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echarts-stat
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echarts-stat - npm Package Compare versions
Comparing version 1.1.0 to 1.1.1
@@ -57,9 +57,2 @@ (function webpackUniversalModuleDefinition(root, factory) { | ||
| module.exports = __webpack_require__(1); | ||
| /***/ }), | ||
| /* 1 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
| var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) { | ||
@@ -69,6 +62,6 @@ | ||
| clustering: __webpack_require__(2), | ||
| regression: __webpack_require__(6), | ||
| statistics: __webpack_require__(7), | ||
| histogram: __webpack_require__(16) | ||
| clustering: __webpack_require__(1), | ||
| regression: __webpack_require__(5), | ||
| statistics: __webpack_require__(6), | ||
| histogram: __webpack_require__(15) | ||
@@ -80,3 +73,3 @@ }; | ||
| /***/ }), | ||
| /* 2 */ | ||
| /* 1 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -86,4 +79,5 @@ | ||
| var dataPreprocess = __webpack_require__(3); | ||
| var array = __webpack_require__(4); | ||
| var dataProcess = __webpack_require__(2); | ||
| var dataPreprocess = dataProcess.dataPreprocess; | ||
| var array = __webpack_require__(3); | ||
| var arraySize = array.size; | ||
@@ -178,3 +172,3 @@ var sumOfColumn = array.sumOfColumn; | ||
| * @param {Array} data two-dimension array. | ||
| * @param {[type]} k the number of clusters in a dataset | ||
| * @param {[type]} k the number of clusters in a dataset. It has to be greater than 1. | ||
| * @param {boolean} stepByStep | ||
@@ -184,3 +178,5 @@ * @return {} | ||
| function hierarchicalKMeans(data, k, stepByStep) { | ||
| if (k < 2 ) { | ||
| return; | ||
| } | ||
| var dataSet = dataPreprocess(data); | ||
@@ -358,3 +354,3 @@ var size = arraySize(dataSet); | ||
| /***/ }), | ||
| /* 3 */ | ||
| /* 2 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -364,6 +360,6 @@ | ||
| var array = __webpack_require__(4); | ||
| var array = __webpack_require__(3); | ||
| var isArray = array.isArray; | ||
| var size = array.size; | ||
| var number = __webpack_require__(5); | ||
| var number = __webpack_require__(4); | ||
| var isNumber = number.isNumber; | ||
@@ -415,7 +411,22 @@ | ||
| return dataPreprocess; | ||
| /** | ||
| * @param {string|number} val | ||
| * @return {number} | ||
| */ | ||
| function getPrecision(val) { | ||
| var str = val.toString(); | ||
| // scientific notation is not considered | ||
| var dotIndex = str.indexOf('.'); | ||
| return dotIndex < 0 ? 0 : str.length - 1 - dotIndex; | ||
| } | ||
| return { | ||
| dataPreprocess: dataPreprocess, | ||
| getPrecision: getPrecision | ||
| }; | ||
| }.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__)); | ||
| /***/ }), | ||
| /* 4 */ | ||
| /* 3 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -576,3 +587,3 @@ | ||
| /***/ }), | ||
| /* 5 */ | ||
| /* 4 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -589,3 +600,3 @@ | ||
| value = (value === null ? NaN : +value); | ||
| value = value === null ? NaN : +value; | ||
| return typeof value === 'number' && !isNaN(value); | ||
@@ -611,3 +622,3 @@ } | ||
| /***/ }), | ||
| /* 6 */ | ||
| /* 5 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -617,3 +628,4 @@ | ||
| var dataPreprocess = __webpack_require__(3); | ||
| var dataProcess = __webpack_require__(2); | ||
| var dataPreprocess = dataProcess.dataPreprocess; | ||
@@ -919,3 +931,3 @@ var regreMethods = { | ||
| /***/ }), | ||
| /* 7 */ | ||
| /* 6 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -927,10 +939,10 @@ | ||
| statistics.max = __webpack_require__(8); | ||
| statistics.deviation = __webpack_require__(9); | ||
| statistics.mean = __webpack_require__(11); | ||
