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echarts-stat - npm Package Compare versions

Comparing version 1.0.0 to 1.1.0

img/histogram.png
src/histogram.js
src/statistics.js
src/statistics/deviation.js
src/statistics/max.js
src/statistics/mean.js
src/statistics/median.js
src/statistics/min.js
src/statistics/quantile.js
src/statistics/sampleVariance.js
src/statistics/sum.js
src/util/range.js
src/util/tickStep.js
test/histogram.html

1968

dist/ecStat.js

@@ -24,5 +24,5 @@ (function webpackUniversalModuleDefinition(root, factory) {

/******/ var module = installedModules[moduleId] = {
/******/ i: moduleId,
/******/ l: false,
/******/ exports: {}
/******/ exports: {},
/******/ id: moduleId,
/******/ loaded: false
/******/ };

@@ -34,3 +34,3 @@

/******/ // Flag the module as loaded
/******/ module.l = true;
/******/ module.loaded = true;

@@ -48,28 +48,2 @@ /******/ // Return the exports of the module

/******/ // identity function for calling harmony imports with the correct context
/******/ __webpack_require__.i = function(value) { return value; };
/******/ // define getter function for harmony exports
/******/ __webpack_require__.d = function(exports, name, getter) {
/******/ if(!__webpack_require__.o(exports, name)) {
/******/ Object.defineProperty(exports, name, {
/******/ configurable: false,
/******/ enumerable: true,
/******/ get: getter
/******/ });
/******/ }
/******/ };
/******/ // getDefaultExport function for compatibility with non-harmony modules
/******/ __webpack_require__.n = function(module) {
/******/ var getter = module && module.__esModule ?
/******/ function getDefault() { return module['default']; } :
/******/ function getModuleExports() { return module; };
/******/ __webpack_require__.d(getter, 'a', getter);
/******/ return getter;
/******/ };
/******/ // Object.prototype.hasOwnProperty.call
/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); };
/******/ // __webpack_public_path__

@@ -79,3 +53,3 @@ /******/ __webpack_require__.p = "";

/******/ // Load entry module and return exports
/******/ return __webpack_require__(__webpack_require__.s = 12);
/******/ return __webpack_require__(0);
/******/ })

@@ -87,162 +61,296 @@ /************************************************************************/

var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
module.exports = __webpack_require__(1);
var number = __webpack_require__(1);
var objToString = Object.prototype.toString;
/**
* Get the size of a array
* @param {Array} data
* @return {Array}
*/
function size(data) {
var s = [];
while (isArray(data)) {
s.push(data.length);
data = data[0];
}
return s;
}
/***/ }),
/* 1 */
/***/ (function(module, exports, __webpack_require__) {
/**
* @param {*} value
* @return {boolean}
*/
function isArray(value) {
return objToString.call(value) === '[object Array]';
}
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
/**
* constructs a (m x n) array with all values 0
* @param {number} m the row
* @param {number} n the column
* @return {Array}
*/
function zeros(m, n) {
var zeroArray = [];
for (var i = 0; i < m ; i++) {
zeroArray[i] = [];
for (var j = 0; j < n; j++) {
zeroArray[i][j] = 0;
}
}
return zeroArray;
}
return {
/**
* Sums each element in the array
* @param {Array} vector
* @return {number}
*/
function sum(vector) {
var sum = 0;
for (var i = 0; i < vector.length; i++) {
sum += vector[i];
}
return sum;
}
clustering: __webpack_require__(2),
regression: __webpack_require__(6),
statistics: __webpack_require__(7),
histogram: __webpack_require__(16)
/**
* Computes the sum of the specified column elements in a two-dimensional array
* @param {Array.<Array>} dataList two-dimensional array
* @param {number} n the specified column, zero-based
* @return {number}
*/
function sumOfColumn(dataList, n) {
var sum = 0;
for (var i = 0; i < dataList.length; i++) {
sum += dataList[i][n];
}
return sum;
}
};
return {
size: size,
isArray: isArray,
zeros: zeros,
sum: sum,
sumOfColumn: sumOfColumn
};
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 1 */
/* 2 */
/***/ (function(module, exports, __webpack_require__) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
/**
* Test whether value is a number.
* @param {*} value
* @return {boolean}
*/
function isNumber(value) {
return typeof value === 'number' && !isNaN(value);
}
var dataPreprocess = __webpack_require__(3);
var array = __webpack_require__(4);
var arraySize = array.size;
var sumOfColumn = array.sumOfColumn;
var arraySum = array.sum;
var zeros = array.zeros;
var isArray = array.isArray;
var mathSqrt = Math.sqrt;
var mathPow = Math.pow;
/**
* Test if a number is integer.
* @param {number} value
* @return {boolean}
*/
function isInteger(value) {
return isFinite(value) && value === Math.round(value);
}
/**
* KMeans of clustering algorithm
* @param {Array.<Array.<number>>} data two-dimension array
* @param {number} k the number of clusters in a dataset
* @return {Object}
*/
function kMeans(data, k) {
return {
isNumber: isNumber,
isInteger: isInteger
};
var size = arraySize(data);
// create array to assign data points to centroids, also holds SE of each point
var clusterAssigned = zeros(size[0], 2);
var centroids = createRandCent(data, k);
var clusterChanged = true;
var minDist;
var minIndex;
var distIJ;
var ptsInClust;
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
while (clusterChanged) {
clusterChanged = false;
for (var i = 0; i < size[0]; i++) {
minDist = Infinity;
minIndex = -1;
for (var j = 0; j < k; j++) {
distIJ = distEuclid(data[i], centroids[j]);
if (distIJ < minDist) {
minDist = distIJ;
minIndex = j;
}
}
if (clusterAssigned[i][0] !== minIndex) {
clusterChanged = true;
}
clusterAssigned[i][0] = minIndex;
clusterAssigned[i][1] = mathPow(minDist, 2);
}
//recalculate centroids
for (var i = 0; i < k; i++) {
ptsInClust = [];
for (var j = 0; j < clusterAssigned.length; j++) {
if (clusterAssigned[j][0] === i) {
ptsInClust.push(data[j]);
}
}
centroids[i] = meanInColumns(ptsInClust);
}
}
/***/ }),
/* 2 */
/***/ (function(module, exports, __webpack_require__) {
var clusterWithKmeans = {
centroids: centroids,
clusterAssigned: clusterAssigned
};
return clusterWithKmeans;
}
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
/**
* Calculate the average of each column in a two-dimensional array
* and returns the values as an array.
* @param {Array.<Array>} dataList two-dimensional array
* @return {Array}
*/
function meanInColumns(dataList) {
var array = __webpack_require__(0);
var isArray = array.isArray;
var size = array.size;
var number = __webpack_require__(1);
var isNumber = number.isNumber;
var size = arraySize(dataList);
var meanArray = [];
var sum;
var mean;
for (var j = 0; j < size[1]; j++) {
sum = 0;
for (var i = 0; i < size[0]; i++) {
sum += dataList[i][j];
}
mean = sum / size[0];
meanArray.push(mean);
}
return meanArray;
}
/**
* Data preprocessing, filter the wrong data object.
* for example [12,] --- missing y value
* [,12] --- missing x value
* [12, b] --- incorrect y value
* ['a', 12] --- incorrect x value
* @param {Array.<Array>} data
* @return {Array.<Array.<number>>}
*/
function dataPreprocess(data) {
/**
* The combine of hierarchical clustering and k-means.
* @param {Array} data two-dimension array.
* @param {[type]} k the number of clusters in a dataset
* @param {boolean} stepByStep
* @return {}
*/
function hierarchicalKMeans(data, k, stepByStep) {
if (!isArray(data)) {
throw new Error('Invalid data type, you should input an array');
}
var predata = [];
var arraySize = size(data);
for (var i = 0; i < arraySize[0]; i++) {
var isCorrect = true;
for (var j = 0; j < arraySize[1]; j++) {
if (!isNumber(data[i][j])) {
isCorrect = false;
}
}
if (isCorrect) {
predata.push(data[i]);
}
}
return predata;
}
var dataSet = dataPreprocess(data);
var size = arraySize(dataSet);
var clusterAssment = zeros(size[0], 2);
// initial center point
var centroid0 = meanInColumns(dataSet);
var centList = [centroid0];
var squareError;
for (var i = 0; i < size[0]; i++) {
squareError = distEuclid(dataSet[i], centroid0);
clusterAssment[i][1] = mathPow(squareError, 2);
}
var lowestSSE;
var ptsInClust;
var ptsNotClust;
var clusterInfo;
var sseSplit;
var sseNotSplit;
var index = 1;
var result = {
isEnd: false
};
return dataPreprocess;
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
function oneStep() {
//the existing clusters are continuously divided
//until the number of clusters is k
if (index < k) {
lowestSSE = Infinity;
var centSplit;
var newCentroid;
var newClusterAss;
for (var j = 0; j < centList.length; j++) {
ptsInClust = [];
ptsNotClust = [];
for (var i = 0; i < clusterAssment.length; i++) {
if (clusterAssment[i][0] === j) {
ptsInClust.push(dataSet[i]);
}
else {
ptsNotClust.push(clusterAssment[i][1]);
}
}
clusterInfo = kMeans(ptsInClust, 2);
sseSplit = sumOfColumn(clusterInfo.clusterAssigned, 1);
sseNotSplit = arraySum(ptsNotClust);
if (sseSplit + sseNotSplit < lowestSSE) {
lowestSSE = sseNotSplit + sseSplit;
centSplit = j;
newCentroid = clusterInfo.centroids;
newClusterAss = clusterInfo.clusterAssigned;
}
}
for (var i = 0; i < newClusterAss.length; i++) {
if (newClusterAss[i][0] === 0) {
newClusterAss[i][0] = centSplit;
}
else if (newClusterAss[i][0] === 1) {
newClusterAss[i][0] = centList.length;
}
}
centList[centSplit] = newCentroid[0];
centList.push(newCentroid[1]);
for ( i = 0, j = 0; i < clusterAssment.length && j < newClusterAss.length; i++) {
if (clusterAssment[i][0] === centSplit) {
clusterAssment[i][0] = newClusterAss[j][0];
clusterAssment[i][1] = newClusterAss[j++][1];
}
}
var pointInClust = [];
for (var i = 0; i < centList.length; i++) {
pointInClust[i] = [];
for (var j = 0; j < clusterAssment.length; j++) {
if (clusterAssment[j][0] === i) {
pointInClust[i].push(dataSet[j]);
}
}
}
result.clusterAssment = clusterAssment;
result.centroids = centList;
result.pointsInCluster = pointInClust;
index++;
}
else {
result.isEnd = true;
}
return result;
}
var step = {
next: oneStep
};
if (!stepByStep) {
var result;
while (!(result = step.next()).isEnd);
return result;
}
else {
return step;
}
}
/**
* Create random centroid of kmeans.
* @param {Array.<number>} dataSet two-dimension array
* @param {number} k the number of centroids to be created
* @return {Array.<number>} random centroids of kmeans
*/
function createRandCent(dataSet, k) {
var size = arraySize(dataSet);
//constructs a two-dimensional array with all values 0
var centroids = zeros(k, size[1]);
var minJ;
var maxJ;
var rangeJ;
//create random cluster centers, within bounds of each dimension
for (var j = 0; j < size[1]; j++) {
minJ = dataSet[0][j];
maxJ = dataSet[0][j];
for (var i = 1; i < size[0]; i++) {
if (dataSet[i][j] < minJ) {
minJ = dataSet[i][j];
}
if (dataSet[i][j] > maxJ) {
maxJ = dataSet[i][j];
}
}
rangeJ = maxJ - minJ;
for (var i = 0; i < k; i++) {
centroids[i][j] = minJ + rangeJ * Math.random();
}
}
return centroids;
}
/**
* Distance method for calculating similarity
* @param {Array.<number>} vec1
* @param {Array.<nnumber>} vec2
* @return {number}
*/
function distEuclid(vec1, vec2) {
if (!isArray(vec1) && !isArray(vec2)) {
return mathSqrt(mathPow(vec1 - vec2, 2));
}
var powerSum = 0;
//subtract the corresponding elements in the vectors
for (var i = 0; i < vec1.length; i++) {
powerSum += mathPow(vec1[i] - vec2[i], 2);
}
return mathSqrt(powerSum);
}
return {
kMeans: kMeans,
hierarchicalKMeans: hierarchicalKMeans
};
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),

@@ -252,15 +360,57 @@ /* 3 */

var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
return {
var array = __webpack_require__(4);
var isArray = array.isArray;
var size = array.size;
var number = __webpack_require__(5);
var isNumber = number.isNumber;
clustering: __webpack_require__(4),
regression: __webpack_require__(5),
statistic: __webpack_require__(6)
/**
* Data preprocessing, filter the wrong data object.
* for example [12,] --- missing y value
* [,12] --- missing x value
* [12, b] --- incorrect y value
* ['a', 12] --- incorrect x value
* @param {Array.<Array>} data
* @return {Array.<Array.<number>>}
*/
function dataPreprocess(data) {
};
if (!isArray(data)) {
throw new Error('Invalid data type, you should input an array');
}
var predata = [];
var arraySize = size(data);
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
if (arraySize.length === 1) {
for (var i = 0; i < arraySize[0]; i++) {
if (isNumber(data[i])) {
predata.push(data[i]);
}
}
}
else if (arraySize.length === 2) {
for (var i = 0; i < arraySize[0]; i++) {
var isCorrect = true;
for (var j = 0; j < arraySize[1]; j++) {
if (!isNumber(data[i][j])) {
isCorrect = false;
}
}
if (isCorrect) {
predata.push(data[i]);
}
}
}
return predata;
}
return dataPreprocess;
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),

