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simple-statistics
Advanced tools
simple-statistics - npm Package Compare versions
Comparing version 2.5.0 to 3.0.0
@@ -0,1 +1,18 @@ | ||
| <a name="3.0.0"></a> | ||
| # [3.0.0](https://github.com/simple-statistics/simple-statistics/compare/v2.5.0...v3.0.0) (2017-04-06) | ||
| **Breaking change**: before this release, simple-statistics would return `NaN` | ||
| when provided with invalid input. After 3.0.0, simple-statistics will throw | ||
| exceptions when provided with invalid input. If you previously used `isNaN` to | ||
| test for these error cases, switch to using `try` and `catch`, or make sure | ||
| that valid input is given to simple-statistics. | ||
| ### Features | ||
| * **mean:** combineMeans, a method for combining calculated means ([d9e3ebc](https://github.com/simple-statistics/simple-statistics/commit/d9e3ebc)) | ||
| * **mean:** subtractFromMean, a method to remove a value from the mean ([afe76e9](https://github.com/simple-statistics/simple-statistics/commit/afe76e9)) | ||
| * **variance:** combineVariances, a method for combining pre-calculated variances of two dataset ([68133f7](https://github.com/simple-statistics/simple-statistics/commit/68133f7)) | ||
| <a name="2.5.0"></a> | ||
@@ -2,0 +19,0 @@ # [2.5.0](https://github.com/simple-statistics/simple-statistics/compare/v2.4.0...v2.5.0) (2017-02-24) |
@@ -1,2 +0,2 @@ | ||
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e=t.length,a=r.length;if(!e||!a)return null;n||(n=0);var u=o(t),s=o(r),f=i(t),c=i(r);if("number"==typeof u&&"number"==typeof s&&"number"==typeof f&&"number"==typeof c){var h=((e-1)*f+(a-1)*c)/(e+a-2);return(u-s-n)/Math.sqrt(h*(1/e+1/a))}}var o=t(28),i=t(54);r.exports=e},{28:28,54:54}],67:[function(t,r,n){"use strict";function e(t){for(var r,n=0,e=0;e<t.length;e++)0!==e&&t[e]===r||(r=t[e],n++);return n}r.exports=e},{}],68:[function(t,r,n){"use strict";function e(t){if(0===t.length)throw new Error("variance requires at least one data point");return o(t,2)/t.length}var o=t(63);r.exports=e},{63:63}],69:[function(t,r,n){"use strict";function e(t,r,n){return(t-r)/n}r.exports=e},{}]},{},[1])(1)}); | ||
| //# sourceMappingURL=dist/simple-statistics.min.js.map |
@@ -53,2 +53,4 @@ /* @flow */ | ||
| ss.addToMean = require('./src/add_to_mean'); | ||
| ss.combineMeans = require('./src/combine_means'); | ||
| ss.combineVariances = require('./src/combine_variances'); | ||
| ss.geometricMean = require('./src/geometric_mean'); | ||
@@ -59,2 +61,3 @@ ss.harmonicMean = require('./src/harmonic_mean'); | ||
| ss.medianSorted = require('./src/median_sorted'); | ||
| ss.subtractFromMean = require('./src/subtract_from_mean'); | ||
@@ -61,0 +64,0 @@ ss.rootMeanSquare = ss.rms = require('./src/root_mean_square'); |
| { | ||
| "name": "simple-statistics", | ||
| "version": "2.5.0", | ||
| "version": "3.0.0", | ||
| "description": "Simple Statistics", | ||
@@ -5,0 +5,0 @@ "author": "Tom MacWright <tom@macwright.org> (http://macwright.org/)", |
@@ -12,3 +12,5 @@ # Simple Statistics | ||
| ## :green_book: [API Documentation](http://simplestatistics.org/docs/) | ||
| * :green_book: [API Documentation](http://simplestatistics.org/docs/) | ||
| * :chart_with_upwards_trend: [Benchmarks](./benchmarks/) | ||
| * :kissing: [A list of other statistics libraries](./SEEALSO.md) | ||
@@ -24,17 +26,7 @@ ## Installation | ||
| * `https://unpkg.com/simple-statistics@2.5.0/dist/simple-statistics.js` | ||
| * Minified: `https://unpkg.com/simple-statistics@2.5.0/dist/simple-statistics.min.js` | ||
| * `https://unpkg.com/simple-statistics@3.0.0/dist/simple-statistics.js` | ||
| * Minified: `https://unpkg.com/simple-statistics@3.0.0/dist/simple-statistics.min.js` | ||
| Bower: | ||
| * Bower: `https://unpkg.com/simple-statistics@2.5.0/bower.zip` | ||
| ## Ports | ||
| * Python: [simplestatistics](https://github.com/sheriferson/simplestatistics) is an idiomatic implementation of basic statistical algorithms. | ||
| ## Examples | ||
| * [Linear regression with simple-statistics and d3js](http://bl.ocks.org/3931800) | ||
| * [Jenks Natural Breaks with a choropleth map with d3js](http://bl.ocks.org/tmcw/4969184) | ||
| * Embedded statistics in a spreadsheet with nxsheet: [regression](https://nxsheet.com/sheets/56d0a87264e47ee60a95f652) and [stddev](https://nxsheet.com/sheets/56d55ca7743aea68490764ba) | ||
