这篇文章主要介绍了基于JavaScript伪随机正态分布代码实例,文中通过示例代码介绍的非常详
这篇文章主要介绍了基于JavaScript伪随机正态分布代码实例,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下
在游戏开发中经常遇到随机奖励的情况，一般会采取先生成数组，再一个一个取的方式发随机奖励。
下面是js测试正态分布代码：
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title></title>
</head>
<body>
<canvas id="myCanvas" width="800" height="400" style="border:1px solid #c3c3c3;">
Your browser does not support the canvas element.
</canvas>
<canvas id="myCanvas2" width="800" height="400" style="border:1px solid #c3c3c3;">
Your browser does not support the canvas element.
</canvas>
<textarea id="text" cols="200" rows="5000"></textarea>
<script type="text/javascript">
var timesArr = [];
var timesArrObj = {};
window.onload = function () {
// for (var meter = 0; meter < 800; meter++) {
// var times = getNumberInNormalDistribution(20, 7);
// addPoint(times, meter);
// timesArr.push(Math.floor(times));
// }
// drawGreenTab(timesArr,1);
// drawLine(0, 380, 800, 380);
drawResult(1);
drawResult(2);
drawResult(0);
drawGreenTab(timesArrObj[1], 1);
drawGreenTab(timesArrObj[2], 2);
drawGreenTab(timesArrObj[0], 0);
}
//制作绿色柱状图表
function drawGreenTab(timesArr, color) {
var timesTypes = {};
for (var i in timesArr) {
var times = timesArr[i];
if (timesTypes[times] == null) {
timesTypes[times] = 0;
} else {
timesTypes[times] = timesTypes[times] + 1;
}
}
for (var i in timesTypes) {
drawRect(i, timesTypes[i], 4, color);
}
}
//画圆点
function addPoint(y, x, color) {
y = 400 - y;
var c = document.getElementById("myCanvas");
var cxt = c.getContext("2d");
if (color == null) {
cxt.fillStyle = "#FF0000";
} else {
cxt.fillStyle = color;
}
cxt.beginPath();
cxt.arc(x, y, 2, 0, Math.PI * 2, true);
cxt.closePath();
cxt.fill();
}
var meter = 0;
//划线
function drawLine(beginx, beginy, endx, endy) {
var c = document.getElementById("myCanvas");
var cxt = c.getContext("2d");
cxt.moveTo(beginx, beginy);
cxt.lineTo(endx, endy);
cxt.stroke();
}
//模拟正态分布取值
function getNumberInNormalDistribution(mean, std_dev) {
return mean + (uniform2NormalDistribution() * std_dev);
}
//模拟正态分布偏差
function uniform2NormalDistribution() {
var sum = 0.0;
for (var i = 0; i < 12; i++) {
sum = sum + Math.random();
}
return sum - 6;
}
//画一个长方形
function drawRect(x, y, width, index) {
var color = "#FF0000";
if (index == 1) {
color = "#00FF00";
} else if (index == 2) {
color = "#0000FF";
}
var c = document.getElementById("myCanvas2");
var cxt = c.getContext("2d");
cxt.fillStyle = color;
cxt.fillRect(x * width + index * 200, 400 - y, width - 2, y);
}
//画出生成的图像
function drawResult(index) {
var color = "#FF0000";
if (index % 3 == 1) {
color = "#00FF00";
} else if (index % 3 == 2) {
color = "#0000FF";
}
var result = generateList();
var resultStr = "";
// for (var i in result) {
// resultStr = resultStr + result[i] + "\n";
// }
//document.getElementById("text").value = resultStr;
var resulttimes = {};
for (var i in result) {
if (resulttimes[result[i]] == null) {
resulttimes[result[i]] = 1;
} else {
resulttimes[result[i]] = resulttimes[result[i]] + 1;
}
}
for (var i in resulttimes) {
resultStr = resultStr + resulttimes[i] + "\n";
}
document.getElementById("text").value = resultStr;
var timeslist = [];
var times = 1;
for (var i in result) {
if (result[i] == index) {
addPoint(times, i / 5, color);
if (timesArrObj[index] == null) {
timesArrObj[index] = [];
}
timesArrObj[index].push(times);
times = 0;
} else {
times++;
}
}
}
//权重数组
var wt = [105, 216, 316, 488, 1000, 2000, 3680, 5890];//,14770,71535
//生成结果数组函数，结果为权重数组的索引，从0开始
function generateList() {
//生成的结果数组长度
var n = 50000;
var wtp = [];
var sum = 0;
for (var i in wt) {
sum = sum + wt[i];
}
for (var i in wt) {
wtp.push(wt[i] / sum);
}
var result = [];
var p = [];
for (var i in wtp) {
var inp = getNumberInNormalDistribution(1 / wtp[i], 1 / wtp[i] / 3);
p.push(inp);
}
for (var i = 0; i < n; i++) {
var minp = 99999999;
var minj = -1;
for (var j in p) {
if (p[j] < minp) {
minp = p[j];
minj = j;
}
}
result.push(minj);
for (var j in p) {
p[j] = p[j] - minp;
}
p[minj] = getNumberInNormalDistribution(1 / wtp[minj], 1 / wtp[minj] / 3);
}
return result;
}
</script>
</body>
</html>以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。
javascript 伪随机 正态分布