@@ -270,807 +420,961 @@ /* 4 */

var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var dataPreprocess = __webpack_require__(2);
var array = __webpack_require__(0);
var arraySize = array.size;
var sumOfColumn = array.sumOfColumn;
var arraySum = array.sum;
var zeros = array.zeros;
var isArray = array.isArray;
var mathSqrt = Math.sqrt;
var mathPow = Math.pow;
var objToString = Object.prototype.toString;
var arrayProto = Array.prototype;
var nativeMap = arrayProto.map;
/**
* KMeans of clustering algorithm
* @param {Array.<Array.<number>>} data two-dimension array
* @param {number} k the number of clusters in a dataset
* @return {Object}
*/
function kMeans(data, k) {
/**
* Get the size of a array
* @param {Array} data
* @return {Array}
*/
function size(data) {
var s = [];
while (isArray(data)) {
s.push(data.length);
data = data[0];
}
return s;
}
var size = arraySize(data);
// create array to assign data points to centroids, also holds SE of each point
var clusterAssigned = zeros(size[0], 2);
var centroids = createRandCent(data, k);
var clusterChanged = true;
var minDist;
var minIndex;
var distIJ;
var ptsInClust;
/**
* @param {*} value
* @return {boolean}
*/
function isArray(value) {
return objToString.call(value) === '[object Array]';
}
while (clusterChanged) {
clusterChanged = false;
for (var i = 0; i < size[0]; i++) {
minDist = Infinity;
minIndex = -1;
for (var j = 0; j < k; j++) {
distIJ = distEuclid(data[i], centroids[j]);
if (distIJ < minDist) {
minDist = distIJ;
minIndex = j;
}
}
if (clusterAssigned[i][0] !== minIndex) {
clusterChanged = true;
}
clusterAssigned[i][0] = minIndex;
clusterAssigned[i][1] = mathPow(minDist, 2);
}
//recalculate centroids
for (var i = 0; i < k; i++) {
ptsInClust = [];
for (var j = 0; j < clusterAssigned.length; j++) {
if (clusterAssigned[j][0] === i) {
ptsInClust.push(data[j]);
}
}
centroids[i] = meanInColumns(ptsInClust);
}
}
/**
* constructs a (m x n) array with all values 0
* @param {number} m the row
* @param {number} n the column
* @return {Array}
*/
function zeros(m, n) {
var zeroArray = [];
for (var i = 0; i < m ; i++) {
zeroArray[i] = [];
for (var j = 0; j < n; j++) {
zeroArray[i][j] = 0;
}
}
return zeroArray;
}
var clusterWithKmeans = {
centroids: centroids,
clusterAssigned: clusterAssigned
};
return clusterWithKmeans;
}
/**
* Sums each element in the array.
* Internal use, for performance considerations, to avoid
* unnecessary judgments and calculations.
* @param {Array} vector
* @return {number}
*/
function sum(vector) {
var sum = 0;
for (var i = 0; i < vector.length; i++) {
sum += vector[i];
}
return sum;
}
/**
* Calculate the average of each column in a two-dimensional array
* and returns the values as an array.
* @param {Array.<Array>} dataList two-dimensional array
* @return {Array}
*/
function meanInColumns(dataList) {
/**
* Computes the sum of the specified column elements in a two-dimensional array
* @param {Array.<Array>} dataList two-dimensional array
* @param {number} n the specified column, zero-based
* @return {number}
*/
function sumOfColumn(dataList, n) {
var sum = 0;
for (var i = 0; i < dataList.length; i++) {
sum += dataList[i][n];
}
return sum;
}
var size = arraySize(dataList);
var meanArray = [];
var sum;
var mean;
for (var j = 0; j < size[1]; j++) {
sum = 0;
for (var i = 0; i < size[0]; i++) {
sum += dataList[i][j];
}
mean = sum / size[0];
meanArray.push(mean);
}
return meanArray;
}
/**
* The combine of hierarchical clustering and k-means.
* @param {Array} data two-dimension array.
* @param {[type]} k the number of clusters in a dataset
* @param {boolean} stepByStep
* @return {}
*/
function hierarchicalKMeans(data, k, stepByStep) {
function ascending(a, b) {
var dataSet = dataPreprocess(data);
var size = arraySize(dataSet);
var clusterAssment = zeros(size[0], 2);
// initial center point
var centroid0 = meanInColumns(dataSet);
var centList = [centroid0];
var squareError;
for (var i = 0; i < size[0]; i++) {
squareError = distEuclid(dataSet[i], centroid0);
clusterAssment[i][1] = mathPow(squareError, 2);
}
var lowestSSE;
var ptsInClust;
var ptsNotClust;
var clusterInfo;
var sseSplit;
var sseNotSplit;
var index = 1;
var result = {
isEnd: false
};
return a > b ? 1 : a < b ? -1 : a === b ? 0 : NaN;
function oneStep() {
//the existing clusters are continuously divided
//until the number of clusters is k
if (index < k) {
lowestSSE = Infinity;
var centSplit;
var newCentroid;
var newClusterAss;
}
for (var j = 0; j < centList.length; j++) {
ptsInClust = [];
ptsNotClust = [];
for (var i = 0; i < clusterAssment.length; i++) {
if (clusterAssment[i][0] === j) {
ptsInClust.push(dataSet[i]);
}
else {
ptsNotClust.push(clusterAssment[i][1]);
}
}
clusterInfo = kMeans(ptsInClust, 2);
sseSplit = sumOfColumn(clusterInfo.clusterAssigned, 1);
sseNotSplit = arraySum(ptsNotClust);
if (sseSplit + sseNotSplit < lowestSSE) {
lowestSSE = sseNotSplit + sseSplit;
centSplit = j;
newCentroid = clusterInfo.centroids;
newClusterAss = clusterInfo.clusterAssigned;
}
}
/**
* Binary search algorithm --- this bisector is specidfied to histogram, which every bin like that [a, b),
* so the return value use to add 1.
* @param {Array.<number>} array
* @param {number} value
* @param {number} start
* @param {number} end
* @return {number}
*/
function bisect(array, value, start, end) { //移出去
for (var i = 0; i < newClusterAss.length; i++) {
if (newClusterAss[i][0] === 0) {
newClusterAss[i][0] = centSplit;
}
else if (newClusterAss[i][0] === 1) {
newClusterAss[i][0] = centList.length;
}
}
if (start == null) {
start = 0;
}
if (end == null) {
end = array.length;
}
while (start < end) {
var mid = Math.floor((start + end) / 2);
var compare = ascending(array[mid], value);
if (compare > 0) {
end = mid;
}
else if (compare < 0) {
start = mid + 1;
}
else {
return mid + 1;
}
}
return start;
}
centList[centSplit] = newCentroid[0];
centList.push(newCentroid[1]);
for ( i = 0, j = 0; i < clusterAssment.length && j < newClusterAss.length; i++) {
if (clusterAssment[i][0] === centSplit) {
clusterAssment[i][0] = newClusterAss[j][0];
clusterAssment[i][1] = newClusterAss[j++][1];
}
}
/**
* 数组映射
* @memberOf module:zrender/core/util
* @param {Array} obj
* @param {Function} cb
* @param {*} [context]
* @return {Array}
*/
function map(obj, cb, context) {
if (!(obj && cb)) {
return;
}
if (obj.map && obj.map === nativeMap) {
return obj.map(cb, context);
}
else {
var result = [];
for (var i = 0, len = obj.length; i < len; i++) {
result.push(cb.call(context, obj[i], i, obj));
}
return result;
}
}
var pointInClust = [];
for (var i = 0; i < centList.length; i++) {
pointInClust[i] = [];
for (var j = 0; j < clusterAssment.length; j++) {
if (clusterAssment[j][0] === i) {
pointInClust[i].push(dataSet[j]);
}
}
}
result.clusterAssment = clusterAssment;
result.centroids = centList;
result.pointsInCluster = pointInClust;
return {
size: size,
isArray: isArray,
zeros: zeros,
sum: sum,
sumOfColumn: sumOfColumn,
ascending: ascending,
bisect: bisect,
map: map
};
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
index++;
}
else {
result.isEnd = true;
}
/***/ }),
/* 5 */
/***/ (function(module, exports, __webpack_require__) {
return result;
}
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var step = {
next: oneStep
};
/**
* Test whether value is a number.
* @param {*} value
* @return {boolean}
*/
function isNumber(value) {
if (!stepByStep) {
var result;
while (!(result = step.next()).isEnd);
return result;
}
else {
return step;
}
value = (value === null ? NaN : +value);
return typeof value === 'number' && !isNaN(value);
}
}
/**
* Test if a number is integer.
* @param {number} value
* @return {boolean}
*/
function isInteger(value) {
return isFinite(value) && value === Math.round(value);
}
/**
* Create random centroid of kmeans.
* @param {Array.<number>} dataSet two-dimension array
* @param {number} k the number of centroids to be created
* @return {Array.<number>} random centroids of kmeans
*/
function createRandCent(dataSet, k) {
var size = arraySize(dataSet);
//constructs a two-dimensional array with all values 0
var centroids = zeros(k, size[1]);
var minJ;
var maxJ;
var rangeJ;
//create random cluster centers, within bounds of each dimension
for (var j = 0; j < size[1]; j++) {
minJ = dataSet[0][j];
maxJ = dataSet[0][j];
for (var i = 1; i < size[0]; i++) {
if (dataSet[i][j] < minJ) {
minJ = dataSet[i][j];
}
if (dataSet[i][j] > maxJ) {
maxJ = dataSet[i][j];
}
}
rangeJ = maxJ - minJ;
for (var i = 0; i < k; i++) {
centroids[i][j] = minJ + rangeJ * Math.random();
}
}
return centroids;
}
return {
isNumber: isNumber,
isInteger: isInteger
};
/**