| * Bower: `https://unpkg.com/simple-statistics@3.0.0/bower.zip` |
@@ -19,2 +19,3 @@ 'use strict'; | ||
| * @returns {number} value of bernoulli distribution at this point | ||
| * @throws {Error} if p is outside 0 and 1 | ||
| * @example | ||
@@ -25,3 +26,5 @@ * bernoulliDistribution(0.5); // => { '0': 0.5, '1': 0.5 } | ||
| // Check that `p` is a valid probability (0 ≤ p ≤ 1) | ||
| if (p < 0 || p > 1 ) { return NaN; } | ||
| if (p < 0 || p > 1 ) { | ||
| throw new Error('bernoulliDistribution requires probability to be between 0 and 1 inclusive'); | ||
| } | ||
@@ -28,0 +31,0 @@ return binomialDistribution(1, p); |
@@ -10,8 +10,9 @@ 'use strict'; | ||
| * | ||
| * `sample` is expected to be an array, and `chunkSize` a number. | ||
| * The `sample` array can contain any kind of data. | ||
| * `x` is expected to be an array, and `chunkSize` a number. | ||
| * The `x` array can contain any kind of data. | ||
| * | ||
| * @param {Array} sample any array of values | ||
| * @param {number} chunkSize size of each output array | ||
| * @param {Array} x a sample | ||
| * @param {number} chunkSize size of each output array. must be a positive integer | ||
| * @returns {Array<Array>} a chunked array | ||
| * @throws {Error} if chunk size is less than 1 or not an integer | ||
| * @example | ||
@@ -21,3 +22,3 @@ * chunk([1, 2, 3, 4, 5, 6], 2); | ||
| */ | ||
| function chunk(sample/*:Array<any>*/, chunkSize/*:number*/)/*:?Array<Array<any>>*/ { | ||
| function chunk(x/*:Array<any>*/, chunkSize/*:number*/)/*:?Array<Array<any>>*/ { | ||
@@ -31,9 +32,13 @@ // a list of result chunks, as arrays in an array | ||
| // invalid input. | ||
| if (chunkSize <= 0) { | ||
| throw new Error('chunk size must be a positive integer'); | ||
| if (chunkSize < 1) { | ||
| throw new Error('chunk size must be a positive number'); | ||
| } | ||
| if (Math.floor(chunkSize) !== chunkSize) { | ||
| throw new Error('chunk size must be an integer'); | ||
| } | ||
| // `start` is the index at which `.slice` will start selecting | ||
| // new array elements | ||
| for (var start = 0; start < sample.length; start += chunkSize) { | ||
| for (var start = 0; start < x.length; start += chunkSize) { | ||
@@ -43,3 +48,3 @@ // for each chunk, slice that part of the array and add it | ||
| // the original array. | ||
| output.push(sample.slice(start, start + chunkSize)); | ||
| output.push(x.slice(start, start + chunkSize)); | ||
| } | ||
@@ -46,0 +51,0 @@ return output; |
@@ -207,6 +207,7 @@ 'use strict'; | ||
| * from The R Journal Vol. 3/2, December 2011 | ||
| * @param {Array<number>} data input data, as an array of number values | ||
| * @param {Array<number>} x input data, as an array of number values | ||
| * @param {number} nClusters number of desired classes. This cannot be | ||
| * greater than the number of values in the data array. | ||
| * @returns {Array<Array<number>>} clustered input | ||
| * @throws {Error} if the number of requested clusters is higher than the size of the data | ||
| * @example | ||
@@ -217,9 +218,9 @@ * ckmeans([-1, 2, -1, 2, 4, 5, 6, -1, 2, -1], 3); | ||
| */ | ||
| function ckmeans(data/*: Array<number> */, nClusters/*: number */)/*: Array<Array<number>> */ { | ||
| function ckmeans(x/*: Array<number> */, nClusters/*: number */)/*: Array<Array<number>> */ { | ||
| if (nClusters > data.length) { | ||
| throw new Error('Cannot generate more classes than there are data values'); | ||
| if (nClusters > x.length) { | ||
| throw new Error('cannot generate more classes than there are data values'); | ||
| } | ||
| var sorted = numericSort(data), | ||
| var sorted = numericSort(x), | ||
| // we'll use this as the maximum number of clusters | ||
@@ -226,0 +227,0 @@ uniqueCount = uniqueCountSorted(sorted); |
@@ -6,7 +6,7 @@ /* @flow */ | ||
| * Implementation of [Combinations](https://en.wikipedia.org/wiki/Combination) with replacement | ||
| * Combinations are unique subsets of a collection - in this case, k elements from a collection at a time. | ||
| * Combinations are unique subsets of a collection - in this case, k x from a collection at a time. | ||
| * 'With replacement' means that a given element can be chosen multiple times. | ||
| * Unlike permutation, order doesn't matter for combinations. | ||
| * | ||
| * @param {Array} elements any type of data | ||
| * @param {Array} x any type of data | ||