| statistics.median = __webpack_require__(13); | ||
| statistics.min = __webpack_require__(15); | ||
| statistics.max = __webpack_require__(14); | ||
| statistics.max = __webpack_require__(10); | ||
| statistics.sum = __webpack_require__(12); | ||
| statistics.max = __webpack_require__(7); | ||
| statistics.deviation = __webpack_require__(8); | ||
| statistics.mean = __webpack_require__(10); | ||
| statistics.median = __webpack_require__(12); | ||
| statistics.min = __webpack_require__(14); | ||
| statistics.quantile = __webpack_require__(13); | ||
| statistics.sampleVariance = __webpack_require__(9); | ||
| statistics.sum = __webpack_require__(11); | ||
@@ -942,3 +954,3 @@ return statistics; | ||
| /***/ }), | ||
| /* 8 */ | ||
| /* 7 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -948,3 +960,3 @@ | ||
| var number = __webpack_require__(5); | ||
| var number = __webpack_require__(4); | ||
| var isNumber = number.isNumber; | ||
@@ -974,3 +986,3 @@ | ||
| /***/ }), | ||
| /* 9 */ | ||
| /* 8 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -980,3 +992,3 @@ | ||
| var variance = __webpack_require__(10); | ||
| var variance = __webpack_require__(9); | ||
@@ -997,3 +1009,3 @@ /** | ||
| /***/ }), | ||
| /* 10 */ | ||
| /* 9 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1003,5 +1015,5 @@ | ||
| var number = __webpack_require__(5); | ||
| var number = __webpack_require__(4); | ||
| var isNumber = number.isNumber; | ||
| var mean = __webpack_require__(11); | ||
| var mean = __webpack_require__(10); | ||
@@ -1031,3 +1043,3 @@ /** | ||
| } | ||
| return sum / data.length - 1; | ||
| return sum / (data.length - 1); | ||
| } | ||
@@ -1040,4 +1052,5 @@ } | ||
| /***/ }), | ||
| /* 11 */ | ||
| /* 10 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1047,3 +1060,3 @@ | ||
| var sum = __webpack_require__(12); | ||
| var sum = __webpack_require__(11); | ||
@@ -1074,3 +1087,3 @@ /** | ||
| /***/ }), | ||
| /* 12 */ | ||
| /* 11 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1080,3 +1093,3 @@ | ||
| var number = __webpack_require__(5); | ||
| var number = __webpack_require__(4); | ||
| var isNumber = number.isNumber; | ||
@@ -1111,3 +1124,3 @@ | ||
| /***/ }), | ||
| /* 13 */ | ||
| /* 12 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1117,3 +1130,3 @@ | ||
| var quantile = __webpack_require__(14); | ||
| var quantile = __webpack_require__(13); | ||
@@ -1135,3 +1148,3 @@ /** | ||
| /***/ }), | ||
| /* 14 */ | ||
| /* 13 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1172,3 +1185,3 @@ | ||
| /***/ }), | ||
| /* 15 */ | ||
| /* 14 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1178,3 +1191,3 @@ | ||
| var number = __webpack_require__(5); | ||
| var number = __webpack_require__(4); | ||
| var isNumber = number.isNumber; | ||
@@ -1204,3 +1217,3 @@ | ||
| /***/ }), | ||
| /* 16 */ | ||
| /* 15 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1210,13 +1223,15 @@ | ||
| var max = __webpack_require__(8); | ||
| var min = __webpack_require__(15); | ||
| var quantile = __webpack_require__(14); | ||
| var deviation = __webpack_require__(9); | ||
| var dataPreprocess = __webpack_require__(3); | ||
| var array = __webpack_require__(4); | ||
| var max = __webpack_require__(7); | ||
| var min = __webpack_require__(14); | ||
| var quantile = __webpack_require__(13); | ||
| var deviation = __webpack_require__(8); | ||
| var dataProcess = __webpack_require__(2); | ||
| var dataPreprocess = dataProcess.dataPreprocess; | ||
| var getPrecision = dataProcess.getPrecision; | ||
| var array = __webpack_require__(3); | ||
| var ascending = array.ascending; | ||
| var map = array.map; | ||
| var range = __webpack_require__(17); | ||
| var range = __webpack_require__(16); | ||