* Distance method for calculating similarity
* @param {Array.<number>} vec1
* @param {Array.<nnumber>} vec2
* @return {number}
*/
function distEuclid(vec1, vec2) {
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
if (!isArray(vec1) && !isArray(vec2)) {
return mathSqrt(mathPow(vec1 - vec2, 2));
}
/***/ }),
/* 6 */
/***/ (function(module, exports, __webpack_require__) {
var powerSum = 0;
//subtract the corresponding elements in the vectors
for (var i = 0; i < vec1.length; i++) {
powerSum += mathPow(vec1[i] - vec2[i], 2);
}
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
return mathSqrt(powerSum);
}
var dataPreprocess = __webpack_require__(3);
return {
kMeans: kMeans,
hierarchicalKMeans: hierarchicalKMeans
};
var regreMethods = {
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/**
* Common linear regression algorithm
* @param {Array.<Array.<number>>} data two-dimensional array
* @return {Object}
*/
linear: function (data) {
/***/ }),
/* 5 */
/***/ (function(module, exports, __webpack_require__) {
var predata = dataPreprocess(data);
var sumX = 0;
var sumY = 0;
var sumXY = 0;
var sumXX = 0;
var len = predata.length;
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
for (var i = 0; i < len; i++) {
sumX += predata[i][0];
sumY += predata[i][1];
sumXY += predata[i][0] * predata[i][1];
sumXX += predata[i][0] * predata[i][0];
}
var array = __webpack_require__(0);
var dataPreprocess = __webpack_require__(2);
var gradient = ((len * sumXY) - (sumX * sumY)) / ((len * sumXX) - (sumX * sumX));
var intercept = (sumY / len) - ((gradient * sumX) / len);
var result = [];
for (var j = 0; j < predata.length; j++) {
var coordinate = [predata[j][0], gradient * predata[j][0] + intercept];
result.push(coordinate);
}
var regreMethods = {
var string = 'y = ' + Math.round(gradient * 100) / 100 + 'x + ' + Math.round(intercept * 100) / 100;
/**
* Common linear regression algorithm
* @param {Array.<Array.<number>>} data two-dimensional array
* @return {Object}
*/
linear: function (data) {
return {
points: result,
parameter: {
gradient: gradient,
intercept: intercept
},
expression: string
};
},
var predata = dataPreprocess(data);
var sumX = 0;
var sumY = 0;
var sumXY = 0;
var sumXX = 0;
var len = predata.length;
/**
* If the raw data include [0,0] point, we should choose linearThroughOrigin
* instead of linear.
* @param {Array.<Array>} data two-dimensional number array
* @return {Object}
*/
linearThroughOrigin: function (data) {
for (var i = 0; i < len; i++) {
sumX += predata[i][0];
sumY += predata[i][1];
sumXY += predata[i][0] * predata[i][1];
sumXX += predata[i][0] * predata[i][0];
}
var predata = dataPreprocess(data);
var sumXX = 0;
var sumXY = 0;
var gradient = ((len * sumXY) - (sumX * sumY)) / ((len * sumXX) - (sumX * sumX));
var intercept = (sumY / len) - ((gradient * sumX) / len);
for (var i = 0; i < predata.length; i++) {
sumXX += predata[i][0] * predata[i][0];
sumXY += predata[i][0] * predata[i][1];
}
var result = [];
for (var j = 0; j < predata.length; j++) {
var coordinate = [predata[j][0], gradient * predata[j][0] + intercept];
result.push(coordinate);
}
var gradient = sumXY / sumXX;
var result = [];
var string = 'y = ' + Math.round(gradient * 100) / 100 + 'x + ' + Math.round(intercept * 100) / 100;
for (var j = 0; j < predata.length; j++) {
var coordinate = [predata[j][0], predata[j][0] * gradient];
result.push(coordinate);
}
return {
points: result,
parameter: {
gradient: gradient,
intercept: intercept
},
expression: string
};
},
var string = 'y = ' + Math.round(gradient * 100) / 100 + 'x';
/**
* If the raw data include [0,0] point, we should choose linearThroughOrigin
* instead of linear.
* @param {Array.<Array>} data two-dimensional number array
* @return {Object}
*/
linearThroughOrigin: function (data) {
return {
points: result,
parameter: {
gradient: gradient
},
expression: string
};
},
var predata = dataPreprocess(data);
var sumXX = 0;
var sumXY = 0;
/**
* Exponential regression
* @param {Array.<Array.<number>>} data two-dimensional number array
* @return {Object}
*/
exponential: function (data) {
for (var i = 0; i < predata.length; i++) {
sumXX += predata[i][0] * predata[i][0];
sumXY += predata[i][0] * predata[i][1];
}
var predata = dataPreprocess(data);
var sumX = 0;
var sumY = 0;
var sumXXY = 0;
var sumYlny = 0;
var sumXYlny = 0;
var sumXY = 0;
var gradient = sumXY / sumXX;
var result = [];
for (var i = 0; i < predata.length; i++) {
sumX += predata[i][0];
sumY += predata[i][1];
sumXY += predata[i][0] * predata[i][1];
sumXXY += predata[i][0] * predata[i][0] * predata[i][1];
sumYlny += predata[i][1] * Math.log(predata[i][1]);
sumXYlny += predata[i][0] * predata[i][1] * Math.log(predata[i][1]);
}
for (var j = 0; j < predata.length; j++) {
var coordinate = [predata[j][0], predata[j][0] * gradient];
result.push(coordinate);
}
var denominator = (sumY * sumXXY) - (sumXY * sumXY);
var coefficient = Math.pow(Math.E, (sumXXY * sumYlny - sumXY * sumXYlny) / denominator);
var index = (sumY * sumXYlny - sumXY * sumYlny) / denominator;
var result = [];
var string = 'y = ' + Math.round(gradient * 100) / 100 + 'x';
for (var j = 0; j < predata.length; j++) {
var coordinate = [predata[j][0], coefficient * Math.pow(Math.E, index * predata[j][0])];
result.push(coordinate);
}
return {
points: result,
parameter: {
gradient: gradient
},
expression: string
};
},
var string = 'y = ' + Math.round(coefficient * 100) / 100 + 'e^(' + Math.round(index * 100) / 100 + 'x)';
/**
* Exponential regression
* @param {Array.<Array.<number>>} data two-dimensional number array
* @return {Object}
*/
exponential: function (data) {
return {
points: result,
parameter: {
coefficient: coefficient,
index: index
},
expression: string
};
var predata = dataPreprocess(data);
var sumX = 0;
var sumY = 0;
var sumXXY = 0;
var sumYlny = 0;
var sumXYlny = 0;
var sumXY = 0;
},
for (var i = 0; i < predata.length; i++) {
sumX += predata[i][0];
sumY += predata[i][1];
sumXY += predata[i][0] * predata[i][1];
sumXXY += predata[i][0] * predata[i][0] * predata[i][1];
sumYlny += predata[i][1] * Math.log(predata[i][1]);
sumXYlny += predata[i][0] * predata[i][1] * Math.log(predata[i][1]);
}
/**
* Logarithmic regression
* @param {Array.<Array.<number>>} data two-dimensional number array
* @return {Object}
*/
logarithmic: function (data) {
var denominator = (sumY * sumXXY) - (sumXY * sumXY);
var coefficient = Math.pow(Math.E, (sumXXY * sumYlny - sumXY * sumXYlny) / denominator);
var index = (sumY * sumXYlny - sumXY * sumYlny) / denominator;
var result = [];
var predata = dataPreprocess(data);
var sumlnx = 0;
var sumYlnx = 0;
var sumY = 0;
var sumlnxlnx = 0;
for (var j = 0; j < predata.length; j++) {
var coordinate = [predata[j][0], coefficient * Math.pow(Math.E, index * predata[j][0])];
result.push(coordinate);
}
for (var i = 0; i < predata.length; i++) {
sumlnx += Math.log(predata[i][0]);
sumYlnx += predata[i][1] * Math.log(predata[i][0]);
sumY += predata[i][1];
sumlnxlnx += Math.pow(Math.log(predata[i][0]), 2);
}
var string = 'y = ' + Math.round(coefficient * 100) / 100 + 'e^(' + Math.round(index * 100) / 100 + 'x)';
var gradient = (i * sumYlnx - sumY * sumlnx) / (i * sumlnxlnx - sumlnx * sumlnx);
var intercept = (sumY - gradient * sumlnx) / i;
var result = [];
return {
points: result,
parameter: {
coefficient: coefficient,
index: index
},
expression: string
};
for (var j = 0; j < predata.length; j++) {
var coordinate = [predata[j][0], gradient * Math.log(predata[j][0]) + intercept];
result.push(coordinate);
}
},
var string =
'y = '
+ Math.round(intercept * 100) / 100
+ ' + '
+ Math.round(gradient * 100) / 100 + 'ln(x)';
/**
* Logarithmic regression
* @param {Array.<Array.<number>>} data two-dimensional number array
* @return {Object}
*/
logarithmic: function (data) {
return {
points: result,
parameter: {
gradient: gradient,
intercept: intercept
},
expression: string
};
var predata = dataPreprocess(data);
var sumlnx = 0;
var sumYlnx = 0;
var sumY = 0;
var sumlnxlnx = 0;
},
for (var i = 0; i < predata.length; i++) {
sumlnx += Math.log(predata[i][0]);
sumYlnx += predata[i][1] * Math.log(predata[i][0]);
sumY += predata[i][1];
sumlnxlnx += Math.pow(Math.log(predata[i][0]), 2);
}
/**
* Polynomial regression
* @param {Array.<Array.<number>>} data two-dimensional number array
* @param {number} order order of polynomials
* @return {Object}
*/
polynomial: function (data, order) {
var gradient = (i * sumYlnx - sumY * sumlnx) / (i * sumlnxlnx - sumlnx * sumlnx);
var intercept = (sumY - gradient * sumlnx) / i;
var result = [];
var predata = dataPreprocess(data);
if (typeof order === 'undefined') {