| * @param {int} k the number of objects in each group (without replacement) | ||
@@ -18,3 +18,3 @@ * @returns {Array<Array>} array of permutations | ||
| function combinationsReplacement( | ||
| elements /*: Array<any> */, | ||
| x /*: Array<any> */, | ||
| k /*: number */) { | ||
@@ -24,7 +24,7 @@ | ||
| for (var i = 0; i < elements.length; i++) { | ||
| for (var i = 0; i < x.length; i++) { | ||
| if (k === 1) { | ||
| // If we're requested to find only one element, we don't need | ||
| // to recurse: just push `elements[i]` onto the list of combinations. | ||
| combinationList.push([elements[i]]) | ||
| // to recurse: just push `x[i]` onto the list of combinations. | ||
| combinationList.push([x[i]]) | ||
| } else { | ||
@@ -34,3 +34,3 @@ // Otherwise, recursively find combinations, given `k - 1`. Note that | ||
| // requesting k=2. This -1 gets reversed in the for loop right after this | ||
| // code, since we concatenate `elements[i]` onto the selected combinations, | ||
| // code, since we concatenate `x[i]` onto the selected combinations, | ||
| // bringing `k` back up to your requested level. | ||
@@ -40,7 +40,7 @@ // This recursion may go many levels deep, since it only stops once | ||
| var subsetCombinations = combinationsReplacement( | ||
| elements.slice(i, elements.length), | ||
| x.slice(i, x.length), | ||
| k - 1); | ||
| for (var j = 0; j < subsetCombinations.length; j++) { | ||
| combinationList.push([elements[i]] | ||
| combinationList.push([x[i]] | ||
| .concat(subsetCombinations[j])); | ||
@@ -47,0 +47,0 @@ } |
@@ -5,5 +5,5 @@ /* @flow */ | ||
| * Implementation of Combinations | ||
| * Combinations are unique subsets of a collection - in this case, k elements from a collection at a time. | ||
| * Combinations are unique subsets of a collection - in this case, k x from a collection at a time. | ||
| * https://en.wikipedia.org/wiki/Combination | ||
| * @param {Array} elements any type of data | ||
| * @param {Array} x any type of data | ||
| * @param {int} k the number of objects in each group (without replacement) | ||
@@ -15,3 +15,3 @@ * @returns {Array<Array>} array of permutations | ||
| function combinations(elements /*: Array<any> */, k/*: number */) { | ||
| function combinations(x /*: Array<any> */, k/*: number */) { | ||
| var i; | ||
@@ -23,10 +23,10 @@ var subI; | ||
| for (i = 0; i < elements.length; i++) { | ||
| for (i = 0; i < x.length; i++) { | ||
| if (k === 1) { | ||
| combinationList.push([elements[i]]) | ||
| combinationList.push([x[i]]) | ||
| } else { | ||
| subsetCombinations = combinations(elements.slice( i + 1, elements.length ), k - 1); | ||
| subsetCombinations = combinations(x.slice( i + 1, x.length ), k - 1); | ||
| for (subI = 0; subI < subsetCombinations.length; subI++) { | ||
| next = subsetCombinations[subI]; | ||
| next.unshift(elements[i]); | ||
| next.unshift(x[i]); | ||
| combinationList.push(next); | ||
@@ -33,0 +33,0 @@ } |
@@ -8,9 +8,9 @@ 'use strict'; | ||
| /** | ||
| * Given an array of data, this will find the extent of the | ||
| * data and return an array of breaks that can be used | ||
| * to categorize the data into a number of classes. The | ||
| * Given an array of x, this will find the extent of the | ||
| * x and return an array of breaks that can be used | ||
| * to categorize the x into a number of classes. The | ||
| * returned array will always be 1 longer than the number of | ||
| * classes because it includes the minimum value. | ||
| * | ||
| * @param {Array<number>} data input data, as an array of number values | ||
| * @param {Array<number>} x an array of number values | ||
| * @param {number} nClasses number of desired classes | ||
@@ -21,16 +21,16 @@ * @returns {Array<number>} array of class break positions | ||
| */ | ||
| function equalIntervalBreaks(data/*: Array<number> */, nClasses/*:number*/)/*: Array<number> */ { | ||
| function equalIntervalBreaks(x/*: Array<number> */, nClasses/*:number*/)/*: Array<number> */ { | ||
| if (data.length <= 1) { | ||
| return data; | ||
| if (x.length < 2) { | ||
| return x; | ||
| } | ||
| var theMin = min(data), | ||
| theMax = max(data); | ||
| var theMin = min(x), | ||
| theMax = max(x); | ||
| // the first break will always be the minimum value | ||
| // in the dataset | ||
| // in the xset | ||
| var breaks = [theMin]; | ||
| // The size of each break is the full range of the data | ||
| // The size of each break is the full range of the x | ||