| var bisect = array.bisect; | ||
| var tickStep = __webpack_require__(18); | ||
| var tickStep = __webpack_require__(17); | ||
@@ -1232,10 +1247,6 @@ /** | ||
| if (threshold == null) { | ||
| threshold = thresholdMethod.squareRoot; | ||
| } | ||
| else { | ||
| threshold = thresholdMethod[threshold]; | ||
| } | ||
@@ -1245,9 +1256,14 @@ var values = dataPreprocess(data); | ||
| var minValue = min(values); | ||
| var binsNumber = threshold(values, minValue, maxValue); | ||
| var step = tickStep(minValue, maxValue, binsNumber); | ||
| var precision = -Math.floor(Math.log(Math.abs(maxValue - minValue) / binsNumber) / Math.LN10); | ||
| // return the xAxis coordinate for each bins, except the end point of the value | ||
| var rangeArray = range(Math.ceil(minValue / step) * step, Math.floor(maxValue / step) * step, step); | ||
| var rangeArray = range( | ||
| // use function toFixed() to avoid data like '0.700000001' | ||
| +((Math.ceil(minValue / step) * step).toFixed(precision)), | ||
| +((Math.floor(maxValue / step) * step).toFixed(precision)), | ||
| step, | ||
| precision | ||
| ); | ||
@@ -1261,3 +1277,3 @@ var len = rangeArray.length; | ||
| bins[i].sample = []; | ||
| bins[i].x0 = i > 0 // 不要数组直接挂属性，改成Object | ||
| bins[i].x0 = i > 0 | ||
| ? rangeArray[i - 1] | ||
@@ -1281,12 +1297,17 @@ : (rangeArray[i] - minValue) === step | ||
| var data = map(bins, function (bin) { | ||
| return [(bin.x0 + bin.x1) / 2, bin.sample.length]; | ||
| // use function toFixed() to avoid data like '6.5666638489' | ||
| return [+((bin.x0 + bin.x1) / 2).toFixed(precision), bin.sample.length]; | ||
| }); | ||
| var customData = map(bins, function (bin) { | ||
| return [bin.x0, bin.x1, bin.sample.length]; | ||
| }); | ||
| return { | ||
| bins: bins, | ||
| data: data | ||
| data: data, | ||
| customData: customData | ||
| }; | ||
| } | ||
| /** | ||
@@ -1333,3 +1354,3 @@ * Four kinds of threshold methods used to | ||
| /***/ }), | ||
| /* 17 */ | ||
| /* 16 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1339,10 +1360,15 @@ | ||
| var dataProcess = __webpack_require__(2); | ||
| var getPrecision = dataProcess.getPrecision; | ||
| /** | ||
| * Computing range array | ||
| * Computing range array. | ||
| * Adding param precision to fix range value, avoiding range[i] = 0.7000000001. | ||
| * @param {number} start | ||
| * @param {number} stop | ||
| * @param {number} end | ||
| * @param {number} step | ||
| * @param {number} precision | ||
| * @return {Array.<number>} | ||
| */ | ||
| return function (start, stop, step) { | ||
| return function (start, end, step, precision) { | ||
@@ -1352,3 +1378,3 @@ var len = arguments.length; | ||
| if (len < 2) { | ||
| stop = start; | ||
| end = start; | ||
| start = 0; | ||
@@ -1360,11 +1386,14 @@ step = 1; | ||
| } | ||
| else { | ||
| else if (len < 4) { | ||
| step = +step; | ||
| precision = getPrecision(step); | ||
| } | ||
| else { | ||
| precision = +precision; | ||
| } | ||
| var n = Math.ceil((stop - start) / step); | ||
| var n = Math.ceil(((end - start) / step).toFixed(precision)); | ||
| var range = new Array(n + 1); | ||
| for (var i = 0; i < n + 1; i++) { | ||
| range[i] = start + i * step; | ||
| range[i] = +(start + i * step).toFixed(precision); | ||
| } | ||
@@ -1377,3 +1406,3 @@ return range; | ||
| /***/ }), | ||
| /* 18 */ | ||
| /* 17 */ | ||
| /***/ (function(module, exports, __webpack_require__) { | ||
@@ -1393,3 +1422,4 @@ | ||
| var step0 = Math.abs(stop - start) / count; | ||
| var step1 = Math.pow(10, Math.floor(Math.log(step0) / Math.LN10)); | ||
| var precision = Math.floor(Math.log(step0) / Math.LN10); | ||
| var step1 = Math.pow(10, precision); | ||
| var error = step0 / step1; | ||
@@ -1406,3 +1436,3 @@ | ||
| } | ||
