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c#获取相同概率随机数的算法代码

程序员文章站 2023-12-21 13:32:46
这几天在做公司年会的一个抽奖软件,开始做的的时候,认为算法是很简单的,把员工的数据放进list里,把list的标号作为需要获取的随机数,根据得到的随机数就确定是谁中奖。后来...

这几天在做公司年会的一个抽奖软件,开始做的的时候,认为算法是很简单的,把员工的数据放进list里,把list的标号作为需要获取的随机数,根据得到的随机数就确定是谁中奖。后来测试发现,随机数的分布是非常不均匀的。后来才知道,原来计算机获取的随机数都是伪随机数,当抽奖的速度非常快的时候,获取的随机数是非常不均匀的,所以在每次抽奖的时候要添加延时。后来重新设计算法,最终实现了。

算法原理跟二分查找的过程有点像。一枚硬币抽中正、反面的概率是一样,当抽样的次数无限增多,抽中的概率是50%。

代码如下:

复制代码 代码如下:

public partial class mainwindow : window
    {
        string s;
        int number;
        public mainwindow()
        {
            initializecomponent();
        }
        public int getrandom()
        {
            //string[] arr = new string[5] { "我们", "是", "一", "个","团队" };

            random r = new random();
            int num = 2;
            int choose = r.next(num);
            return choose;
            //messagebox.show(arr[choose].tostring());
        }
        public string grandom(int n)
        {
            //if()
            if (n == 0)
            {
                //s = getrandom() + s;
                //system.threading.thread.sleep(1);
                return s;
            }
            if (n % 2 == 0)
            {
                n = n / 2;

            }
            else
            {
                n = (n - 1) / 2;
                //s = getrandom() + s;
            }
            s = getrandom() + s;
            system.threading.thread.sleep(20);
            grandom(n);
            //system.threading.thread.sleep(1);
            return s;
        }
        public int32 estimate(int n)
        {
            string num = grandom(n);
            number = convert.toint32(num, 2);
            if (number > n - 1)
            {
                //num = "";
                s = "";
                estimate(n);
            }
            //else
            return number;
        }
        private void button_click(object sender, routedeventargs e)
        {
            for (int i = 0; i < 100; i++)
            {
                label1.content += estimate(200) + ";";
                s = "";
            }
        }
    }

以上算法不是非常好,取消延时,将random对象设置为全局变量。修改版代码如下:

复制代码 代码如下:

string s;
        int number;
        random r = new random();

        public int getrandom()
        {
            //string[] arr = new string[5] { "我们", "是", "一", "个","团队" };

            //random r = new random();
            int num = 2;
            int choose = r.next(num);
            return choose;
            //messagebox.show(arr[choose].tostring());
        }
        public string grandom(int n)
        {
            //if()
            if (n == 0)
            {
                //s = getrandom() + s;
                //system.threading.thread.sleep(1);
                return s;
            }
            if (n % 2 == 0)
            {
                n = n / 2;

            }
            else
            {
                n = (n - 1) / 2;
                //s = getrandom() + s;
            }
            s = getrandom() + s;
            grandom(n);

            return s;
        }
        public int32 estimate(int n)
        {
            string num = grandom(n);
            number = convert.toint32(num, 2);
            if (number > n - 1)
            {
                //num = "";
                s = "";
                estimate(n);
            }
            //else
            return number;
        }
        private void button_click(object sender, routedeventargs e)
        {
            for (int i = 0; i < 1000; i++)
            {
                label1.content = estimate(200);
                s = "";
            }

        //以下为测试
            //int a = 0, b = 0, c = 0, d = 0, f = 0;
            //for (int i = 0; i < 1000; i++)
            //{
            //    //label1.content = estimate(2);
            //    int content = estimate(5);
            //    s = "";

            //    switch (content)
            //    {
            //        case 0:
            //            a ++;
            //            break;
            //        case 1:
            //            b ++;
            //            break;
            //        case 2:
            //            c ++;
            //            break;
            //        case 3:
            //            d ++;
            //            break;
            //        case 4:
            //            f ++;
            //            break;

            //    }
            //    label1.content = a;
            //    label2.content = b;
            //    label3.content = c;
            //    label4.content = d;
            //    label5.content = f;
            //}
        }
    }
}

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