我正在使用HighCharts构建不规则的时间图,目前看起来像这样:
我想知道是否有可能为三条(或未来可能更多)的线创建一条“平均”线。
它将开始跟随蓝线,然后接近1月中旬的绿线等等。
目前我正在处理的代码如下:
$('#chart').highcharts({
chart: { type: 'spline' },
title: { text: '' },
xAxis: { type: 'datetime' },
yAxis: {
title: { text: '' }
}
series: [{
name: 'Line 1',
data: [
[Date.UTC(2014,0,16), 173.33],
[Date.UTC(2014,0,23), 163.33],
[Date.UTC(2014,0,30), 137.67],
[Date.UTC(2014,1,6), 176.33],
[Date.UTC(2014,1,13), 178.67],
[Date.UTC(2014,1,27), 167.33],
],
color: 'purple'
},
{
name: 'Line 2',
data: [
[Date.UTC(2014,0,11), 156.33],
[Date.UTC(2014,1,15), 167.67],
],
color: 'green'
},
{
name: 'Line 3',
data: [
[Date.UTC(2014,0,1), 135],
[Date.UTC(2014,0,5), 146.33],
[Date.UTC(2014,0,27), 146.75],
],
color: 'blue'
}]
});
答案 0 :(得分:7)
您所描述的内容称为trend or regression line。 Highcharts没有内置的添加这些线条的能力,但数学并不太难(而且,自己做的更有趣)。我用最小二乘的线性回归编写了最简单的例子。
/////////////////////
//utility functions//
////////////////////
// linear regression
// given array of x values and array of y values
// returns rV object with slope/intercept
lineFit = function(xs, ys, rV){
rV.slope = 0.0;
rV.intercept = 0.0;
rV.rSquared = 1.0; // assume perfection
if (xs.length < 2)
{
return false;
}
if (xs.Count != ys.Count)
{
return false;
}
var N = xs.length;
var sumX = sumFunc(xs,null);
var sumY = sumFunc(ys,null);
var funcSq = function(i){return (i*i);}
var funcSq2 = function(i,j){return (i*j);}
var sumXx = sumFunc(xs, funcSq);
var sumYy = sumFunc(ys, funcSq);
var sumXy = sumFunc(zip(xs,ys),funcSq2);
rV.slope = ((N * sumXy) - (sumX * sumY)) / (N * sumXx - (sumX*sumX));
rV.intercept = (sumY - rV.slope * sumX) / N;
rV.rSquared = Math.abs((rV.slope * (sumXy - (sumX * sumY) / N)) / (sumYy - ((sumY * sumY) / N)));
return true;
}
// sums arrays with optional function transformation
sumFunc = function(arr, func){
var total = 0;
$.each(arr, function(i,k){
if ($.isArray(k)){
if (func == null){
k = k[0] + k[1];
}else{
k = func(k[0],k[1]);
}
} else {
if (func != null){
k = func(k);
}
}
total += k;
});
return total;
}
// python style zip function
// to pair to array together
zip = function(arr1,arr2) {
var rV = [];
for(var i=0; i<arr1.length; i++){
rV.push([arr1[i],arr2[i]]);
}
return rV;
}
lineFit函数将返回具有斜率和截距属性的rV对象(通过引用)。之后,您可以使用旧式y = slope * x + intercept
向Highcharts添加一行,minX是回归线的起始值,maxX是结束值。
{
name: 'Regression Line',
data: [[minX, reg.slope * minX + reg.intercept],
[maxX, reg.slope * maxX + reg.intercept]],
color: 'red',
marker:{enabled:false},
lineWidth: 5
}
工作fiddle here。
答案 1 :(得分:2)
根据Mark的回答提供的想法,我编写了一些代码来生成自定义的第四行,使用来自所有三行的数据,并计算每个点所需的值。
我的新代码如下:
line1 = [
[Date.UTC(2014,0,16), 173.33],
[Date.UTC(2014,0,23), 163.33],
[Date.UTC(2014,0,30), 137.67],
[Date.UTC(2014,1,6), 176.33],
[Date.UTC(2014,1,13), 178.67],
[Date.UTC(2014,1,27), 167.33],
];
line2 = [
[Date.UTC(2014,0,11), 156.33],
[Date.UTC(2014,1,15), 167.67],
];
line3 = [
[Date.UTC(2014,0,1), 135],
[Date.UTC(2014,0,5), 146.33],
[Date.UTC(2014,0,27), 146.75],
[Date.UTC(2014,2,2), 168.75]
];
function average(array, index) {
sum = array[0][1];
for(var i = 1; i <= index; i++) {
sum += array[i][1];
}
value = sum / (index + 1);
return parseFloat(value.toFixed(2));
}
// Make a fourth line with all of the data points for the other
// three lines, sorted by date
all_lines = line1.concat(line2).concat(line3);
all_lines.sort(function(a, b) { return a[0] - b[0]});
// Calculate the value for each data point in the fourth line -
// the average of all the values before it
average_line = [];
for(var i = 0; i < all_lines.length; i++) {
average_line.push([all_lines[i][0], average(all_lines, i)])
}
$('#chart').highcharts({
chart: { type: 'spline' },
title: {
text: '',
},
xAxis: {
type: 'datetime'
},
yAxis: {
title: {
text: ''
}
},
legend: {
layout: 'vertical',
align: 'right',
verticalAlign: 'middle',
borderWidth: 0
},
series: [{
name: 'Line 1',
data: line1,
color: 'purple'
},
{
name: 'Line 2',
data: line2,
color: 'green'
},
{
name: 'Line 3',
data: line3,
color: 'blue'
},
{
name: 'Average',
data: average_line,
color: 'red'
}]
});
现在看起来的图表(蓝线上有一个额外的数据点)是: