run[1]}`])
    660      *
    661      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomNormal(run[0],Math.sqrt(run[1])))  // sqrt so Std Dev, not Variance
    662      *       let res = JXG.Math.Statistics.histogram(x, { bins: 40, density: true, cumulative: false, range: false });
    663      *       board.create('curve', [res[1], res[0]], { strokeColor: run[2], strokeWidth:2});
    664      *   })
    665      *
    666      * </pre><div id="JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-4" class="jxgbox" style="width: 300px; height: 300px;"></div>
    667      * <script type="text/javascript">
    668      * {
    669      *  let board = JXG.JSXGraph.initBoard('JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-4',
    670      *       { boundingbox: [-5, 1.5, 5, -.03], axis: true});
    671      *
    672      *   let runs = [
    673      *       [0, 0.2, 'blue'],
    674      *       [0, 1.0, 'red'],
    675      *       [0, 5.0, 'orange'],
    676      *       [-2,0.5, 'green'],
    677      *   ]
    678      *
    679      *   let labelY = 1.2
    680      *   runs.forEach((run,i) => {
    681      *       board.create('segment',[[1.0,labelY-(i/20)],[2.0,labelY-(i/20)]],{strokeColor:run[2]})
    682      *       board.create('text',[2.5,labelY-(i/20),`μ=${run[0]}, σ<sup>2</sup>=${run[1]}`])
    683      *
    684      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomNormal(run[0],Math.sqrt(run[1])))  // sqrt so Std Dev, not Variance
    685      *       let res = JXG.Math.Statistics.histogram(x, { bins: 40, density: true, cumulative: false, range: false });
    686      *       board.create('curve', [res[1], res[0]], { strokeColor: run[2], strokeWidth:2});
    687      *   })
    688      * }
    689      * </script><pre>
    690
    691      */
    692     randomNormal: function (mean, stdDev) {
    693         return this.generateGaussian(mean, stdDev);
    694     },
    695
    696     /**
    697      * Generate value of a uniform distributed random variable in the interval [a, b].
    698      * @param {Number} a
    699      * @param {Number} b
    700      * @returns Number
    701      * @memberof JXG.Math.Statistics
    702      */
    703     randomUniform: function (a, b) {
    704         return Math.random() * (b - a) + a;
    705     },
    706
    707     /**
    708      * Generate value of a random variable with exponential distribution, i.e.
    709      * <i>f(x; lambda) = lambda * e^(-lambda x)</i> if <i>x >= 0</i> and <i>f(x; lambda) = 0</i> if <i>x < 0</i>.
    710      * See <a href="https://en.wikipedia.org/wiki/Exponential_distribution">https://en.wikipedia.org/wiki/Exponential_distribution</a>.
    711      * Algorithm: D.E. Knuth, TAOCP 2, p. 128.
    712      *
    713      * @param {Number} lambda <i>> 0</i>
    714      * @returns Number
    715      * @memberof JXG.Math.Statistics
    716      * @example
    717      *  let board = JXG.JSXGraph.initBoard('JXGbox',
    718      *       { boundingbox: [-.5, 1.5, 5, -.1], axis: true});
    719      *
    720      *   let runs = [
    721      *       [0.5, 'red'],
    722      *       [1.0, 'green'],
    723      *       [1.5, 'blue'],
    724      *   ]
    725      *
    726      *   let labelY = 1
    727      *   runs.forEach((run,i) => {
    728      *       board.create('segment',[[1.8,labelY-(i/20)],[2.3,labelY-(i/20)]],{strokeColor:run[1]})
    729      *       board.create('text',[2.5,labelY-(i/20),`λ=${run[0]}`])
    730      *
    731      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomExponential(run[0]))
    732      *       let res = JXG.Math.Statistics.histogram(x, { bins: 40, density: true, cumulative: false, range: false });
    733      *       board.create('curve', [res[1], res[0]], { strokeColor: run[1], strokeWidth:2});
    734      *   })
    735      *
    736      * </pre><div id="JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-5" class="jxgbox" style="width: 300px; height: 300px;"></div>
    737      * <script type="text/javascript">
    738      * {