| // divided by the number of classes requested | ||
@@ -37,0 +37,0 @@ var breakSize = (theMax - theMin) / nClasses; |
@@ -10,4 +10,5 @@ 'use strict'; | ||
| * | ||
| * @param {number} n input | ||
| * @param {number} n input, must be an integer number 1 or greater | ||
| * @returns {number} factorial: n! | ||
| * @throws {Error} if n is less than 0 or not an integer | ||
| * @example | ||
@@ -19,4 +20,10 @@ * factorial(5); // => 120 | ||
| // factorial is mathematically undefined for negative numbers | ||
| if (n < 0) { return NaN; } | ||
| if (n < 0) { | ||
| throw new Error('factorial requires an integer input'); | ||
| } | ||
| if (Math.floor(n) !== n) { | ||
| throw new Error('factorial requires a non-negative value'); | ||
| } | ||
| // typically you'll expand the factorial function going down, like | ||
@@ -23,0 +30,0 @@ // 5! = 5 * 4 * 3 * 2 * 1. This is going in the opposite direction, |
@@ -20,4 +20,6 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x input array | ||
| * @param {Array<number>} x sample of one or more data points | ||
| * @returns {number} geometric mean | ||
| * @throws {Error} if x is empty | ||
| * @throws {Error} if x contains a negative number | ||
| * @example | ||
@@ -39,3 +41,5 @@ * var growthRates = [1.80, 1.166666, 1.428571]; | ||
| // The mean of no numbers is null | ||
| if (x.length === 0) { return undefined; } | ||
| if (x.length === 0) { | ||
| throw new Error('geometricMean requires at least one data point'); | ||
| } | ||
@@ -47,3 +51,5 @@ // the starting value. | ||
| // the geometric mean is only valid for positive numbers | ||
| if (x[i] <= 0) { return undefined; } | ||
| if (x[i] <= 0) { | ||
| throw new Error('geometricMean requires only positive numbers as input'); | ||
| } | ||
@@ -50,0 +56,0 @@ // repeatedly multiply the value by each number |
@@ -15,4 +15,6 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x input | ||
| * @param {Array<number>} x sample of one or more data points | ||
| * @returns {number} harmonic mean | ||
| * @throws {Error} if x is empty | ||
| * @throws {Error} if x contains a negative number | ||
| * @example | ||
@@ -23,3 +25,5 @@ * harmonicMean([2, 3]).toFixed(2) // => '2.40' | ||
| // The mean of no numbers is null | ||
| if (x.length === 0) { return undefined; } | ||
| if (x.length === 0) { | ||
| throw new Error('harmonicMean requires at least one data point'); | ||
| } | ||
@@ -30,3 +34,5 @@ var reciprocalSum = 0; | ||
| // the harmonic mean is only valid for positive numbers | ||
| if (x[i] <= 0) { return undefined; } | ||
| if (x[i] <= 0) { | ||
| throw new Error('harmonicMean requires only positive numbers as input'); | ||
| } | ||
@@ -33,0 +39,0 @@ reciprocalSum += 1 / x[i]; |
@@ -12,3 +12,3 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} sample | ||
| * @param {Array<number>} x sample of one or more numbers | ||
| * @returns {number} interquartile range: the span between lower and upper quartile, | ||
@@ -19,7 +19,7 @@ * 0.25 and 0.75 | ||
| */ | ||
| function interquartileRange(sample/*: Array<number> */) { | ||
| function interquartileRange(x/*: Array<number> */) { | ||
| // Interquartile range is the span between the upper quartile, | ||
| // at `0.75`, and lower quartile, `0.25` | ||
| var q1 = quantile(sample, 0.75), | ||
| q2 = quantile(sample, 0.25); | ||
| var q1 = quantile(x, 0.75), | ||
| q2 = quantile(x, 0.25); | ||
@@ -26,0 +26,0 @@ if (typeof q1 === 'number' && typeof q2 === 'number') { |
@@ -9,4 +9,5 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x input | ||
| * @param {Array<number>} x sample of one or more data points | ||
| * @returns {number} maximum value | ||
| * @throws {Error} if the the length of x is less than one | ||
| * @example | ||
@@ -26,3 +27,3 @@ * max([1, 2, 3, 4]); | ||
| if (value === undefined) { | ||
| return NaN; | ||
| throw new Error('max requires at least one data point'); | ||
| } | ||
@@ -29,0 +30,0 @@ return value; |
@@ -14,3 +14,4 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x input values | ||
| * @param {Array<number>} x sample of one or more data points | ||
| * @throws {Error} if the the length of x is less than one | ||
| * @returns {number} mean | ||
@@ -22,3 +23,5 @@ * @example | ||
| // The mean of no numbers is null | ||
| if (x.length === 0) { return NaN; } | ||
| if (x.length === 0) { | ||