order = 2;
}
//coefficient matrix
var coeMatrix = [];
var lhs = [];
var k = order + 1;
for (var j = 0; j < predata.length; j++) {
var coordinate = [predata[j][0], gradient * Math.log(predata[j][0]) + intercept];
result.push(coordinate);
}
for (var i = 0; i < k; i++) {
var sumA = 0;
for (var n = 0; n < predata.length; n++) {
sumA += predata[n][1] * Math.pow(predata[n][0], i);
}
lhs.push(sumA);
var string = 'y = ' + Math.round(intercept * 100) / 100 + ' + ' + Math.round(gradient * 100) / 100 + 'ln(x)';
var temp = [];
for (var j = 0; j < k; j++) {
var sumB = 0;
for (var m = 0; m < predata.length; m++) {
sumB += Math.pow(predata[m][0], i + j);
}
temp.push(sumB);
}
coeMatrix.push(temp);
}
coeMatrix.push(lhs);
return {
points: result,
parameter: {
gradient: gradient,
intercept: intercept
},
expression: string
};
var coeArray = gaussianElimination(coeMatrix, k);
},
var result = [];
/**
* Polynomial regression
* @param {Array.<Array.<number>>} data two-dimensional number array
* @param {number} order order of polynomials
* @return {Object}
*/
polynomial: function (data, order) {
for (var i = 0; i < predata.length; i++) {
var value = 0;
for (var n = 0; n < coeArray.length; n++) {
value += coeArray[n] * Math.pow(predata[i][0], n);
}
result.push([predata[i][0], value]);
}
var predata = dataPreprocess(data);
if (typeof order === 'undefined') {
order = 2;
}
//coefficient matrix
var coeMatrix = [];
var lhs = [];
var k = order + 1;
var string = 'y = ';
for (var i = coeArray.length - 1; i >= 0; i--) {
if (i > 1) {
string += Math.round(coeArray[i] * Math.pow(10, i + 1)) / Math.pow(10, i + 1) + 'x^' + i + ' + ';
}
else if (i === 1) {
string += Math.round(coeArray[i] * 100) / 100 + 'x' + ' + ';
}
else {
string += Math.round(coeArray[i] * 100) / 100;
}
}
for (var i = 0; i < k; i++) {
var sumA = 0;
for (var n = 0; n < predata.length; n++) {
sumA += predata[n][1] * Math.pow(predata[n][0], i);
}
lhs.push(sumA);
return {
points: result,
parameter: coeArray,
expression: string
};
var temp = [];
for (var j = 0; j < k; j++) {
var sumB = 0;
for (var m = 0; m < predata.length; m++) {
sumB += Math.pow(predata[m][0], i + j);
}
temp.push(sumB);
}
coeMatrix.push(temp);
}
coeMatrix.push(lhs);
}
var coeArray = gaussianElimination(coeMatrix, k);
};
var result = [];
/**
* Gaussian elimination
* @param {Array.<Array.<number>>} matrix two-dimensional number array
* @param {number} number
* @return {Array}
*/
function gaussianElimination(matrix, number) {
for (var i = 0; i < predata.length; i++) {
var value = 0;
for (var n = 0; n < coeArray.length; n++) {
value += coeArray[n] * Math.pow(predata[i][0], n);
}
result.push([predata[i][0], value]);
}
for (var i = 0; i < matrix.length - 1; i++) {
var maxColumn = i;
for (var j = i + 1; j < matrix.length - 1; j++) {
if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][maxColumn])) {
maxColumn = j;
}
}
// the matrix here is the transpose of the common Augmented matrix.
// so the can perform the primary column transform, in fact, equivalent
// to the primary line changes
for (var k = i; k < matrix.length; k++) {
var temp = matrix[k][i];
matrix[k][i] = matrix[k][maxColumn];
matrix[k][maxColumn] = temp;
}
for (var n = i + 1; n < matrix.length - 1; n++) {
for (var m = matrix.length - 1; m >= i; m--) {
matrix[m][n] -= matrix[m][i] / matrix[i][i] * matrix[i][n];
}
}
}
var string = 'y = ';
for (var i = coeArray.length - 1; i >= 0; i--) {
if (i > 1) {
string += Math.round(coeArray[i] * Math.pow(10, i + 1)) / Math.pow(10, i + 1) + 'x^' + i + ' + ';
}
else if (i === 1) {
string += Math.round(coeArray[i] * 100) / 100 + 'x' + ' + ';
}
else {
string += Math.round(coeArray[i] * 100) / 100;
}
}
var data = new Array(number);
var len = matrix.length - 1;
for (var j = matrix.length - 2; j >= 0; j--) {
var temp = 0;
for (var i = j + 1; i < matrix.length - 1; i++) {
temp += matrix[i][j] * data[i];
}
data[j] = (matrix[len][j] - temp) / matrix[j][j];
return {
points: result,
parameter: coeArray,
expression: string
};
}
}
return data;
}
};
var regression = function (regreMethod, data, order) {
/**
* Gaussian elimination
* @param {Array.<Array.<number>>} matrix two-dimensional number array
* @param {number} number
* @return {Array}
*/
function gaussianElimination(matrix, number) {
return regreMethods[regreMethod](data, order);
for (var i = 0; i < matrix.length - 1; i++) {
var maxColumn = i;
for (var j = i + 1; j < matrix.length - 1; j++) {
if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][maxColumn])) {
maxColumn = j;
}
}
// the matrix here is the transpose of the common Augmented matrix.
// so the can perform the primary column transform, in fact, equivalent
// to the primary line changes
for (var k = i; k < matrix.length; k++) {
var temp = matrix[k][i];
matrix[k][i] = matrix[k][maxColumn];
matrix[k][maxColumn] = temp;
}
for (var n = i + 1; n < matrix.length - 1; n++) {
for (var m = matrix.length - 1; m >= i; m--) {
matrix[m][n] -= matrix[m][i] / matrix[i][i] * matrix[i][n];
}
}
}
};
var data = Array(number);
var len = matrix.length - 1;
for (var j = matrix.length - 2; j >= 0; j--) {
var temp = 0;
for (var i = j + 1; i < matrix.length - 1; i++) {
temp += matrix[i][j] * data[i];
}
data[j] = (matrix[len][j] - temp) / matrix[j][j];
return regression;
}
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
return data;
}
/***/ }),
/* 7 */
/***/ (function(module, exports, __webpack_require__) {
var regression = function (regreMethod, data, order) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
return regreMethods[regreMethod](data, order);
var statistics = {};
};
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);
return regression;
return statistics;
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 6 */
/* 8 */
/***/ (function(module, exports, __webpack_require__) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var statistic = {};
var number = __webpack_require__(5);
var isNumber = number.isNumber;
statistic.max = __webpack_require__(7);
statistic.mean = __webpack_require__(8);
statistic.median = __webpack_require__(9);
statistic.min = __webpack_require__(10);
statistic.sum = __webpack_require__(11);
/**
* Is a method for computing the max value of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function max(data) {
return statistic;
var maxData = -Infinity;
for (var i = 0; i < data.length; i++) {
if (isNumber(data[i]) && data[i] > maxData) {
maxData = data[i];
}
}
return maxData;
}
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
return max;
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 7 */
/* 9 */
/***/ (function(module, exports, __webpack_require__) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var array = __webpack_require__(0);
var isArray = array.isArray;
var number = __webpack_require__(1);
var isNumber = number.isNumber;
var variance = __webpack_require__(10);
/**
* Is a method for computing the max value of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function max(data) {
/**
* Computing the deviation
* @param {Array.<number>} data
* @return {number}
*/
return function (data) {
if (!isArray(data)) {
throw new Error('Invalid data type, you should input an array');
}
var squaredDeviation = variance(data);
var maxData = -Infinity;
for (var i = 0; i < data.length; i++) {
if (isNumber(data[i]) && data[i] > maxData) {
maxData = data[i];
}
}
return maxData;
}
return squaredDeviation ? Math.sqrt(squaredDeviation) : squaredDeviation;
};
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
return max;
/***/ }),
/* 10 */
/***/ (function(module, exports, __webpack_require__) {
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var number = __webpack_require__(5);
var isNumber = number.isNumber;
var mean = __webpack_require__(11);
/**
* Computing the variance of list of sample
* @param {Array.<number>} data
* @return {number}
*/
function sampleVariance(data) {
var len = data.length;
if (!len || len < 2) {
return 0;
}
if (data.length >= 2) {
var meanValue = mean(data);
var sum = 0;
var temple;
for (var i = 0; i < data.length; i++) {
if (isNumber(data[i])) {
temple = data[i] - meanValue;
sum += temple * temple;
}
}
return sum / data.length - 1;
}
}
return sampleVariance;
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 8 */
/* 11 */
/***/ (function(module, exports, __webpack_require__) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var array = __webpack_require__(0);
var isArray = array.isArray;
var number = __webpack_require__(1);
var isNumber = number.isNumber;
var sum = __webpack_require__(12);
/**
* Is a method for computing the mean value of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function mean(data) {