| return stop >= start ? step1 : -step1; | ||
| return +((stop >= start ? step1 : -step1).toFixed(-precision)); | ||
@@ -1409,0 +1439,0 @@ }; |
@@ -1,1 +0,1 @@ | ||
| !function(r,n){"object"==typeof exports&&"object"==typeof module?module.exports=n():"function"==typeof define&&define.amd?define([],n):"object"==typeof exports?exports.ecStat=n():r.ecStat=n()}(this,function(){return function(r){function n(e){if(t[e])return t[e].exports;var o=t[e]={exports:{},id:e,loaded:!1};return r[e].call(o.exports,o,o.exports,n),o.loaded=!0,o.exports}var t={};return n.m=r,n.c=t,n.p="",n(0)}([function(r,n,t){r.exports=t(12)},function(r,n,t){var e;e=function(r){function n(r){return r=null===r?NaN:+r,"number"==typeof r&&!isNaN(r)}function t(r){return isFinite(r)&&r===Math.round(r)}return{isNumber:n,isInteger:t}}.call(n,t,n,r),!(void 0!==e&&(r.exports=e))},function(r,n,t){var e;e=function(r){return function(r,n){var t=r.length;if(!t)return 0;if(n<=0||t<2)return r[0];if(n>=1)return r[t-1];var e=(t-1)*n,o=Math.floor(e),a=r[o],u=r[o+1];return a+(u-a)*(e-o)}}.call(n,t,n,r),!(void 0!==e&&(r.exports=e))},function(r,n,t){var e;e=function(r){function n(r){for(var 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"+Math.round(100*u)/100+"x";return{points:i,parameter:{gradient:u},expression:s}},exponential:function(r){for(var t=o(r),n=0,e=0,a=0,u=0,i=0,f=0,l=0;l<t.length;l++)n+=t[l][0],e+=t[l][1],f+=t[l][0]*t[l][1],a+=t[l][0]*t[l][0]*t[l][1],u+=t[l][1]*Math.log(t[l][1]),i+=t[l][0]*t[l][1]*Math.log(t[l][1]);for(var s=e*a-f*f,c=Math.pow(Math.E,(a*u-f*i)/s),h=(e*i-f*u)/s,v=[],p=0;p<t.length;p++){var g=[t[p][0],c*Math.pow(Math.E,h*t[p][0])];v.push(g)}var d="y = "+Math.round(100*c)/100+"e^("+Math.round(100*h)/100+"x)";return{points:v,parameter:{coefficient:c,index:h},expression:d}},logarithmic:function(r){for(var t=o(r),n=0,e=0,a=0,u=0,i=0;i<t.length;i++)n+=Math.log(t[i][0]),e+=t[i][1]*Math.log(t[i][0]),a+=t[i][1],u+=Math.pow(Math.log(t[i][0]),2);for(var f=(i*e-a*n)/(i*u-n*n),l=(a-f*n)/i,s=[],c=0;c<t.length;c++){var h=[t[c][0],f*Math.log(t[c][0])+l];s.push(h)}var v="y = "+Math.round(100*l)/100+" + "+Math.round(100*f)/100+"ln(x)";return{points:s,parameter:{gradient:f,intercept:l},expression:v}},polynomial:function(r,n){var e=o(r);"undefined"==typeof n&&(n=2);for(var a=[],u=[],i=n+1,f=0;f<i;f++){for(var l=0,s=0;s<e.length;s++)l+=e[s][1]*Math.pow(e[s][0],f);u.push(l);for(var c=[],h=0;h<i;h++){for(var v=0,p=0;p<e.length;p++)v+=Math.pow(e[p][0],f+h);c.push(v)}a.push(c)}a.push(u);for(var g=t(a,i),d=[],f=0;f<e.length;f++){for(var M=0,s=0;s<g.length;s++)M+=g[s]*Math.pow(e[f][0],s);d.push([e[f][0],M])}for(var x="y = ",f=g.length-1;f>=0;f--)x+=f>1?Math.round(g[f]*Math.pow(10,f+1))/Math.pow(10,f+1)+"x^"+f+" + ":1===f?Math.round(100*g[f])/100+"x + ":Math.round(100*g[f])/100;return{points:d,parameter:g,expression:x}}},u=function(r,t,n){return a[r](t,n)};return u}.call(t,n,t,r),!(void 0!==e&&(r.exports=e))},function(r,t,n){var e;e=function(r){var t={};return t.max=n(6),t.deviation=n(5),t.mean=n(7),t.median=n(15),t.min=n(8),t.quantile=n(3),t.sampleVariance=n(9),t.sum=n(10),t}.call(t,n,t,r),!(void 0!==e&&(r.exports=e))},function(r,t,n){var e;e=function(r){function t(r){return e(r,.5)}var e=n(3);return t}.call(t,n,t,r),!(void 0!==e&&(r.exports=e))},function(r,t,n){var e;e=function(r){var t=n(2),e=t.getPrecision;return function(r,t,n,o){var a=arguments.length;a<2?(t=r,r=0,n=1):a<3?n=1:a<4?(n=+n,o=e(n)):o=+o;for(var u=Math.ceil(((t-r)/n).toFixed(o)),i=new Array(u+1),f=0;f<u+1;f++)i[f]=+(r+f*n).toFixed(o);return i}}.call(t,n,t,r),!(void 0!==e&&(r.exports=e))},function(r,t,n){var e;e=function(r){return function(r,t,n){var e=Math.abs(t-r)/n,o=Math.floor(Math.log(e)/Math.LN10),a=Math.pow(10,o),u=e/a;return u>=Math.sqrt(50)?a*=10:u>=Math.sqrt(10)?a*=5:u>=Math.sqrt(2)&&(a*=2),+(t>=r?a:-a).toFixed(-o)}}.call(t,n,t,r),!(void 0!==e&&(r.exports=e))}])}); |
