首先，我在1点上移动ramdom5() 6次，得到7个随机数。 其次，将7个数相加得到公和。 第三，除法的余数是7。 最后加1，得到从1到7的结果。 这个方法给出了在1到7的范围内获得数字的相等概率，除了1。1的概率略高。

public int random7(){
    Random random = new Random();
    //function (1 + random.nextInt(5)) is given
    int random1_5 = 1 + random.nextInt(5); // 1,2,3,4,5
    int random2_6 = 2 + random.nextInt(5); // 2,3,4,5,6
    int random3_7 = 3 + random.nextInt(5); // 3,4,5,6,7
    int random4_8 = 4 + random.nextInt(5); // 4,5,6,7,8
    int random5_9 = 5 + random.nextInt(5); // 5,6,7,8,9
    int random6_10 = 6 + random.nextInt(5); //6,7,8,9,10
    int random7_11 = 7 + random.nextInt(5); //7,8,9,10,11

    //sumOfRandoms is between 28 and 56
    int sumOfRandoms = random1_5 + random2_6 + random3_7 + 
                       random4_8 + random5_9 + random6_10 + random7_11;
    //result is number between 0 and 6, and
    //equals 0 if sumOfRandoms = 28 or 35 or 42 or 49 or 56 , 5 options
    //equals 1 if sumOfRandoms = 29 or 36 or 43 or 50, 4 options
    //equals 2 if sumOfRandoms = 30 or 37 or 44 or 51, 4 options
    //equals 3 if sumOfRandoms = 31 or 38 or 45 or 52, 4 options
    //equals 4 if sumOfRandoms = 32 or 39 or 46 or 53, 4 options
    //equals 5 if sumOfRandoms = 33 or 40 or 47 or 54, 4 options
    //equals 6 if sumOfRandoms = 34 or 41 or 48 or 55, 4 options
    //It means that the probabilities of getting numbers between 0 and 6 are almost equal.
    int result = sumOfRandoms % 7;
    //we should add 1 to move the interval [0,6] to the interval [1,7]
    return 1 + result;
}

该算法将rand5的调用次数减少到理论最小值7/5。通过产生接下来的5个rand7数字来调用它7次。

没有任何随机位的拒绝，也不可能一直等待结果。

#!/usr/bin/env ruby

# random integer from 1 to 5
def rand5
    STDERR.putc '.'
    1 + rand( 5 )
end

@bucket = 0
@bucket_size = 0

# random integer from 1 to 7
def rand7
    if @bucket_size == 0
        @bucket = 7.times.collect{ |d| rand5 * 5**d }.reduce( &:+ )
        @bucket_size = 5
    end

    next_rand7 = @bucket%7 + 1

    @bucket      /= 7
    @bucket_size -= 1

    return next_rand7
end

35.times.each{ putc rand7.to_s }

为什么不除以5再乘以7，然后四舍五入呢?(当然，你必须使用浮点数no.)

它比其他解决方案更简单、更可靠(真的吗?)例如，在Python中:

def ranndomNo7():
    import random
    rand5 = random.randint(4)    # Produces range: [0, 4]
    rand7 = int(rand5 / 5 * 7)   # /5, *7, +0.5 and floor()
    return rand7

这不是很容易吗?

这类似于@RobMcAfee，除了我使用魔术数字而不是2维数组。

int rand7() {
    int m = 1203068;
    int r = (m >> (rand5() - 1) * 5 + rand5() - 1) & 7;

    return (r > 0) ? r : rand7();
}

这里是我的一般实现，在给定一个范围为[0,B-1]的均匀发生器的情况下，生成范围为[0,N-1]的均匀。

public class RandomUnif {

    public static final int BASE_NUMBER = 5;

    private static Random rand = new Random();

    /** given generator, returns uniform integer in the range 0.. BASE_NUMBER-1
    public static int randomBASE() {
        return rand.nextInt(BASE_NUMBER);
    }

    /** returns uniform integer in the range 0..n-1 using randomBASE() */
    public static int randomUnif(int n) {
        int rand, factor;
        if( n <= 1 ) return 0;
        else if( n == BASE_NUMBER ) return randomBASE();
        if( n < BASE_NUMBER ) {
            factor = BASE_NUMBER / n;
            do
                rand = randomBASE() / factor;
            while(rand >= n);
            return rand;
        } else {
            factor = (n - 1) / BASE_NUMBER + 1;
            do {
                rand = factor * randomBASE() + randomUnif(factor);
            } while(rand >= n);
            return rand;
        }
    }
}

不是特别高效，但一般和紧凑。对基生成器的均值调用:

 n  calls
 2  1.250 
 3  1.644 
 4  1.252 
 5  1.000 
 6  3.763 
 7  3.185 
 8  2.821 
 9  2.495 
10  2.250 
11  3.646 
12  3.316 
13  3.060 
14  2.853 
15  2.650 
16  2.814 
17  2.644 
18  2.502 
19  2.361 
20  2.248 
21  2.382 
22  2.277 
23  2.175 
24  2.082 
25  2.000 
26  5.472 
27  5.280 
28  5.119 
29  4.899 

以下是我的发现:

Random5产生1~5的范围，随机分布 如果我们运行3次并将它们加在一起，我们将得到3~15个随机分布的范围 在3~15范围内执行算术 (3~15) - 1 = (2~14) (2~14)/2 = (1~7)

然后我们得到1~7的范围，这是我们正在寻找的Random7。