/**
* Is a method for computing the mean value of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function mean(data) {
if (!isArray(data)) {
throw new Error('Invalid data type, you should input an array');
}
var sumData = 0;
var len = data.length;
for (var i = 0; i < len; i++) {
if (isNumber(data[i])) {
sumData += data[i];
}
}
return sumData / len;
}
var len = data.length;
return mean;
if (!len) {
return 0;
}
return sum(data) / data.length;
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
}
return mean;
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 9 */
/* 12 */
/***/ (function(module, exports, __webpack_require__) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var array = __webpack_require__(0);
var isArray = array.isArray;
var number = __webpack_require__(1);
var isNumber = number.isNumber;
var number = __webpack_require__(5);
var isNumber = number.isNumber;
/**
* Is a method for computing the median value of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function median(data) {
/**
* Is a method for computing the sum of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function sum(data) {
if (!isArray(data)) {
throw new Error('Invalid data type, you should input an array');
}
var predata = [];
var len = data.length;
for (var i = 0; i < len; i++) {
if (isNumber(data[i])) {
predata.push(data[i]);
}
}
var len = data.length;
predata.sort(function (a, b) {
return a - b;
});
if (!len) {
return 0;
}
var sumData = 0;
for (var i = 0; i < len; i++) {
if (isNumber(data[i])) {
sumData += data[i];
}
}
return sumData;
}
var medianData;
return sum;
if ((len % 2) === 0) {
var n = len / 2;
medianData = (predata[n] + predata[n + 1]) / 2;
}
else {
var m = (len + 1) / 2;
medianData = predata[m];
}
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
return medianData;
}
/***/ }),
/* 13 */
/***/ (function(module, exports, __webpack_require__) {
return median;
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
var quantile = __webpack_require__(14);
/**
* Is a method for computing the median value of a sorted array of numbers
* @param {Array.<number>} data
* @return {number}
*/
function median(data) {
return quantile(data, 0.5);
}
return median;
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 10 */
/* 14 */
/***/ (function(module, exports, __webpack_require__) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var array = __webpack_require__(0);
var isArray = array.isArray;
var number = __webpack_require__(1);
var isNumber = number.isNumber;
/**
* Estimating quantiles from a sorted sample of numbers
* @see https://en.wikipedia.org/wiki/Quantile#Estimating_quantiles_from_a_sample
* R-7 method
* @param {Array.<number>} data sorted array
* @param {number} p
*/
return function (data, p) {
/**
* Is a method for computing the min value of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function min(data) {
var len = data.length;
if (!isArray(data)) {
throw new Error('Invalid data type, you should input an array');
}
if (!len) {
return 0;
}
if (p <= 0 || len < 2) {
return data[0];
}
if (p >= 1) {
return data[len -1];
}
// in the wikipedia's R-7 method h = (N - 1)p + 1, but here array index start from 0
var h = (len - 1) * p;
var i = Math.floor(h);
var a = data[i];
var b = data[i + 1];
return a + (b - a) * (h - i);
};
var minData = Infinity;
for (var i = 0; i < data.length; i++) {
if (isNumber(data[i]) && data[i] < minData) {
minData = data[i];
}
}
return minData;
}
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
return min;
/***/ }),
/* 15 */
/***/ (function(module, exports, __webpack_require__) {
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var number = __webpack_require__(5);
var isNumber = number.isNumber;
/**
* Is a method for computing the min value of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function min(data) {
var minData = Infinity;
for (var i = 0; i < data.length; i++) {
if (isNumber(data[i]) && data[i] < minData) {
minData = data[i];
}
}
return minData;
}
return min;
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 11 */
/* 16 */
/***/ (function(module, exports, __webpack_require__) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
var array = __webpack_require__(0);
var isArray = array.isArray;
var number = __webpack_require__(1);
var isNumber = number.isNumber;
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 ascending = array.ascending;
var map = array.map;
var range = __webpack_require__(17);
var bisect = array.bisect;
var tickStep = __webpack_require__(18);
/**
* Is a method for computing the sum of a list of numbers,
* which will filter other data types.
* @param {Array.<number>} data
* @return {number}
*/
function sum(data) {
/**
* Compute bins for histogram
* @param {Array.<number>} data
* @param {string} threshold
* @return {Object}
*/
function computeBins(data, threshold) {
if (!isArray(data)) {
throw new Error('Invalid data type, you should input an array');
}
var sumData = 0;
for (var i = 0; i < data.length; i++) {
if (isNumber(data[i])) {
sumData += data[i];
}
}
return sumData;
}
if (threshold == null) {
return sum;
threshold = thresholdMethod.squareRoot;
}.call(exports, __webpack_require__, exports, module),
__WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
}
else {
threshold = thresholdMethod[threshold];
}
var values = dataPreprocess(data);
var maxValue = max(values);
var minValue = min(values);
var binsNumber = threshold(values, minValue, maxValue);
var step = tickStep(minValue, maxValue, binsNumber);
// 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 len = rangeArray.length;
var bins = new Array(len + 1);
for (var i = 0; i <= len; i++) {
bins[i] = {};
bins[i].sample = [];
bins[i].x0 = i > 0 // 不要数组直接挂属性，改成Object
? rangeArray[i - 1]
: (rangeArray[i] - minValue) === step
? minValue
: (rangeArray[i] - step);
bins[i].x1 = i < len
? rangeArray[i]
: (maxValue - rangeArray[i-1]) === step
? maxValue
: rangeArray[i - 1] + step;
}
for (var i = 0; i < values.length; i++) {
if (minValue <= values[i] && values[i] <= maxValue) {
bins[bisect(rangeArray, values[i], 0, len)].sample.push(values[i]);
}
}
var data = map(bins, function (bin) {
return [(bin.x0 + bin.x1) / 2, bin.sample.length];
});
return {
bins: bins,
data: data
};
}
/**
* Four kinds of threshold methods used to
* compute how much bins the histogram should be divided
* @see https://en.wikipedia.org/wiki/Histogram
* @type {Object}
*/
var thresholdMethod = {
squareRoot: function (data) {
var bins = Math.ceil(Math.sqrt(data.length));
return bins > 50 ? 50 : bins;
},
scott: function (data, min, max) {
return Math.ceil((max - min) / (3.5 * deviation(data) * Math.pow(data.length, -1 / 3)));
},
freedmanDiaconis: function (data, min, max) {
data.sort(ascending);
return Math.ceil(
(max - min) / (2 * (quantile(data, 0.75) - quantile(data, 0.25)) * Math.pow(data.length, -1 / 3))
);
},
sturges: function (data) {
return Math.ceil(Math.log(data.length) / Math.LN2) + 1;
}
};
return computeBins;
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 12 */
/* 17 */
/***/ (function(module, exports, __webpack_require__) {
module.exports = __webpack_require__(3);
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
/**
* Computing range array
* @param {number} start
* @param {number} stop
* @param {number} step
* @return {Array.<number>}
*/
return function (start, stop, step) {
var len = arguments.length;
if (len < 2) {
stop = start;
start = 0;
step = 1;
}
else if (len < 3) {
step = 1;
}
else {
step = +step;
}
var n = Math.ceil((stop - start) / step);
var range = new Array(n + 1);
for (var i = 0; i < n + 1; i++) {
range[i] = start + i * step;
}
return range;
};
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ }),
/* 18 */
/***/ (function(module, exports, __webpack_require__) {
var __WEBPACK_AMD_DEFINE_RESULT__;!(__WEBPACK_AMD_DEFINE_RESULT__ = function (require) {
/**
* Computing the length of step
* @see https://github.com/d3/d3-array/blob/master/src/ticks.js
* @param {number} start
* @param {number} stop
* @param {number} count
*/
return function (start, stop, count) {
var step0 = Math.abs(stop - start) / count;
var step1 = Math.pow(10, Math.floor(Math.log(step0) / Math.LN10));
var error = step0 / step1;
if (error >= Math.sqrt(50)) {
step1 *= 10;
}
else if (error >= Math.sqrt(10)) {
step1 *= 5;
}
else if(error >= Math.sqrt(2)) {
step1 *= 2;
}
return stop >= start ? step1 : -step1;
};
}.call(exports, __webpack_require__, exports, module), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__));
/***/ })
/******/ ]);
});
/******/ ])
});
;