@@ -1,1 +0,1 @@ | ||
| module.exports = require('./src/ecStat'); | ||
| module.exports = require('./dist/ecStat.js'); |
| { | ||
| "name": "echarts-stat", | ||
| "version": "1.1.0", | ||
| "version": "1.1.1", | ||
| "description": "a statistical tool for ECharts", | ||
| "main": "index.js", | ||
| "types": "src/ecStat.d.ts", | ||
| "author": { | ||
| "name": "Deqing", | ||
| "name": "Deqing Li", | ||
| "email": "annong035@gmail.com" | ||
@@ -19,2 +20,2 @@ }, | ||
| } | ||
| } | ||
| } |
@@ -35,3 +35,3 @@ # ecStat | ||
| A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a quantitative variable. It is a kind of bar graph. To construct a histogram, the first step is to "bin" the range of values - that is, divide the entire range of values into a series of intervals - and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. Here the bins(intervals) must be adjacent, and are of equal size. | ||
| A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a quantitative variable. It is a kind of bar graph. To construct a histogram, the first step is to "bin" the range of values - that is, divide the entire range of values into a series of intervals - and then count how many original sample values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. Here the bins(intervals) must be adjacent, and are of equal size. | ||
@@ -52,5 +52,5 @@ #### Syntax | ||
| * `binMethod` - `string`. There are four methods to calculate the number of bins, which are `squareRoot`, `scott`, `freedmanDiaconis`, and `sturges`. Of course, there is no "best" number of bins, and different bin sizes can reveal different features of the data. | ||
| * `binMethod` - `string`. There are four methods to calculate the number of bin, which are `squareRoot`, `scott`, `freedmanDiaconis`, and `sturges`. Of course, there is no "best" number of bin, and different bin size can reveal different feature of data. | ||
| * `squareRoot` - This is the default method, which is also used by Excel histograms. Returns the number of bins according to [Square-root choice](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition): | ||
| * `squareRoot` - This is the default method, which is also used by Excel histogram. Returns the number of bin according to [Square-root choice](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition): | ||
| ```js | ||
@@ -60,3 +60,3 @@ var bins = ecStat.histogram(data); | ||
| * `scott` - Returns the number of bins according to [Scott's normal reference Rule](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition): | ||
| * `scott` - Returns the number of bin according to [Scott's normal reference Rule](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition): | ||
| ```js | ||
@@ -66,3 +66,3 @@ var bins = ecStat.histogram(data, 'scott'); | ||
| * `freedmanDiaconis` - Returns the number of bins according to [The Freedman-Diaconis rule](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition): | ||
| * `freedmanDiaconis` - Returns the number of bin according to [The Freedman-Diaconis rule](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition): | ||
| ```js | ||
@@ -72,3 +72,3 @@ var bins = ecStat.histogram(data, 'freedmanDiaconis'); | ||
| * `sturges` - Returns the number of bins according to [Sturges' formula](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition): | ||
| * `sturges` - Returns the number of bin according to [Sturges' formula](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition): | ||
| ```js | ||
@@ -80,3 +80,3 @@ var bins = ecStat.histogram(data, 'sturges'); | ||