    739      *  let board = JXG.JSXGraph.initBoard('JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-5',
    740      *       { boundingbox: [-.5, 1.5, 5, -.1], axis: true});
    741      *
    742      *   let runs = [
    743      *       [0.5, 'red'],
    744      *       [1.0, 'green'],
    745      *       [1.5, 'blue'],
    746      *   ]
    747      *
    748      *   let labelY = 1
    749      *   runs.forEach((run,i) => {
    750      *       board.create('segment',[[1.8,labelY-(i/20)],[2.3,labelY-(i/20)]],{strokeColor:run[1]})
    751      *       board.create('text',[2.5,labelY-(i/20),`λ=${run[0]}`])
    752      *
    753      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomExponential(run[0]))
    754      *       let res = JXG.Math.Statistics.histogram(x, { bins: 40, density: true, cumulative: false, range: false });
    755      *       board.create('curve', [res[1], res[0]], { strokeColor: run[1], strokeWidth:2});
    756      *   })
    757      * }
    758      * </script><pre>
    759
    760     */
    761     randomExponential: function (lbda) {
    762         var u;
    763
    764         // Knuth, TAOCP 2, p 128
    765         // See https://en.wikipedia.org/wiki/Exponential_distribution
    766         if (lbda <= 0) {
    767             return NaN;
    768         }
    769
    770         do {
    771             u = Math.random();
    772         } while (u === 0);
    773
    774         return -Math.log(u) / lbda;
    775     },
    776
    777     /**
    778      * Generate value of a random variable with gamma distribution of order alpha.
    779      * See <a href="https://en.wikipedia.org/wiki/Gamma_distribution">https://en.wikipedia.org/wiki/Gamma_distribution</a>.
    780      * Algorithm: D.E. Knuth, TAOCP 2, p. 129.
    781
    782      * @param {Number} a shape, <i> > 0</i>
    783      * @param {Number} [b=1] scale, <i> > 0</i>
    784      * @param {Number} [t=0] threshold
    785      * @returns Number
    786      * @memberof JXG.Math.Statistics
    787      * @example
    788      *  let board = JXG.JSXGraph.initBoard('jxgbox',
    789      *       { boundingbox: [-1.7, .5, 20, -.03], axis: true});
    790      *
    791      *   let runs = [
    792      *       [0.5, 1.0, 'brown'],
    793      *       [1.0, 2.0, 'red'],
    794      *       [2.0, 2.0, 'orange'],
    795      *       [3.0, 2.0, 'yellow'],
    796      *       [5.0, 1.0, 'green'],
    797      *       [9.0, 0.5, 'black'],
    798      *       [7.5, 1.0, 'purple'],
    799      *   ]
    800      *
    801      *   let labelY = .4
    802      *   runs.forEach((run,i) => {
    803      *       board.create('segment',[[7,labelY-(i/50)],[9,labelY-(i/50)]],{strokeColor:run[2]})
    804      *       board.create('text',[10,labelY-(i/50),`k=${run[0]}, θ=${run[1]}`])
    805      *
    806      *       // density
    807      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomGamma(run[0],run[1]))
    808      *       let res = JXG.Math.Statistics.histogram(x, { bins: 50, density: true, cumulative: false, range: [0, 20] });
    809      *       board.create('curve', [res[1], res[0]], { strokeColor: run[2]});
    810      *
    811      *   })
    812      *
    813      *
    814      * </pre>
    815      * <div id="JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-6" class="jxgbox" style="width: 300px; height: 300px;"></div>
    816      * <script type="text/javascript">
    817      * {
    818      *  let board = JXG.JSXGraph.initBoard('JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-6',
    819      *       { boundingbox: [-1.7, .5, 20, -.03], axis: true});
    820      *
    821      *   let runs = [
    822      *       [0.5, 1.0, 'brown'],
    823      *       [1.0, 2.0, 'red'],
    824      *       [2.0, 2.0, 'orange'],
    825      *       [3.0, 2.0, 'yellow'],
    826      *       [5.0, 1.0, 'green'],
    827      *       [9.0, 0.5, 'black'],
    828      *       [7.5, 1.0, 'purple'],
    829      *   ]
    830      *
    831      *   let labelY = .4