| throw new Error('mean requires at least one data point'); | ||
| } | ||
@@ -25,0 +28,0 @@ return sum(x) / x.length; |
@@ -7,3 +7,4 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x input | ||
| * @param {Array<number>} x sample of one or more data points | ||
| * @throws {Error} if the the length of x is less than one | ||
| * @returns {number} minimum value | ||
@@ -23,3 +24,3 @@ * @example | ||
| if (value === undefined) { | ||
| return NaN; | ||
| throw new Error('min requires at least one data point'); | ||
| } | ||
@@ -26,0 +27,0 @@ return value; |
@@ -14,2 +14,3 @@ 'use strict'; | ||
| * by changing the global `Array.prototype`. | ||
| * @throws {Error} if the JavaScript environment doesn't support Object.defineProperty | ||
| * @returns {*} the extended Array, or Array.prototype if no object | ||
@@ -16,0 +17,0 @@ * is given. |
@@ -20,5 +20,6 @@ 'use strict'; | ||
| * | ||
| * @param {Array<*>} x input | ||
| * @param {Array<*>} x a sample of one or more data points | ||
| * @returns {?*} mode | ||
| * @throws {ReferenceError} if the JavaScript environment doesn't support Map | ||
| * @throws {Error} if x is empty | ||
| * @example | ||
@@ -52,2 +53,6 @@ * modeFast(['rabbits', 'rabbits', 'squirrels']); // => 'rabbits' | ||
| if (modeCount === 0) { | ||
| throw new Error('mode requires at last one data point'); | ||
| } | ||
| return mode; | ||
@@ -54,0 +59,0 @@ } |
@@ -14,4 +14,5 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} sorted input | ||
| * @param {Array<number>} sorted a sample of one or more data points | ||
| * @returns {number} mode | ||
| * @throws {Error} if sorted is empty | ||
| * @example | ||
@@ -23,5 +24,8 @@ * modeSorted([0, 0, 1]); // => 0 | ||
| // Handle edge cases: | ||
| // The mode of an empty list is NaN | ||
| if (sorted.length === 0) { return NaN; } | ||
| else if (sorted.length === 1) { return sorted[0]; } | ||
| // The mode of an empty list is undefined | ||
| if (sorted.length === 0) { | ||
| throw new Error('mode requires at least one data point'); | ||
| } else if (sorted.length === 1) { | ||
| return sorted[0]; | ||
| } | ||
@@ -28,0 +32,0 @@ // This assumes it is dealing with an array of size > 1, since size |
@@ -15,3 +15,3 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} array input array | ||
| * @param {Array<number>} x input array | ||
| * @return {Array<number>} sorted array | ||
@@ -22,4 +22,4 @@ * @private | ||
| */ | ||
| function numericSort(array /*: Array<number> */) /*: Array<number> */ { | ||
| return array | ||
| function numericSort(x /*: Array<number> */) /*: Array<number> */ { | ||
| return x | ||
| // ensure the array is not changed in-place | ||
@@ -26,0 +26,0 @@ .slice() |
@@ -9,29 +9,33 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} sample input data | ||
| * @param {Array<number>} x sample of one or more data points | ||
| * @param {number} p desired quantile: a number between 0 to 1, inclusive | ||
| * @returns {number} quantile value | ||
| * @throws {Error} if p ix outside of the range from 0 to 1 | ||
| * @throws {Error} if x is empty | ||
| * @example | ||
| * quantileSorted([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], 0.5); // => 9 | ||
| */ | ||
| function quantileSorted(sample /*: Array<number> */, p /*: number */)/*:number*/ { | ||
| var idx = sample.length * p; | ||
| if (p < 0 || p > 1) { | ||
| return NaN; | ||
| function quantileSorted(x /*: Array<number> */, p /*: number */)/*:number*/ { | ||
| var idx = x.length * p; | ||
| if (x.length === 0) { | ||
| throw new Error('quantile requires at least one data point.'); | ||
| } else if (p < 0 || p > 1) { | ||
| throw new Error('quantiles must be between 0 and 1'); | ||
| } else if (p === 1) { | ||
| // If p is 1, directly return the last element | ||
| return sample[sample.length - 1]; | ||
| return x[x.length - 1]; | ||
| } else if (p === 0) { | ||
| // If p is 0, directly return the first element | ||
| return sample[0]; | ||
| return x[0]; | ||
| } else if (idx % 1 !== 0) { | ||
| // If p is not integer, return the next element in array | ||
| return sample[Math.ceil(idx) - 1]; | ||
| } else if (sample.length % 2 === 0) { | ||
| return x[Math.ceil(idx) - 1]; | ||
| } else if (x.length % 2 === 0) { | ||
| // If the list has even-length, we'll take the average of this number | ||
| // and the next value, if there is one | ||
| return (sample[idx - 1] + sample[idx]) / 2; | ||