| * `bins` - `Object`. Contain detailed messages of each bin and data used for [ECharts](https://github.com/ecomfe/echarts) to draw the bar chart. | ||
| * `bins` - `Object`. Contain detailed messages of each bin and data used for [ECharts](https://github.com/ecomfe/echarts) to draw the histogram. | ||
| * `bins.bins` - `Array.<Object>`. An array of bins, where each bin is an object, containing three attributes: | ||
@@ -86,7 +86,8 @@ * `x0` - `number`. The lower bound of the bin (inclusive). | ||
| * `sample` - `Array.<number>`. Containing the associated elements from the input data. | ||
| * `bins.data` - `Array.<Array.<number>>`. An array of bins data, each bins data is an array not only containing the mean value of `x0` and `x1`, but also the length of `sample`, which is the number of sample values in that bin. | ||
| * `bins.data` - `Array.<Array.<number>>`. Used for bar chart to draw the histogram, each bin data is an array not only containing the mean value of `x0` and `x1`, but also the length of `sample`, which is the number of sample values in this bin. | ||
| * `bins.customData` - `Array.<Array<number>>`. Used for custom chart to draw the histogram, each custom data is an array not only containing the `x0` and `x1`, but also the length of `sample`, which is the number of sample values in this bin. | ||
| #### Examples | ||
| When using ECharts bar chart to draw the histogram, we must notice that, setting the `xAxis.scale` as `true`. | ||
| This example using ECharts custom chart to draw the histogram, which is the best type of chart we recommend. | ||
@@ -101,6 +102,5 @@ ```html | ||
| ... | ||
| xAxis: [{ | ||
| type: 'value', | ||
| // this must be set as true, otherwise barWidth and bins width can not corresponding on | ||
| scale: true | ||
| series: [{ | ||
| type: 'custom', | ||
| ... | ||
| }], | ||
@@ -127,3 +127,3 @@ ... | ||
| * `data` － `two-dimensional Numeric Array`. Each data point can have more than two numeric attributes in the original data set. In the following example, `data[0]` is called `data point` and `data[0][1]` is one of the numeric attributes of `data[0]`. | ||
| * `data` － `Array.<Array.<number>>`. Two-dimensional numeric array, each data point can have more than two numeric attributes in the original data set. In the following example, `data[0]` is called `data point` and `data[0][1]` is one of the numeric attributes of `data[0]`. | ||
@@ -138,3 +138,3 @@ ```js | ||
| ``` | ||
| * `clusterNumer` － `number`. The number of clusters generated | ||
| * `clusterNumer` － `number`. The number of clusters generated. **Note that it has to be greater than 1.** | ||
| * `stepByStep` － `boolean`. Control whether doing the clustering step by step | ||
@@ -154,3 +154,3 @@ | ||
| // indicate which cluster each data point belonging to, and the distance to cluster centroids | ||
| // Indicate which cluster each data point belonging to, and the distance to cluster centroids | ||
| result.clusterAssment = [ | ||
@@ -164,3 +164,3 @@ | ||
| // concrete data point in each cluster | ||
| // Concrete data point in each cluster | ||
| result.pointsInCluster = [ | ||
@@ -180,5 +180,5 @@ [ | ||
| Note: the clustering algorithm can handle multiple numeric attributes, but for the convenience of visualization, two numeric attributes are chosen here as an example. | ||
| **Note: the clustering algorithm can handle multiple numeric attributes, but for the convenience of visualization, two numeric attributes are chosen here as an example.** | ||
| ##### Directly visualize the results of clustering | ||
| ##### Directly visualize the final results of clustering | ||
@@ -228,4 +228,4 @@ ```html | ||
| * `regressionType` - `string`. There are four types of regression, whice are `linear`, `exponential`, `logarithmic`, `polynomial` | ||