    832      *   runs.forEach((run,i) => {
    833      *       board.create('segment',[[7,labelY-(i/50)],[9,labelY-(i/50)]],{strokeColor:run[2]})
    834      *       board.create('text',[10,labelY-(i/50),`k=${run[0]}, θ=${run[1]}`])
    835      *
    836      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomGamma(run[0],run[1]))
    837      *       let res = JXG.Math.Statistics.histogram(x, { bins: 50, density: true, cumulative: false, range: [0, 20] });
    838      *       board.create('curve', [res[1], res[0]], { strokeColor: run[2]});
    839      *   })
    840      * }
    841      * </script><pre>
    842      *
    843      */
    844     randomGamma: function (a, b, t) {
    845         var u, v, x, y,
    846             p, q;
    847
    848         if (a <= 0) {
    849             return NaN;
    850         }
    851
    852         b = b || 1;
    853         t = t || 0;
    854
    855         if (a === 1) {
    856             return b * this.randomExponential(1) + t;
    857         }
    858
    859         if (a < 1) {
    860             // Method by Ahrens
    861             // Knuth, TAOCP 2, Ex. 16, p 551
    862             p = Math.E / (a + Math.E);
    863
    864             do {
    865                 u = Math.random();
    866                 do {
    867                     v = Math.random();
    868                 } while (v === 0);
    869                 if (u < p) {
    870                     x = Math.pow(v, 1 / a);
    871                     q = Math.exp(-x);
    872                 } else {
    873                     x = 1 - Math.log(v);
    874                     q = Math.pow(x, a - 1);
    875                 }
    876                 u = Math.random();
    877             } while (u >= q);
    878             return b * x + t;
    879         }
    880
    881         // a > 1
    882         // Knuth, TAOCP 2, p 129
    883         do {
    884             y = Math.tan(Math.PI * Math.random());
    885             x = Math.sqrt(2 * a - 1) * y + a - 1;
    886             if (x > 0) {
    887                 v = Math.random();
    888             } else {
    889                 continue;
    890             }
    891         } while (x <= 0.0 || v > (1 + y * y) * Math.exp((a - 1) * Math.log(x / (a - 1)) - Math.sqrt(2 * a - 1) * y));
    892
    893         return b * x + t;
    894     },
    895
    896     /**
    897      * Generate value of a random variable with beta distribution with shape parameters alpha and beta.
    898      * See <a href="https://en.wikipedia.org/wiki/Beta_distribution">https://en.wikipedia.org/wiki/Beta_distribution</a>.
    899      *
    900      * @param {Number} alpha <i>> 0</i>
    901      * @param {Number} beta <i>> 0</i>
    902      * @returns Number
    903      * @memberof JXG.Math.Statistics
    904      */
    905     randomBeta: function (a, b) {
    906         // Knuth, TAOCP 2, p 129
    907         var x1, x2, x;
    908
    909         if (a <= 0 || b <= 0) {
    910             return NaN;
    911         }
    912
    913         x1 = this.randomGamma(a);
    914         x2 = this.randomGamma(b);
    915         x = x1 / (x1 + x2);
    916         return x;
    917     },
    918
    919     /**
    920      * Generate value of a random variable with chi-square distribution with k degrees of freedom.
    921      * See <a href="https://en.wikipedia.org/wiki/Chi-squared_distribution">https://en.wikipedia.org/wiki/Chi-squared_distribution</a>.
    922      *
    923      * @param {Number} k <i>> 0</i>
    924      * @returns Number
    925      * @memberof JXG.Math.Statistics
    926      */
    927     randomChisquare: function (nu) {
    928         // Knuth, TAOCP 2, p 130
    929
    930         if (nu <= 0) {
    931             return NaN;
    932         }
    933
    934         return 2 * this.randomGamma(nu * 0.5);
    935     },
    936
    937     /**
    938      * Generate value of a random variable with F-distribution with d<sub>1</sub> and d<sub>2</sub> degrees of freedom.
