论文中的概率密度函数,使用 C++ 实现,未按预期工作

发布于 2024-10-01 10:27:36 字数 1555 浏览 6 评论 0原文

所以我正在实现一个启发式算法,并且我遇到了这个函数。

我有一个 1 到 n 的数组(C 上的 0 到 n-1,w/e)。我想选择一些要复制到另一个数组的元素。给定参数 y (0 < y <= 1),我希望得到平均值为 (y * n) 的数字分布。这意味着每当我调用这个函数时,它都会给我一个介于 0 和 n 之间的数字,这些数字的平均值是 y*n。

根据作者的说法,“l”是一个随机数:0 << l <名词在我的测试代码中,它当前生成 0 <= l <= n。我有正确的代码,但我已经搞乱了几个小时了,而且我懒得把它编码回来。

所以我编写了函数的第一部分,对于 y <= 0.5 我将 y 设置为 0.2,n 设置为 100。这意味着它必须返回 0 到 99 之间的数字,平均为 20。 而且结果不是在 0 到 n 之间,而是一些浮点数。 n 越大,这个浮点数就越小。

这是C测试代码。 “x”是“l”参数。

//hate how code tag works, it's not even working now  
int n = 100;  
float y = 0.2;  
float n_copy;  

for(int i = 0 ; i < 20 ; i++)  
{  
    float x = (float) (rand()/(float)RAND_MAX);  // 0 <= x <= 1  
    x = x * n;                                // 0 <= x <= n  
    float p1 = (1 - y) / (n*y);  
    float p2 = (1 - ( x / n ));  
    float exp = (1 - (2*y)) / y;  
    p2 = pow(p2, exp);  
    n_copy = p1 * p2;  
    printf("%.5f\n", n_copy);  
}  

以下是一些结果(截断 5 位小数):

0.03354  
0.00484  
0.00003  
0.00029  
0.00020  
0.00028  
0.00263  
0.01619  
0.00032  
0.00000  
0.03598  
0.03975    
0.00704  
0.00176  
0.00001  
0.01333  
0.03396   
0.02795  
0.00005  
0.00860 

文章为:

http://www.scribd.com/doc/3097936/cAS-The-Cunning-Ant-System

第6页和第7页。

或在谷歌上搜索“cAS:cunning ant system”。

那么我做错了什么?我不认为作者是错的,因为有超过 5 篇论文描述了这个相同的功能。

我所有的互联网给任何帮助我的人。这对我的工作很重要。

谢谢 :)

So i'm implementing a heuristic algorithm, and i've come across this function.

I have an array of 1 to n (0 to n-1 on C, w/e). I want to choose a number of elements i'll copy to another array. Given a parameter y, (0 < y <= 1), i want to have a distribution of numbers whose average is (y * n). That means that whenever i call this function, it gives me a number, between 0 and n, and the average of these numbers is y*n.

According to the author, "l" is a random number: 0 < l < n . On my test code its currently generating 0 <= l <= n. And i had the right code, but i'm messing with this for hours now, and i'm lazy to code it back.

So i coded the first part of the function, for y <= 0.5
I set y to 0.2, and n to 100. That means it had to return a number between 0 and 99, with average 20.
And the results aren't between 0 and n, but some floats. And the bigger n is, smaller this float is.

This is the C test code. "x" is the "l" parameter.

//hate how code tag works, it's not even working now  
int n = 100;  
float y = 0.2;  
float n_copy;  

for(int i = 0 ; i < 20 ; i++)  
{  
    float x = (float) (rand()/(float)RAND_MAX);  // 0 <= x <= 1  
    x = x * n;                                // 0 <= x <= n  
    float p1 = (1 - y) / (n*y);  
    float p2 = (1 - ( x / n ));  
    float exp = (1 - (2*y)) / y;  
    p2 = pow(p2, exp);  
    n_copy = p1 * p2;  
    printf("%.5f\n", n_copy);  
}  

And here are some results (5 decimals truncated):

0.03354  
0.00484  
0.00003  
0.00029  
0.00020  
0.00028  
0.00263  
0.01619  
0.00032  
0.00000  
0.03598  
0.03975    
0.00704  
0.00176  
0.00001  
0.01333  
0.03396   
0.02795  
0.00005  
0.00860 

The article is:

http://www.scribd.com/doc/3097936/cAS-The-Cunning-Ant-System

pages 6 and 7.

or search "cAS: cunning ant system" on google.