| return (x[idx - 1] + x[idx]) / 2; | ||
| } else { | ||
| // Finally, in the simple case of an integer value | ||
| // with an odd-length list, return the sample value at the index. | ||
| return sample[idx]; | ||
| // with an odd-length list, return the x value at the index. | ||
| return x[idx]; | ||
| } | ||
@@ -38,0 +42,0 @@ } |
@@ -22,3 +22,3 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} sample a sample from the population | ||
| * @param {Array<number>} x sample of one or more numbers | ||
| * @param {number} p the desired quantile, as a number between 0 and 1 | ||
@@ -29,4 +29,4 @@ * @returns {number} quantile | ||
| */ | ||
| function quantile(sample /*: Array<number> */, p /*: Array<number> | number */) { | ||
| var copy = sample.slice(); | ||
| function quantile(x /*: Array<number> */, p /*: Array<number> | number */) { | ||
| var copy = x.slice(); | ||
@@ -33,0 +33,0 @@ if (Array.isArray(p)) { |
@@ -10,3 +10,3 @@ 'use strict'; | ||
| * | ||
| * @param {Array<Array<number>>} data input data: this should be doubly-nested | ||
| * @param {Array<Array<number>>} x input data: this should be doubly-nested | ||
| * @param {Function} func function called on `[i][0]` values within the dataset | ||
@@ -19,4 +19,4 @@ * @returns {number} r-squared value | ||
| */ | ||
| function rSquared(data /*: Array<Array<number>> */, func /*: Function */) /*: number */ { | ||
| if (data.length < 2) { return 1; } | ||
| function rSquared(x /*: Array<Array<number>> */, func /*: Function */) /*: number */ { | ||
| if (x.length < 2) { return 1; } | ||
@@ -27,6 +27,6 @@ // Compute the average y value for the actual | ||
| var sum = 0, average; | ||
| for (var i = 0; i < data.length; i++) { | ||
| sum += data[i][1]; | ||
| for (var i = 0; i < x.length; i++) { | ||
| sum += x[i][1]; | ||
| } | ||
| average = sum / data.length; | ||
| average = sum / x.length; | ||
@@ -37,4 +37,4 @@ // Compute the total sum of squares - the | ||
| var sumOfSquares = 0; | ||
| for (var j = 0; j < data.length; j++) { | ||
| sumOfSquares += Math.pow(average - data[j][1], 2); | ||
| for (var j = 0; j < x.length; j++) { | ||
| sumOfSquares += Math.pow(average - x[j][1], 2); | ||
| } | ||
@@ -46,4 +46,4 @@ | ||
| var err = 0; | ||
| for (var k = 0; k < data.length; k++) { | ||
| err += Math.pow(data[k][1] - func(data[k][0]), 2); | ||
| for (var k = 0; k < x.length; k++) { | ||
| err += Math.pow(x[k][1] - func(x[k][0]), 2); | ||
| } | ||
@@ -50,0 +50,0 @@ |
@@ -12,4 +12,5 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x input | ||
| * @param {Array<number>} x a sample of one or more data points | ||
| * @returns {number} root mean square | ||
| * @throws {Error} if x is empty | ||
| * @example | ||
@@ -19,3 +20,5 @@ * rootMeanSquare([-1, 1, -1, 1]); // => 1 | ||
| function rootMeanSquare(x /*: Array<number> */)/*:number*/ { | ||
| if (x.length === 0) { return NaN; } | ||
| if (x.length === 0) { | ||
| throw new Error('rootMeanSquare requires at least one data point'); | ||
| } | ||
@@ -22,0 +25,0 @@ var sumOfSquares = 0; |
@@ -11,4 +11,6 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x first input | ||
| * @param {Array<number>} y second input | ||
| * @param {Array<number>} x a sample of two or more data points | ||
| * @param {Array<number>} y a sample of two or more data points | ||
| * @throws {Error} if x and y do not have equal lengths | ||
| * @throws {Error} if x or y have length of one or less | ||
| * @returns {number} sample covariance | ||
@@ -21,6 +23,10 @@ * @example | ||
| // The two datasets must have the same length which must be more than 1 | ||
| if (x.length <= 1 || x.length !== y.length) { | ||
| return NaN; | ||
| if (x.length !== y.length) { | ||
| throw new Error('sampleCovariance requires samples with equal lengths'); | ||
| } | ||
| if (x.length < 2) { | ||
| throw new Error('sampleCovariance requires at least two data points in each sample'); | ||
| } | ||
| // determine the mean of each dataset so that we can judge each | ||
@@ -27,0 +33,0 @@ // value of the dataset fairly as the difference from the mean. this |
@@ -17,4 +17,5 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x input | ||
| * @param {Array<number>} x a sample of 3 or more data points | ||
| * @returns {number} sample skewness | ||
| * @throws {Error} if x has length of 3 or less | ||
| * @example | ||