    939      * See <a href="https://en.wikipedia.org/wiki/F-distribution">https://en.wikipedia.org/wiki/F-distribution</a>.
    940      * @param {Number} d1 <i>> 0</i>
    941      * @param {Number} d2 <i>> 0</i>
    942      * @returns Number
    943      * @memberof JXG.Math.Statistics
    944      */
    945     randomF: function (nu1, nu2) {
    946         // Knuth, TAOCP 2, p 130
    947         var y1, y2;
    948
    949         if (nu1 <= 0 || nu2 <= 0) {
    950             return NaN;
    951         }
    952
    953         y1 = this.randomChisquare(nu1);
    954         y2 = this.randomChisquare(nu2);
    955
    956         return (y1 * nu2) / (y2 * nu1);
    957     },
    958
    959     /**
    960      * Generate value of a random variable with Students-t-distribution with ν degrees of freedom.
    961      * See <a href="https://en.wikipedia.org/wiki/Student%27s_t-distribution">https://en.wikipedia.org/wiki/Student%27s_t-distribution</a>.
    962      * @param {Number} nu <i>> 0</i>
    963      * @returns Number
    964      * @memberof JXG.Math.Statistics
    965      */
    966     randomT: function (nu) {
    967         // Knuth, TAOCP 2, p 130
    968         var y1, y2;
    969
    970         if (nu <= 0) {
    971             return NaN;
    972         }
    973
    974         y1 = this.randomNormal(0, 1);
    975         y2 = this.randomChisquare(nu);
    976
    977         return y1 / Math.sqrt(y2 / nu);
    978     },
    979
    980     /**
    981      * Generate values for a random variable in binomial distribution with parameters <i>n</i> and <i>p</i>.
    982      * See <a href="https://en.wikipedia.org/wiki/Binomial_distribution">https://en.wikipedia.org/wiki/Binomial_distribution</a>.
    983      * It uses algorithm BG from <a href="https://dl.acm.org/doi/pdf/10.1145/42372.42381">https://dl.acm.org/doi/pdf/10.1145/42372.42381</a>.
    984      *
    985      * @param {Number} n Number of trials (n >= 0)
    986      * @param {Number} p Probability (0 <= p <= 1)
    987      * @returns Number Integer value of a random variable in binomial distribution
    988      * @memberof JXG.Math.Statistics
    989      *
    990      * @example
    991      *  let board = JXG.JSXGraph.initBoard('JXGbox',
    992      *       { boundingbox: [-1.7, .5, 30, -.03], axis: true});
    993      *
    994      *   let runs = [
    995      *       [0.5, 20, 'blue'],
    996      *       [0.7, 20, 'green'],
    997      *       [0.5, 40, 'red'],
    998      *   ]
    999      *
    1000      *   let labelY = .4
    1001      *   runs.forEach((run,i) => {
    1002      *       board.create('segment',[[7,labelY-(i/50)],[9,labelY-(i/50)]],{strokeColor:run[2]})
    1003      *       board.create('text',[10,labelY-(i/50),`p=${run[0]}, n=${run[1]}`])
    1004      *
    1005      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomBinomial(run[1],run[0]))
    1006      *       let res = JXG.Math.Statistics.histogram(x, { bins: 40, density: true, cumulative: false, range: [0, 40] });
    1007      *       board.create('curve', [res[1], res[0]], { strokeColor: run[2]});
    1008      *   })
    1009      * }
    1010      *
    1011      *
    1012      * </pre><div id="JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-3" class="jxgbox" style="width: 300px; height: 300px;"></div>
    1013      * <script type="text/javascript">
    1014      * {
    1015      *  let board = JXG.JSXGraph.initBoard('JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-3',
    1016      *       { boundingbox: [-1.7, .5, 30, -.03], axis: true});
    1017      *
    1018      *   let runs = [
    1019      *       [0.5, 20, 'blue'],
    1020      *       [0.7, 20, 'green'],
    1021      *       [0.5, 40, 'red'],
    1022      *   ]