| * `data` - `two-dimensional Numeric Array`. Each data object should have two numeric attributes in the original data set. For Example: | ||
| * `regressionType` - `string`. There are four types of regression, whice are `'linear'`, `'exponential'`, `'logarithmic'`, `'polynomial'`. | ||
| * `data` - `Array.<Array.<number>>`. Two-dimensional numeric array, Each data object should have two numeric attributes in the original data set. For Example: | ||
@@ -240,3 +240,3 @@ ```js | ||
| ``` | ||
| * `order` - `number`. The order of polynomial. If you choose other types of regression, you can ignore it | ||
| * `order` - `number`. The order of polynomial. If you choose other types of regression, you can ignore it. | ||
@@ -254,3 +254,3 @@ ##### Return Value | ||
| // this is the parameter of linear regression, for other types, it shoule be a little different | ||
| // This is the parameter of linear regression, for other types, it shoule be a little different | ||
| myRegression.parameter = { | ||
@@ -257,0 +257,0 @@ gradient: 1.695, |
| define(function (require) { | ||
| var dataPreprocess = require('./util/dataPreprocess'); | ||
| var dataProcess = require('./util/dataProcess'); | ||
| var dataPreprocess = dataProcess.dataPreprocess; | ||
| var array = require('./util/array'); | ||
@@ -94,3 +95,3 @@ var arraySize = array.size; | ||
| * @param {Array} data two-dimension array. | ||
| * @param {[type]} k the number of clusters in a dataset | ||
| * @param {[type]} k the number of clusters in a dataset. It has to be greater than 1. | ||
| * @param {boolean} stepByStep | ||
@@ -100,3 +101,5 @@ * @return {} | ||
| function hierarchicalKMeans(data, k, stepByStep) { | ||
| if (k < 2 ) { | ||
| return; | ||
| } | ||
| var dataSet = dataPreprocess(data); | ||
@@ -103,0 +106,0 @@ var size = arraySize(dataSet); |
@@ -7,3 +7,5 @@ define(function (require) { | ||
| var deviation = require('./statistics/deviation'); | ||
| var dataPreprocess = require('./util/dataPreprocess'); | ||
| var dataProcess = require('./util/dataProcess'); | ||
| var dataPreprocess = dataProcess.dataPreprocess; | ||
| var getPrecision = dataProcess.getPrecision; | ||
| var array = require('./util/array'); | ||
@@ -25,10 +27,6 @@ var ascending = array.ascending; | ||
| if (threshold == null) { | ||
| threshold = thresholdMethod.squareRoot; | ||
| } | ||
| else { | ||
| threshold = thresholdMethod[threshold]; | ||
| } | ||
@@ -38,9 +36,14 @@ var values = dataPreprocess(data); | ||
| var minValue = min(values); | ||
| var binsNumber = threshold(values, minValue, maxValue); | ||
| var step = tickStep(minValue, maxValue, binsNumber); | ||
| var precision = -Math.floor(Math.log(Math.abs(maxValue - minValue) / binsNumber) / Math.LN10); | ||
| // return the xAxis coordinate for each bins, except the end point of the value | ||
| var rangeArray = range(Math.ceil(minValue / step) * step, Math.floor(maxValue / step) * step, step); | ||
| var rangeArray = range( | ||
| // use function toFixed() to avoid data like '0.700000001' | ||
| +((Math.ceil(minValue / step) * step).toFixed(precision)), | ||
| +((Math.floor(maxValue / step) * step).toFixed(precision)), | ||
| step, | ||
| precision | ||
| ); | ||
@@ -54,3 +57,3 @@ var len = rangeArray.length; | ||
| bins[i].sample = []; | ||
| bins[i].x0 = i > 0 // 不要数组直接挂属性，改成Object | ||
| bins[i].x0 = i > 0 | ||
| ? rangeArray[i - 1] | ||
@@ -74,12 +77,17 @@ : (rangeArray[i] - minValue) === step | ||
| var data = map(bins, function (bin) { | ||
| return [(bin.x0 + bin.x1) / 2, bin.sample.length]; | ||
| // use function toFixed() to avoid data like '6.5666638489' | ||
| return [+((bin.x0 + bin.x1) / 2).toFixed(precision), bin.sample.length]; | ||
| }); | ||