2

dist/ecStat.min.js

@@ -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(o){if(t[o])return t[o].exports;var e=t[o]={i:o,l:!1,exports:{}};return r[o].call(e.exports,e,e.exports,n),e.l=!0,e.exports}var t={};return n.m=r,n.c=t,n.i=function(r){return r},n.d=function(r,t,o){n.o(r,t)||Object.defineProperty(r,t,{configurable:!1,enumerable:!0,get:o})},n.n=function(r){var t=r&&r.__esModule?function(){return r.default}:function(){return r};return n.d(t,"a",t),t},n.o=function(r,n){return Object.prototype.hasOwnProperty.call(r,n)},n.p="",n(n.s=12)}([function(r,n,t){var o;o=function(r){function n(r){for(var n=[];o(r);)n.push(r.length),r=r[0];return n}function o(r){return"[object Array]"===i.call(r)}function e(r,n){for(var t=[],o=0;o<r;o++){t[o]=[];for(var e=0;e<n;e++)t[o][e]=0}return t}function a(r){for(var 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2

package.json
{
"name": "echarts-stat",
"version": "1.0.0",
"version": "1.1.0",
"description": "a statistical tool for ECharts",

@@ -5,0 +5,0 @@ "main": "index.js",

185

README.md

@@ -28,6 +28,83 @@ # ecStat

* [Histogram](#histogram)
* [Clustering](#clustering)
* [Regression](#regression)
* [Statistic](#statistic)
* [Statistics](#statistic)
### Histogram
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.
#### Syntax
```js
var bins = ecStat.histogram(data, binMethod);
```
##### Parameter
* `data` - `Array<number>`. Data samples of numbers.
```js
var data = [8.6, 8.8, 10.5, 10.7, 10.8, 11.0, ... ];
```
* `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.
* `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):
```js
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):
```js
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):
```js
var bins = ecStat.histogram(data, 'freedmanDiaconis');
```
* `sturges` - Returns the number of bins according to [Sturges' formula](https://en.wikipedia.org/wiki/Histogram#Mathematical_definition):
```js
var bins = ecStat.histogram(data, 'sturges');
```
##### Return Value
* `bins` - `Object`. Contain detailed messages of each bin and data used for [ECharts](https://github.com/ecomfe/echarts) to draw the bar chart.
* `bins.bins` - `Array.<Object>`. An array of bins, where each bin is an object, containing three attributes:
* `x0` - `number`. The lower bound of the bin (inclusive).
* `x1` - `number`. The upper bound of the bin (exclusive).
* `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.
#### Examples
When using ECharts bar chart to draw the histogram, we must notice that, setting the `xAxis.scale` as `true`.
```html
<script src='https://cdn.bootcss.com/echarts/3.4.0/echarts.js'></script>
<script src='./dist/ecStat.js'></script>
<script>
var bins = ecStat.histogram(data);
var option = {
...
xAxis: [{
type: 'value',
// this must be set as true, otherwise barWidth and bins width can not corresponding on
scale: true
}],
...
}
</script>
```
![histogram](img/histogram.png)
[Run](http://gallery.echartsjs.com/editor.html?c=xBk5VZddJW)
### Clustering

@@ -43,3 +120,3 @@

* 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` － `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]`.