    1023      *
    1024      *   let labelY = .4
    1025      *   runs.forEach((run,i) => {
    1026      *       board.create('segment',[[7,labelY-(i/50)],[9,labelY-(i/50)]],{strokeColor:run[2]})
    1027      *       board.create('text',[10,labelY-(i/50),`p=${run[0]}, n=${run[1]}`])
    1028      *
    1029      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomBinomial(run[1],run[0]))
    1030      *       let res = JXG.Math.Statistics.histogram(x, { bins: 40, density: true, cumulative: false, range: [0, 40] });
    1031      *       board.create('curve', [res[1], res[0]], { strokeColor: run[2]});
    1032      *   })
    1033      * }
    1034      * </script><pre>
    1035      *
    1036      */
    1037     randomBinomial: function (n, p) {
    1038         var x, y, c,
    1039             a, b, N1;
    1040
    1041         if (p < 0 || p > 1 || n < 0) {
    1042             return NaN;
    1043         }
    1044
    1045         // Edge cases
    1046         if (p === 0) {
    1047             return 0;
    1048         }
    1049         if (p === 1) {
    1050             return n;
    1051         }
    1052
    1053         // Now, we can assume 0 < p < 1.
    1054
    1055         // Fast path for common cases
    1056         if (n === 0) {
    1057             return 0;
    1058         }
    1059         if (n === 1) {
    1060             return ((Math.random() < p) ? 1 : 0);
    1061         }
    1062
    1063         // Exploit symmetry
    1064         if (p > 0.5) {
    1065             return n - this.randomBinomial(n, 1 - p);
    1066         }
    1067
    1068         // General case: n > 1, p <= 0.5
    1069         if (n < 100) {
    1070             // n small:
    1071             // Algorithm BG (Devroye) from:
    1072             // https://dl.acm.org/doi/pdf/10.1145/42372.42381
    1073             // Time O(np) so suitable for np small only.
    1074             x = -1;
    1075             y = 0;
    1076
    1077             c = Math.log(1 - p);
    1078             if (c === 0) {
    1079                 return 0;
    1080             }
    1081
    1082             do {
    1083                 x += 1;
    1084                 y += Math.floor(Math.log(Math.random()) / c) + 1;
    1085             } while (y < n);
    1086         } else {
    1087             // n large:
    1088             // Knuth, TAOCP 2, p 131
    1089             a = 1 + Math.floor(n * 0.5);
    1090             b = n - a + 1;
    1091             x = this.randomBeta(a, b);
    1092             if (x >= p) {
    1093                 N1 = this.randomBinomial(a - 1, p / x);
    1094                 x = N1;
    1095             } else {
    1096                 N1 = this.randomBinomial(b - 1, (p - x) / (1 - x));
    1097                 x = a + N1;
    1098             }
    1099         }
    1100         return x;
    1101     },
    1102
    1103     /**
    1104      * Generate values for a random variable in geometric distribution with probability <i>p</i>.
    1105      * See <a href="https://en.wikipedia.org/wiki/Geometric_distribution">https://en.wikipedia.org/wiki/Geometric_distribution</a>.
    1106      *
    1107      * @param {Number} p (0 <= p <= 1)
    1108      * @returns Number
    1109      * @memberof JXG.Math.Statistics
    1110      */
    1111     randomGeometric: function (p) {
    1112         var u;
    1113
    1114         if (p < 0 || p > 1) {
    1115             return NaN;
    1116         }
    1117         // Knuth, TAOCP 2, p 131
    1118         u = Math.random();
    1119
    1120         return Math.ceil(Math.log(u) / Math.log(1 - p));
    1121     },
    1122
    1123     /**
    1124      * Generate values for a random variable in Poisson distribution with mean <i>mu</i>.