So what am i doing wrong? i don't believe the author is wrong, because there are more than 5 papers describing this same function.

all my internets to whoever helps me. This is important to my work.

Thanks :)

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薄情伤 2024-10-08 10:27:36

您可能会误解对您的期望。

给定一个(适当归一化的)PDF,并且想要抛出与其一致的随机分布,您可以通过积分 PDF 来形成累积概率分布 (CDF),然后反转 CDF,并使用均匀随机谓词作为反转的参数功能。


更详细一点。

f_s(l) 是 PDF,并已在 [0,n) 上标准化。

现在您将其积分以形成 CDF

g_s(l') = \int_0^{l'} dl f_s(l)

请注意,这是对未指定端点的定积分,我将其称为 l'。因此CDF是l'的函数。假设我们有正确的标准化,g_s(N) = 1.0。如果不是这样,我们应用一个简单的系数来修复它。

接下来反转 CDF 并调用结果 G^{-1}(x)。为此,您可能需要选择特定的伽玛值。

然后在 [0,n) 上抛出统一随机数,并将它们用作 G^{-1} 的参数 x。结果应位于[0,1)之间,并应根据f_s分布。

就像贾斯汀说的,你可以使用计算机代数系统来进行数学计算。

You may misunderstand what is expected of you.

Given a (properly normalized) PDF, and wanting to throw a random distribution consistent with it, you form the Cumulative Probability Distribution (CDF) by integrating the PDF, then invert the CDF, and use a uniform random predicate as the argument of the inverted function.


A little more detail.

f_s(l) is the PDF, and has been normalized on [0,n).

Now you integrate it to form the CDF

g_s(l') = \int_0^{l'} dl f_s(l)

Note that this is a definite integral to an unspecified endpoint which I have called l'. The CDF is accordingly a function of l'. Assuming we have the normalization right, g_s(N) = 1.0. If this is not so we apply a simple coefficient to fix it.

Next invert the CDF and call the result G^{-1}(x). For this you'll probably want to choose a particular value of gamma.

Then throw uniform random number on [0,n), and use those as the argument, x, to G^{-1}. The result should lie between [0,1), and should be distributed according to f_s.

Like Justin said, you can use a computer algebra system for the math.

素手挽清风 2024-10-08 10:27:36

dmckee 实际上是正确的,但我想我应该详细说明一下,并尝试解释这里的一些混乱。我肯定会失败。 f_s(l),上面漂亮公式中的函数,是概率分布函数。它告诉您,对于 0 到 n 之间的给定输入 ll 是段长度的概率。 0 到 n 之间所有值的总和(积分)应等于 1。

第 7 页顶部的图表混淆了这一点。它绘制了 lf_s(l) 的关系图,但您必须注意它放在一边的杂散因素。您注意到底部的值从 0 到 1,但侧面有一个 x n 因子,这意味着 l 值实际上从 0 到名词另外,y 轴上有一个 x 1/n,这意味着这些值实际上不会达到 3 左右,而是达到 3/n。

那你现在做什么?好吧,您需要通过在 l 上积分概率分布函数来求解累积分布函数,这实际上证明并不太糟糕(我通过使用 x 来使用 Wolfram Mathematica Online Integrator 来实现< code>l 并仅使用 y <= .5 的方程)。然而,这是使用不定积分,并且您实际上是沿着 x 从 0 到 l 进行积分。如果我们将所得方程设置为等于某个变量(例如 z),那么现在的目标是求解作为 z 函数的 l。 z 这里是 0 到 1 之间的随机数。如果您愿意(我愿意),您可以尝试在这部分使用符号求解器。那么您不仅实现了能够从该分布中随机选择 l 的目标,而且还实现了涅槃。

完成更多工作

我会提供更多帮助。我尝试按照我所说的 y <= .5 进行操作,但是我使用的符号代数系统无法进行反转(其他一些系统可能可以)。然而,后来我决定尝试使用 0.5 < 的方程。 y <= 1。事实证明这要容易得多。如果我将 f_s(l) 中的 l 更改为 x,我得到

y / n / (1 - y) * (x / n)^((2 * y - 1) / (1 - y))