@@ -27,4 +28,4 @@ * sampleSkewness([2, 4, 6, 3, 1]); // => 0.590128656384365 | ||
| if (isNaN(theSampleStandardDeviation) || x.length < 3) { | ||
| return NaN; | ||
| if (x.length < 3) { | ||
| throw new Error('sampleSkewness requires at least three data points'); | ||
| } | ||
@@ -31,0 +32,0 @@ |
@@ -19,3 +19,2 @@ 'use strict'; | ||
| var sampleVarianceX = sampleVariance(x); | ||
| if (isNaN(sampleVarianceX)) { return NaN; } | ||
| return Math.sqrt(sampleVarianceX); | ||
@@ -22,0 +21,0 @@ } |
@@ -17,3 +17,4 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x input array | ||
| * @param {Array<number>} x a sample of two or more data points | ||
| * @throws {Error} if the length of x is less than 2 | ||
| * @return {number} sample variance | ||
@@ -25,3 +26,5 @@ * @example | ||
| // The variance of no numbers is null | ||
| if (x.length <= 1) { return NaN; } | ||
| if (x.length < 2) { | ||
| throw new Error('sampleVariance requires at least two data points'); | ||
| } | ||
@@ -28,0 +31,0 @@ var sumSquaredDeviationsValue = sumNthPowerDeviations(x, 2); |
@@ -8,3 +8,3 @@ 'use strict'; | ||
| * | ||
| * @param {Array} population an array of any kind of element | ||
| * @param {Array<*>} x an array of any kind of value | ||
| * @param {number} n count of how many elements to take | ||
@@ -18,7 +18,7 @@ * @param {Function} [randomSource=Math.random] an optional entropy source that | ||
| */ | ||
| function sampleWithReplacement/*::<T>*/(population/*:Array<T>*/, | ||
| function sampleWithReplacement/*::<T>*/(x/*:Array<T>*/, | ||
| n /*: number */, | ||
| randomSource/*:Function*/) { | ||
| if (population.length === 0) { | ||
| if (x.length === 0) { | ||
| return []; | ||
@@ -32,3 +32,3 @@ } | ||
| var length = population.length; | ||
| var length = x.length; | ||
| var sample = []; | ||
@@ -39,3 +39,3 @@ | ||
| sample.push(population[index]); | ||
| sample.push(x[index]); | ||
| } | ||
@@ -42,0 +42,0 @@ |
@@ -13,3 +13,3 @@ 'use strict'; | ||
| * | ||
| * @param {Array} array input array. can contain any type | ||
| * @param {Array<any>} x input array. can contain any type | ||
| * @param {number} n count of how many elements to take | ||
@@ -24,7 +24,7 @@ * @param {Function} [randomSource=Math.random] an optional entropy source that | ||
| function sample/*:: <T> */( | ||
| array /*: Array<T> */, | ||
| x /*: Array<T> */, | ||
| n /*: number */, | ||
| randomSource /*: Function */) /*: Array<T> */ { | ||
| // shuffle the original array using a fisher-yates shuffle | ||
| var shuffled = shuffle(array, randomSource); | ||
| var shuffled = shuffle(x, randomSource); | ||
@@ -31,0 +31,0 @@ // and then return a subset of it - the first `n` elements. |
@@ -12,14 +12,13 @@ 'use strict'; | ||
| * | ||
| * @param {Array} sample input array | ||
| * @param {Array} x sample of one or more numbers | ||
| * @param {Function} [randomSource=Math.random] an optional entropy source that | ||
| * returns numbers between 0 inclusive and 1 exclusive: the range [0, 1) | ||
| * @returns {Array} sample | ||
| * @returns {Array} x | ||
| * @example | ||
| * var sample = [1, 2, 3, 4]; | ||
| * shuffleInPlace(sample); | ||
| * // sample is shuffled to a value like [2, 1, 4, 3] | ||
| * var x = [1, 2, 3, 4]; | ||
| * shuffleInPlace(x); | ||
| * // x is shuffled to a value like [2, 1, 4, 3] | ||
| */ | ||
| function shuffleInPlace(sample/*:Array<any>*/, randomSource/*:Function*/)/*:Array<any>*/ { | ||
| function shuffleInPlace(x/*:Array<any>*/, randomSource/*:Function*/)/*:Array<any>*/ { | ||
| // a custom random number source can be provided if you want to use | ||
@@ -30,5 +29,5 @@ // a fixed seed or another random number generator, like | ||
| // store the current length of the sample to determine | ||
| // store the current length of the x to determine | ||
| // when no elements remain to shuffle. | ||
| var length = sample.length; | ||
| var length = x.length; | ||
@@ -49,12 +48,12 @@ // temporary is used to hold an item when it is being | ||
| // store the value that we'll move temporarily | ||
| temporary = sample[length]; | ||
| temporary = x[length]; | ||
| // swap the value at `sample[length]` with `sample[index]` | ||
| sample[length] = sample[index]; | ||
| sample[index] = temporary; | ||
| // swap the value at `x[length]` with `x[index]` | ||
| x[length] = x[index]; | ||
| x[index] = temporary; | ||
| } | ||
| return sample; | ||
| return x; | ||