    1125      * See <a href="https://en.wikipedia.org/wiki/Poisson_distribution">https://en.wikipedia.org/wiki/Poisson_distribution</a>.
    1126      *
    1127      * @param {Number} mu (0 < mu)
    1128      * @returns Number
    1129      * @memberof JXG.Math.Statistics
    1130      */
    1131     randomPoisson: function (mu) {
    1132         var e = Math.exp(-mu),
    1133             N,
    1134             m = 0,
    1135             u = 1,
    1136             x,
    1137             alpha = 7 / 8;
    1138
    1139         if (mu <= 0) {
    1140             return NaN;
    1141         }
    1142
    1143         // Knuth, TAOCP 2, p 132
    1144         if (mu < 10) {
    1145             do {
    1146                 u *= Math.random();
    1147                 m += 1;
    1148             } while (u > e);
    1149             N = m - 1;
    1150         } else {
    1151             m = Math.floor(alpha * mu);
    1152             x = this.randomGamma(m);
    1153             if (x < mu) {
    1154                 N = m + this.randomPoisson(mu - x);
    1155             } else {
    1156                 N = this.randomBinomial(m - 1, mu / x);
    1157             }
    1158         }
    1159         return N;
    1160     },
    1161
    1162     /**
    1163      * Generate values for a random variable in Pareto distribution with
    1164      * shape <i>gamma</i> and scale <i>k</i>.
    1165      * See <a href="https://en.wikipedia.org/wiki/Pareto_distribution">https://en.wikipedia.org/wiki/Pareto_distribution</a>.
    1166      * Method: use inverse transformation sampling.
    1167      *
    1168      * @param {Number} gamma shape (0 < gamma)
    1169      * @param {Number} k scale (0 < k < x)
    1170      * @returns Number
    1171      * @memberof JXG.Math.Statistics
    1172      */
    1173     randomPareto: function (gamma, k) {
    1174         var u = Math.random();
    1175
    1176         if (gamma <= 0 || k <= 0) {
    1177             return NaN;
    1178         }
    1179         return k * Math.pow(1 - u, -1 / gamma);
    1180     },
    1181
    1182     /**
    1183      * Generate values for a random variable in hypergeometric distribution.
    1184      * Samples are drawn from a hypergeometric distribution with specified parameters, <i>good</i> (ways to make a good selection),
    1185      * <i>bad</i> (ways to make a bad selection), and <i>samples</i> (number of items sampled, which is less than or equal to <i>good + bad</i>).
    1186      * <p>
    1187      * Naive implementation with runtime <i>O(samples)</i>.
    1188      *
    1189      * @param {Number} good ways to make a good selection
    1190      * @param {Number} bad ways to make a bad selection
    1191      * @param {Number} samples number of items sampled
    1192      * @returns
    1193      * @memberof JXG.Math.Statistics
    1194      */
    1195     randomHypergeometric: function (good, bad, k) {
    1196         var i, u,
    1197             x = 0,
    1198             // kk,
    1199             // n = good + bad,
    1200             d1 = good + bad - k,
    1201             d2 = Math.min(good, bad),
    1202             y = d2;
    1203
    1204         if (good < 1 || bad < 1 || k > good + bad) {
    1205             return NaN;
    1206         }
    1207
    1208         // Naive method
    1209         // kk = Math.min(k, n - k);
    1210         // for (i = 0; i < k; i ++) {
    1211         //     u = Math.random();
    1212         //     if (n * u <= good) {
    1213         //         x += 1;
    1214         //         if (x === good) {
    1215         //             return x;
    1216         //         }
    1217         //         good -= 1;
    1218         //     }
    1219         //     n -= 1;
    1220         // }
    1221         // return x;
    1222
    1223         // Implementation from
    1224         // Monte Carlo by George S. Fishman
    1225         // https://link.springer.com/book/10.1007/978-1-4757-2553-7
    1226         // page 218
    1227         //
    1228         i = k;
    1229         while (y * i > 0) {
    1230             u = Math.random();
    1231             y -= Math.floor(u + y / (d1 + i));
    1232             i -= 1;
    1233         }
    1234         x = d2 - y;
    1235         if (good <= bad) {
    1236             return x;
    1237         } else {
    1238             return k - x;
    1239         }
    1240     },
    1241
    1242     /**
    1243      * Compute the histogram of a dataset.