Integrating this over x from 0 to l 我得到(使用 Mathematica 的在线积分器) :

(l / n)^(y / (1 - y))

对于这种事情来说,没有比这更好的了。如果我将其设置为等于 z 并求解 l,我会得到:

l = n * z^(1 / y - 1)      for .5 < y <= 1

一次快速检查是 y = 1。在这种情况下,无论 z 是什么,我们都会得到 l = n是。到目前为止,一切都很好。现在,您只需生成 z(0 到 1 之间的随机数),就会得到一个 l,它按照您的需要进行分布,即 0.5 l。 y <= 1。但是等等,查看第 7 页的图表,您会注意到概率分布函数是对称的。这意味着我们可以使用上面的结果来找到 0 << 的值。 y <= .5。我们只需更改 l -> nly -> 1-y 并得到

n - l = n * z^(1 / (1 - y) - 1)

l = n * (1 - z^(1 / (1 - y) - 1))      for 0 < y <= .5

无论如何,这应该可以解决你的问题,除非我在某个地方犯了一些错误。祝你好运。

dmckee is actually correct, but I thought that I would elaborate more and try to explain away some of the confusion here. I could definitely fail. f_s(l), the function you have in your pretty formula above, is the probability distribution function. It tells you, for a given input l between 0 and n, the probability that l is the segment length. The sum (integral) for all values between 0 and n should be equal to 1.

The graph at the top of page 7 confuses this point. It plots l vs. f_s(l), but you have to watch out for the stray factors it puts on the side. You notice that the values on the bottom go from 0 to 1, but there is a factor of x n on the side, which means that the l values actually go from 0 to n. Also, on the y-axis there is a x 1/n which means these values don't actually go up to about 3, they go to 3/n.

So what do you do now? Well, you need to solve for the cumulative distribution function by integrating the probability distribution function over l which actually turns out to be not too bad (I did it with the Wolfram Mathematica Online Integrator by using x for l and using only the equation for y <= .5). That however was using an indefinite integral and you are really integration along x from 0 to l. If we set the resulting equation equal to some variable (z for instance), the goal now is to solve for l as a function of z. z here is a random number between 0 and 1. You can try using a symbolic solver for this part if you would like (I would). Then you have not only achieved your goal of being able to pick random ls from this distribution, you have also achieved nirvana.

A little more work done

I'll help a little bit more. I tried doing what I said about for y <= .5, but the symbolic algebra system I was using wasn't able to do the inversion (some other system might be able to). However, then I decided to try using the equation for .5 < y <= 1. This turns out to be much easier. If I change l to x in f_s(l) I get

y / n / (1 - y) * (x / n)^((2 * y - 1) / (1 - y))

Integrating this over x from 0 to l I got (using Mathematica's Online Integrator):

(l / n)^(y / (1 - y))

It doesn't get much nicer than that with this sort of thing. If I set this equal to z and solve for l I get:

l = n * z^(1 / y - 1)      for .5 < y <= 1

One quick check is for y = 1. In this case, we get l = n no matter what z is. So far so good. Now, you just generate z (a random number between 0 and 1) and you get an l that is distributed as you desired for .5 < y <= 1. But wait, looking at the graph on page 7 you notice that the probability distribution function is symmetric. That means that we can use the above result to find the value for 0 < y <= .5. We just change l -> n-l and y -> 1-y and get

n - l = n * z^(1 / (1 - y) - 1)

l = n * (1 - z^(1 / (1 - y) - 1))      for 0 < y <= .5

Anyway, that should solve your problem unless I made some error somewhere. Good luck.

小忆控 2024-10-08 10:27:36

鉴于对于所描述的任何值 l、y、n,您称为 p1 和 p2 的术语都在 [0,1) 中,而 exp 在 [1,..) 中,使得 pow(p2, exp) 也在 [0,1) 中1)因此我不明白你如何获得范围为 [0,n) 的输出

Given that for any values l, y, n as described, the terms you call p1 and p2 are both in [0,1) and exp is in [1,..) making pow(p2, exp) also in [0,1) thus I don't see how you'd ever get an output with the range [0,n)

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