| } | ||
| module.exports = shuffleInPlace; |
@@ -12,3 +12,3 @@ 'use strict'; | ||
| * | ||
| * @param {Array} sample an array of any kind of element | ||
| * @param {Array} x sample of 0 or more numbers | ||
| * @param {Function} [randomSource=Math.random] an optional entropy source that | ||
@@ -21,5 +21,5 @@ * returns numbers between 0 inclusive and 1 exclusive: the range [0, 1) | ||
| */ | ||
| function shuffle/*::<T>*/(sample/*:Array<T>*/, randomSource/*:Function*/) { | ||
| function shuffle/*::<T>*/(x/*:Array<T>*/, randomSource/*:Function*/) { | ||
| // slice the original array so that it is not modified | ||
| sample = sample.slice(); | ||
| var sample = x.slice(); | ||
@@ -26,0 +26,0 @@ // and then shuffle that shallow-copied array, in place |
@@ -8,3 +8,4 @@ 'use strict'; | ||
| * The [standard deviation](http://en.wikipedia.org/wiki/Standard_deviation) | ||
| * is the square root of the variance. It's useful for measuring the amount | ||
| * is the square root of the variance. This is also known as the population | ||
| * standard deviation. It's useful for measuring the amount | ||
| * of variation or dispersion in a set of values. | ||
@@ -23,5 +24,6 @@ * | ||
| function standardDeviation(x /*: Array<number> */)/*:number*/ { | ||
| // The standard deviation of no numbers is null | ||
| if (x.length === 1) { | ||
| return 0; | ||
| } | ||
| var v = variance(x); | ||
| if (isNaN(v)) { return 0; } | ||
| return Math.sqrt(v); | ||
@@ -28,0 +30,0 @@ } |
@@ -18,4 +18,4 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} sample an array of numbers as input | ||
| * @param {number} x expected value of the population mean | ||
| * @param {Array<number>} x sample of one or more numbers | ||
| * @param {number} expectedValue expected value of the population mean | ||
| * @returns {number} value | ||
@@ -25,16 +25,16 @@ * @example | ||
| */ | ||
| function tTest(sample/*: Array<number> */, x/*: number */)/*:number*/ { | ||
| function tTest(x/*: Array<number> */, expectedValue/*: number */)/*:number*/ { | ||
| // The mean of the sample | ||
| var sampleMean = mean(sample); | ||
| var sampleMean = mean(x); | ||
| // The standard deviation of the sample | ||
| var sd = standardDeviation(sample); | ||
| var sd = standardDeviation(x); | ||
| // Square root the length of the sample | ||
| var rootN = Math.sqrt(sample.length); | ||
| var rootN = Math.sqrt(x.length); | ||
| // returning the t value | ||
| return (sampleMean - x) / (sd / rootN); | ||
| return (sampleMean - expectedValue) / (sd / rootN); | ||
| } | ||
| module.exports = tTest; |
@@ -12,3 +12,3 @@ 'use strict'; | ||
| * | ||
| * @param {Array} input an array of primitive values. | ||
| * @param {Array<*>} x an array of any kind of value | ||
| * @returns {number} count of unique values | ||
@@ -19,8 +19,8 @@ * @example | ||
| */ | ||
| function uniqueCountSorted(input/*: Array<any>*/)/*: number */ { | ||
| function uniqueCountSorted(x/*: Array<any>*/)/*: number */ { | ||
| var uniqueValueCount = 0, | ||
| lastSeenValue; | ||
| for (var i = 0; i < input.length; i++) { | ||
| if (i === 0 || input[i] !== lastSeenValue) { | ||
| lastSeenValue = input[i]; | ||
| for (var i = 0; i < x.length; i++) { | ||
| if (i === 0 || x[i] !== lastSeenValue) { | ||
| lastSeenValue = x[i]; | ||
| uniqueValueCount++; | ||
@@ -27,0 +27,0 @@ } |
@@ -13,5 +13,6 @@ 'use strict'; | ||
| * | ||
| * @param {Array<number>} x a population | ||
| * @param {Array<number>} x a population of one or more data points | ||
| * @returns {number} variance: a value greater than or equal to zero. | ||
| * zero indicates that all values are identical. | ||
| * @throws {Error} if x's length is 0 | ||
| * @example | ||
@@ -22,3 +23,5 @@ * variance([1, 2, 3, 4, 5, 6]); // => 2.9166666666666665 | ||
| // The variance of no numbers is null | ||
| if (x.length === 0) { return NaN; } | ||
| if (x.length === 0) { | ||
| throw new Error('variance requires at least one data point'); | ||
| } | ||
@@ -25,0 +28,0 @@ // Find the mean of squared deviations between the |
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- increased by4.92%
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- increased by4%
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- increased by4.44%
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- decreased by-20.51%
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