    1244      * Optional parameters can be supplied through a JavaScript object
    1245      * with the following default values:
    1246      * <pre>
    1247      * {
    1248      *   bins: 10,          // Number of bins
    1249      *   range: false,      // false or array. The lower and upper range of the bins.
    1250      *                      // If not provided, range is simply [min(x), max(x)].
    1251      *                      // Values outside the range are ignored.
    1252      *   density: false,    // If true, normalize the counts by dividing by sum(counts)
    1253      *   cumulative: false
    1254      * }
    1255      * </pre>
    1256      * The function returns an array containing two arrays. The first array is of length bins+1
    1257      * containing the start values of the bins. The last entry contains the end values of the last bin.
    1258      * <p>
    1259      * The second array contains the counts of each bin.
    1260      * @param {Array} x
    1261      * @param {Object} opt Optional parameters
    1262      * @returns Array [bin, counts] Array bins contains start values of bins, array counts contains
    1263      * the number of entries of x which are contained in each bin.
    1264      * @memberof JXG.Math.Statistics
    1265      *
    1266      * @example
    1267      *  let board = JXG.JSXGraph.initBoard('jxgbox',
    1268      *       { boundingbox: [-1.7, .5, 20, -.03], axis: true});
    1269      *  let board2 = JXG.JSXGraph.initBoard('jxgbox2',
    1270      *       { boundingbox: [-1.6, 1.1, 20, -.06], axis: true});
    1271      *
    1272      *   let runs = [
    1273      *       [0.5, 1.0, 'brown'],
    1274      *       [1.0, 2.0, 'red'],
    1275      *       [2.0, 2.0, 'orange'],
    1276      *       [3.0, 2.0, 'yellow'],
    1277      *       [5.0, 1.0, 'green'],
    1278      *       [9.0, 0.5, 'black'],
    1279      *       [7.5, 1.0, 'purple'],
    1280      *   ]
    1281      *
    1282      *   let labelY = .4
    1283      *   runs.forEach((run,i) => {
    1284      *       board.create('segment',[[7,labelY-(i/50)],[9,labelY-(i/50)]],{strokeColor:run[2]})
    1285      *       board.create('text',[10,labelY-(i/50),`k=${run[0]}, θ=${run[1]}`])
    1286      *
    1287      *       // density
    1288      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomGamma(run[0],run[1]))
    1289      *       let res = JXG.Math.Statistics.histogram(x, { bins: 50, density: true, cumulative: false, range: [0, 20] });
    1290      *       board.create('curve', [res[1], res[0]], { strokeColor: run[2], strokeWidth:2});
    1291      *
    1292      *       // cumulative density
    1293      *       res = JXG.Math.Statistics.histogram(x, { bins: 50, density: true, cumulative: true, range: [0, 20] });
    1294      *       res[0].unshift(0)  // add zero to front so cumulative starts at zero
    1295      *       res[1].unshift(0)
    1296      *       board2.create('curve', [res[1], res[0]], { strokeColor: run[2], strokeWidth:2 });
    1297      *   })
    1298      *
    1299      *
    1300      * </pre><div id="JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302" class="jxgbox" style="width: 300px; height: 300px; float:left;"></div>
    1301      * <div style='float:left;'>  </div>
    1302      * <div id="JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-2" class="jxgbox" style="width: 300px; height: 300px;"></div>
    1303      * <script type="text/javascript">
    1304      * {
    1305      *  let board = JXG.JSXGraph.initBoard('JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302',
    1306      *       { boundingbox: [-1.7, .5, 20, -.03], axis: true});
    1307      *  let board2 = JXG.JSXGraph.initBoard('JXGda56df4d-a5a5-4c87-9ffc-9bbc1b512302-2',
    1308      *       { boundingbox: [-1.6, 1.1, 20, -.06], axis: true});
    1309      *