| var customData = map(bins, function (bin) { | ||
| return [bin.x0, bin.x1, bin.sample.length]; | ||
| }); | ||
| return { | ||
| bins: bins, | ||
| data: data | ||
| data: data, | ||
| customData: customData | ||
| }; | ||
| } | ||
| /** | ||
@@ -86,0 +94,0 @@ * Four kinds of threshold methods used to |
| define(function (require) { | ||
| var dataPreprocess = require('./util/dataPreprocess'); | ||
| var dataProcess = require('./util/dataProcess'); | ||
| var dataPreprocess = dataProcess.dataPreprocess; | ||
@@ -5,0 +6,0 @@ var regreMethods = { |
@@ -10,4 +10,4 @@ define(function (require) { | ||
| statistics.min = require('./statistics/min'); | ||
| statistics.max = require('./statistics/quantile'); | ||
| statistics.max = require('./statistics/sampleVariance'); | ||
| statistics.quantile = require('./statistics/quantile'); | ||
| statistics.sampleVariance = require('./statistics/sampleVariance'); | ||
| statistics.sum = require('./statistics/sum'); | ||
@@ -14,0 +14,0 @@ |
@@ -30,3 +30,3 @@ define(function (require) { | ||
| } | ||
| return sum / data.length - 1; | ||
| return sum / (data.length - 1); | ||
| } | ||
@@ -37,2 +37,2 @@ } | ||
| }); | ||
| }); |
@@ -10,3 +10,3 @@ define(function (require) { | ||
| value = (value === null ? NaN : +value); | ||
| value = value === null ? NaN : +value; | ||
| return typeof value === 'number' && !isNaN(value); | ||
@@ -13,0 +13,0 @@ } |
| define(function (require) { | ||
| var dataProcess = require('./dataProcess'); | ||
| var getPrecision = dataProcess.getPrecision; | ||
| /** | ||
| * Computing range array | ||
| * Computing range array. | ||
| * Adding param precision to fix range value, avoiding range[i] = 0.7000000001. | ||
| * @param {number} start | ||
| * @param {number} stop | ||
| * @param {number} end | ||
| * @param {number} step | ||
| * @param {number} precision | ||
| * @return {Array.<number>} | ||
| */ | ||
| return function (start, stop, step) { | ||
| return function (start, end, step, precision) { | ||
@@ -15,3 +20,3 @@ var len = arguments.length; | ||
| if (len < 2) { | ||
| stop = start; | ||
| end = start; | ||
| start = 0; | ||
@@ -23,11 +28,14 @@ step = 1; | ||
| } | ||
| else { | ||
| else if (len < 4) { | ||
| step = +step; | ||
| precision = getPrecision(step); | ||
| } | ||
| else { | ||
| precision = +precision; | ||
| } | ||
| var n = Math.ceil((stop - start) / step); | ||
| var n = Math.ceil(((end - start) / step).toFixed(precision)); | ||
| var range = new Array(n + 1); | ||
| for (var i = 0; i < n + 1; i++) { | ||
| range[i] = start + i * step; | ||
| range[i] = +(start + i * step).toFixed(precision); | ||
| } | ||
@@ -34,0 +42,0 @@ return range; |
@@ -13,3 +13,4 @@ define(function (require) { | ||
| var step0 = Math.abs(stop - start) / count; | ||
| var step1 = Math.pow(10, Math.floor(Math.log(step0) / Math.LN10)); | ||
| var precision = Math.floor(Math.log(step0) / Math.LN10); | ||
| var step1 = Math.pow(10, precision); | ||
| var error = step0 / step1; | ||
@@ -26,3 +27,3 @@ | ||
| } | ||
| return stop >= start ? step1 : -step1; | ||
| return +((stop >= start ? step1 : -step1).toFixed(-precision)); | ||
@@ -29,0 +30,0 @@ }; |
@@ -13,3 +13,3 @@ require.config({ | ||
| main: 'echarts', | ||
| location: '../../echarts/src', | ||
| location: '../../echarts/dist', | ||
| name: 'echarts' | ||
@@ -19,3 +19,3 @@ }, | ||
| main: 'zrender', | ||
| location: '../../zrender/src', | ||
| location: '../../zrender/dist', | ||
| name: 'zrender' | ||
@@ -22,0 +22,0 @@ } |
@@ -6,3 +6,3 @@ var PROD = process.argv.indexOf('-p') >= 0; | ||
| entry: { | ||
| 'ecStat': __dirname + '/index.js' | ||
| 'ecStat': __dirname + '/src/ecStat.js' | ||
| }, | ||
@@ -9,0 +9,0 @@ output: { |
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