http://blog.sina.com.cn/s/blog_76c31b8e0100qskf.html

在編程過程中，由于數(shù)據(jù)仿真模擬的需要，我們經(jīng)常需要產(chǎn)生一些隨機(jī)數(shù)，在C#中，產(chǎn)生一般隨機(jī)數(shù)用Random即可，但是，若要產(chǎn)生服從特定分布的隨機(jī)數(shù)，就需要一定的算法來支持了，為了方便廣大編程人員，我將我再做項(xiàng)目過程中用到的產(chǎn)生服從正態(tài)分布、泊松分布、指數(shù)分布以及負(fù)指數(shù)分布隨機(jī)數(shù)的方法與大家共享，希望會(huì)有所幫助！

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.IO;

namespace PZ
{
????class Model
????{
????????/// <summary>
????????/// 正態(tài)分布隨機(jī)數(shù)
????????/// </summary>
????????const int N = 100;
????????const int MAX = 50;
????????const double MIN = 0.1;
????????const int MIU = 40;
????????const int SIGMA = 1;
????????static Random aa = new Random((int)(DateTime.Now.Ticks / 10000));
????????public double AverageRandom(double min, double max)//產(chǎn)生(min,max)之間均勻分布的隨機(jī)數(shù)
????????{
????????????int MINnteger = (int)(min * 10000);
????????????int MAXnteger = (int)(max * 10000);
????????????int resultInteger = aa.Next(MINnteger, MAXnteger);
????????????return resultInteger / 10000.0;
????????}
????????public double Normal(double x, double miu, double sigma) //正態(tài)分布概率密度函數(shù)
????????{
????????????return 1.0 / (x * Math.Sqrt(2 * Math.PI) * sigma) * Math.Exp(-1 * (Math.Log(x) - miu) * (Math.Log(x) - miu) / (2 * sigma * sigma));
????????}
????????public double Random_Normal(double miu, double sigma, double min, double max)//產(chǎn)生正態(tài)分布隨機(jī)數(shù)
????????{
????????????double x;
????????????double dScope;
????????????double y;
????????????do
????????????{
????????????????x = AverageRandom(min, max);
????????????????y = Normal(x, miu, sigma);
????????????????dScope = AverageRandom(0, Normal(miu, miu, sigma));
????????????} while (dScope > y);
????????????return x;
????????}

????????/// <summary>
????????/// 指數(shù)分布隨機(jī)數(shù)
????????/// </summary>
????????/// <param name="const_a"></param>
????????/// <returns></returns>
????????public??double RandExp(double const_a)//const_a是指數(shù)分布的參數(shù)λ
????????{
????????????Random rand = new Random(Guid.NewGuid().GetHashCode());
????????????double p;
????????????double temp;
????????????if (const_a != 0)
????????????????temp = 1 / const_a;
????????????else
????????????????throw new System.InvalidOperationException("除數(shù)不能為零！不能產(chǎn)生參數(shù)為零的指數(shù)分布！");
????????????double randres;
????????????while (true) //用于產(chǎn)生隨機(jī)的密度，保證比參數(shù)λ小
????????????{
????????????????p = rand.NextDouble();
????????????????if (p < const_a)
????????????????????break;
????????????}
????????????randres = -temp * Math.Log(temp * p, Math.E);
????????????return randres;
????????}

?

????????/// <summary>
????????/// 負(fù)指數(shù)分布隨機(jī)數(shù)產(chǎn)生
????????/// </summary>
????????/// <param name="lam">參數(shù)</param>
????????/// <returns></returns>
????????Random ran;

????????public Model()
????????{
????????????ran = new Random();
????????}
????????public double ngtIndex(double lam)
????????{
????????????double dec = ran.NextDouble();
????????????while (dec == 0)
????????????????dec = ran.NextDouble();
????????????return -Math.Log(dec) / lam;
????????}

?

????????/// <summary>
????????/// 泊松分布產(chǎn)生
????????/// </summary>
????????/// <param name="lam">參數(shù)</param>
????????/// <param name="time">時(shí)間</param>
????????/// <returns></returns>
????????public double poisson(double lam, double time)
????????{
????????????int count = 0;

????????????while (true)
????????????{
????????????????time -= ngtIndex(lam);
????????????????if (time > 0)
????????????????????count++;
????????????????else
????????????????????break;
????????????}
????????????return count;
????????}
?
????????}

????}

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