    1310      *   let runs = [
    1311      *       [0.5, 1.0, 'brown'],
    1312      *       [1.0, 2.0, 'red'],
    1313      *       [2.0, 2.0, 'orange'],
    1314      *       [3.0, 2.0, 'yellow'],
    1315      *       [5.0, 1.0, 'green'],
    1316      *       [9.0, 0.5, 'black'],
    1317      *       [7.5, 1.0, 'purple'],
    1318      *   ]
    1319      *
    1320      *   let labelY = .4
    1321      *   runs.forEach((run,i) => {
    1322      *       board.create('segment',[[7,labelY-(i/50)],[9,labelY-(i/50)]],{strokeColor:run[2]})
    1323      *       board.create('text',[10,labelY-(i/50),`k=${run[0]}, θ=${run[1]}`])
    1324      *
    1325      *       let x = Array(50000).fill(0).map(() => JXG.Math.Statistics.randomGamma(run[0],run[1]))
    1326      *       let res = JXG.Math.Statistics.histogram(x, { bins: 50, density: true, cumulative: false, range: [0, 20] });
    1327      *       board.create('curve', [res[1], res[0]], { strokeColor: run[2], strokeWidth:2});
    1328      *
    1329      *       // cumulative density
    1330      *       res = JXG.Math.Statistics.histogram(x, { bins: 50, density: true, cumulative: true, range: [0, 20] });
    1331      *       res[0].unshift(0)  // add zero to front so cumulative starts at zero
    1332      *       res[1].unshift(0)
    1333      *       board2.create('curve', [res[1], res[0]], { strokeColor: run[2], strokeWidth:2 });
    1334      *   })
    1335      * }
    1336      * </script><pre>
    1337      *
    1338      */
    1339     histogram: function (x, opt) {
    1340         var i, le, k,
    1341             mi, ma, num_bins, delta,
    1342             range,
    1343             s,
    1344             counts = [],
    1345             bins = [];
    1346
    1347         // Evaluate number of bins
    1348         num_bins = opt.bins || 10;
    1349
    1350         // Evaluate range
    1351         range = opt.range || false;
    1352         if (range === false) {
    1353             mi = Math.min.apply(null, x);
    1354             ma = Math.max.apply(null, x);
    1355         } else {
    1356             mi = range[0];
    1357             ma = range[1];
    1358         }
    1359
    1360         // Set uniform delta
    1361         if (num_bins > 0) {
    1362             delta = (ma - mi) / (num_bins - 1);
    1363         } else {
    1364             delta = 0;
    1365         }
    1366
    1367         // Set the bins and init the counts array
    1368         for (i = 0; i < num_bins; i++) {
    1369             counts.push(0);
    1370             bins.push(mi + i * delta);
    1371         }
    1372         // bins.push(ma);
    1373
    1374         // Determine the counts
    1375         le = x.length;
    1376         for (i = 0; i < le; i++) {
    1377             k = Math.floor((x[i] - mi) / delta);
    1378             if (k >= 0 && k < num_bins) {
    1379                 counts[k] += 1;
    1380             }
    1381         }
    1382
    1383         // Normalize if density===true
    1384         if (opt.density) {
    1385             s = JXG.Math.Statistics.sum(counts);
    1386             for (i = 0; i < num_bins; i++) {
    1387                 counts[i] /= (s * delta);
    1388                 // counts[i] /= s;
    1389             }
    1390         }
    1391
    1392         // Cumulative counts
    1393         if (opt.cumulative) {
    1394             if (opt.density) {
    1395                 for (i = 0; i < num_bins; i++) {
    1396                     counts[i] *= delta;  // normalize
    1397                 }
    1398             } for (i = 1; i < num_bins; i++) {
    1399                 counts[i] += counts[i - 1];
    1400             }
    1401         }
    1402
    1403         return [counts, bins];
    1404     }
    1405 };
    1406
    1407 export default Mat.Statistics;
    1408