@@ -54,8 +131,8 @@ ```js

```
* clusterNumer － `number`, the number of clusters generated
* stepByStep － `Boolean`, control whether doing the clustering step by step
* `clusterNumer` － `number`. The number of clusters generated
* `stepByStep` － `boolean`. Control whether doing the clustering step by step
##### Return Value
* result － `Object`, including the centroids, clusterAssment, and pointsInCluster. For Example:
* `result` － `Object`. Including the centroids, clusterAssment, and pointsInCluster. For Example:

@@ -141,4 +218,4 @@ ```js

* 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` - `two-dimensional Numeric Array`. Each data object should have two numeric attributes in the original data set. For Example:

@@ -153,7 +230,7 @@ ```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
##### Return Value
* myRegression - `Object`, Including points, parameter, and expression. For Example:
* `myRegression` - `Object`. Including points, parameter, and expression. For Example:

@@ -246,81 +323,127 @@ ```js

### Statistic
### Statistics
This interface provides general statistical services such as maximum, minimum, average, median, sum, etc.
This interface provides basic summary statistical services.
#### ecStat.statistic.max()
#### ecStat.statistics.deviation()
##### Syntax
```
var maxValue = ecStat.statistic.max(dataList);
var sampleDeviation = ecStat.statistics.deviation(dataList);
```
##### Parameter
* dataList : `Array.<number>`
* `dataList` : `Array.<number>`
##### Return Value
* maxValue: `number`, the maximum value of the data list
* `sampleDeviation`: `number`. Return the deviation of the numeric array *dataList*. If the *dataList* is empty or the length less than 2, return 0.
#### ecStat.statistic.min()
#### ecStat.statistics.sampleVariance()
##### Syntax
```
var minValue = ecStat.statistic.min(dataList);
var varianceValue = ecStat.statistics.sampleVariance(dataList);
```
##### Parameter
* dataList : `Array.<number>`
* `dataList` : `Array.<number>`
##### Return Value
* minValue: `number`, the minimum value of the data list
* `varianceValue`: `number`. Return the variance of the numeric array *dataList*. If the *dataList* is empty or the length less than 2, return 0.
#### ecStat.statistic.mean()
#### ecStat.statistics.quantile()
##### Syntax
```
var meanValue = ecStat.statistic.mean(dataList);
var quantileValue = ecStat.statistics.quantile(dataList, p);
```
##### Parameter
* dataList : `Array.<number>`
* `dataList` : `Array.<number>`. Sorted array of numbers.
* `p`: `number`. where 0 =< *p* <= 1. For example, the first quartile at p = 0.25, the seconed quartile at p = 0.5(same as the median), and the third quartile at p = 0.75.
##### Return Value
* meanValue: `number`, the average of the data list
* `quantileValue`: `number`. Return the *p*-quantile of the sorted array of numbers. If p <= 0 or the length of *dataList* less than 2, return the first element of the sorted array; if p >= 1, return the last element of the sorted array; If *dataList* is empty, return 0.
#### ecStat.statistic.median()
#### ecStat.statistics.max()
##### Syntax
```
var medianValue = ecStat.statistic.median(dataList);
var maxValue = ecStat.statistics.max(dataList);
```
##### Parameter
* dataList : `Array.<number>`
* `dataList` : `Array.<number>`
##### Return Value
* medianValue: `number`, the median of the data list
* `maxValue`: `number`. The maximum value of the *dataList*.
#### ecStat.statistic.sum()
#### ecStat.statistics.min()
##### Syntax
```
var sumValue = ecStat.statistic.sum(dataList);
var minValue = ecStat.statistics.min(dataList);
```
##### Parameter
* dataList : `Array.<number>`
* `dataList` : `Array.<number>`
##### Return Value
* sumValue: `number`, the sum of the data list
* `minValue`: `number`. The minimum value of the *dataList*.
#### ecStat.statistics.mean()
##### Syntax
```
var meanValue = ecStat.statistics.mean(dataList);
```
##### Parameter
* `dataList` : `Array.<number>`
##### Return Value
* `meanValue`: `number`. The average of the *dataList*.
#### ecStat.statistics.median()
##### Syntax
```
var medianValue = ecStat.statistics.median(dataList);
```
##### Parameter
* `dataList` : `Array.<number>`. Sorted array of numbers
##### Return Value
* `medianValue`: `number`. The median of the *dataList*.
#### ecStat.statistics.sum()
##### Syntax
```
var sumValue = ecStat.statistics.sum(dataList);
```
##### Parameter
* `dataList` : `Array.<number>`
##### Return Value
* `sumValue`: `number`. The sum of the *dataList*.

3

src/ecStat.js

@@ -7,3 +7,4 @@ define(function (require) {

regression: require('./regression'),
statistic: require('./statistic')
statistics: require('./statistics'),
histogram: require('./histogram')

@@ -10,0 +11,0 @@ };

10

src/regression.js
define(function (require) {
var array = require('./util/array');
var dataPreprocess = require('./util/dataPreprocess');
var regreMethods = {

@@ -163,3 +161,7 @@

var string = 'y = ' + Math.round(intercept * 100) / 100 + ' + ' + Math.round(gradient * 100) / 100 + 'ln(x)';
var string =
'y = '
+ Math.round(intercept * 100) / 100
+ ' + '
+ Math.round(gradient * 100) / 100 + 'ln(x)';

@@ -278,3 +280,3 @@ return {

var data = Array(number);
var data = new Array(number);
var len = matrix.length - 1;

@@ -281,0 +283,0 @@ for (var j = matrix.length - 2; j >= 0; j--) {

77

src/util/array.js
define(function (require) {
var number = require('./number');
var objToString = Object.prototype.toString;
var arrayProto = Array.prototype;
var nativeMap = arrayProto.map;

@@ -46,3 +47,5 @@ /**

/**
* Sums each element in the array
* Sums each element in the array.
* Internal use, for performance considerations, to avoid
* unnecessary judgments and calculations.
* @param {Array} vector

@@ -73,2 +76,67 @@ * @return {number}

function ascending(a, b) {
return a > b ? 1 : a < b ? -1 : a === b ? 0 : NaN;
}
/**
* Binary search algorithm --- this bisector is specidfied to histogram, which every bin like that [a, b),
* so the return value use to add 1.
* @param {Array.<number>} array
* @param {number} value
* @param {number} start
* @param {number} end
* @return {number}
*/
function bisect(array, value, start, end) { //移出去
if (start == null) {
start = 0;
}
if (end == null) {
end = array.length;
}
while (start < end) {
var mid = Math.floor((start + end) / 2);
var compare = ascending(array[mid], value);
if (compare > 0) {
end = mid;
}
else if (compare < 0) {
start = mid + 1;
}
else {
return mid + 1;
}
}
return start;
}
/**
* 数组映射
* @memberOf module:zrender/core/util
* @param {Array} obj
* @param {Function} cb
* @param {*} [context]
* @return {Array}
*/
function map(obj, cb, context) {
if (!(obj && cb)) {
return;
}
if (obj.map && obj.map === nativeMap) {
return obj.map(cb, context);
}
else {
var result = [];
for (var i = 0, len = obj.length; i < len; i++) {
result.push(cb.call(context, obj[i], i, obj));
}
return result;
}
}
return {

@@ -79,5 +147,8 @@ size: size,

sum: sum,
sumOfColumn: sumOfColumn
sumOfColumn: sumOfColumn,
ascending: ascending,
bisect: bisect,
map: map
};
});

28

src/util/dataPreprocess.js

@@ -25,11 +25,25 @@ define(function (require) {

var arraySize = size(data);
for (var i = 0; i < arraySize[0]; i++) {
var isCorrect = true;
for (var j = 0; j < arraySize[1]; j++) {
if (!isNumber(data[i][j])) {
isCorrect = false;
if (arraySize.length === 1) {
for (var i = 0; i < arraySize[0]; i++) {
if (isNumber(data[i])) {
predata.push(data[i]);
}
}
if (isCorrect) {
predata.push(data[i]);
}
else if (arraySize.length === 2) {
for (var i = 0; i < arraySize[0]; i++) {
var isCorrect = true;
for (var j = 0; j < arraySize[1]; j++) {
if (!isNumber(data[i][j])) {
isCorrect = false;
}
}
if (isCorrect) {
predata.push(data[i]);
}
}

@@ -36,0 +50,0 @@ }

2

src/util/number.js

@@ -9,2 +9,4 @@ define(function (require) {

function isNumber(value) {
value = (value === null ? NaN : +value);
return typeof value === 'number' && !isNaN(value);

@@ -11,0 +13,0 @@ }

4

test/config.js

@@ -7,5 +7,5 @@ require.config({

{
main: 'echarts-stat',
main: 'ecStat',
location: '../src',
name: 'echarts-stat'
name: 'ecStat'
},

@@ -12,0 +12,0 @@ {

src/statistic.js
src/statistic/max.js
src/statistic/mean.js
src/statistic/median.js
src/statistic/min.js
src/statistic/sum.js
test/clustering_animation.html

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test/clustering_bikmeans.html

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test/regression_exponential.html

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test/regression_linear.html

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test/regression_linearThroughOrigin.html

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test/regression_logarithmic.html

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test/regression-polynomial.html

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