生成非负随机数(Matlab) [英] Generate nonnegative random number ( matlab)

问题描述

我想在Matlab中生成一个N维列向量，平均值为0.5(可以调整方差)，但是我希望所有数字都是正数，有人知道怎么做吗?

I want to generate a N dimensional column vector in matlab, with mean 0.5 ( variance is ok to adjust ) , but I want all numbers to be positive, does anyone know how to do it?

推荐答案

这取决于您想要的分布. rand(v)函数在尺寸为v的数组中生成均匀的随机分布(我相信范围是[0,1]，尽管我不确定0或1在理论上是否是可能的值).

It depends on the distribution that you want. The rand(v) function generates a uniform random distribution (range [0,1] I believe although I'm not sure if either 0 or 1 are theoretically possible values) in an array with dimensions v.

因此，如果要使用3x4x5x6数组，则可以这样做

So if you want a 3x4x5x6 array, you would do

myRandArray = rand([3 4 5 6]);

如果您希望上限值更大，可以这样做

If you want the upper value to be larger, you could do

myRandArray = maxVal * rand([3 4 5 6]);

maxVal是最大值.而且，如果您希望范围从minVal到maxVal，请这样做

Where maxVal is the largest value. And if you want a range minVal to maxVal, then do

myRandArray = minVal + (maxVal - minVal) * rand([3 4 5 6]);

对于其他发行版(例如，对于正常发行版的randn)，显然可以对上述内容进行调整.如果您希望正态分布被截断"-您可能需要以太多的值开头:

For other distributions (like randn for normal distribution) you can make adjustments to the above, obviously. If you want a "truncated normal distribution" - you may need to start with too many values:

dims = [3 4 5 6];
n = prod( dims );
tooMany = 0.5 + randn(2 * n); % since you want mean = 0.5
tooMany(tooMany < 0) = [];
if numel( tooMany ) > n
  myRandArray = reshape(tooMany(1:n), dims);
end

您显然可以对此进行改进，但这是一个普遍的想法.

You can obviously improve on this, but it's a general idea.

例如，您可以仅生成n个值，查看有多少个失败(例如m)，然后生成另一个m * n/(n-m)，然后根据需要重复，直到精确到n.

For example, you could generate only n values, see how many fail (say m), then generate another m * n / (n - m), and repeat as needed until you have exactly n.

请注意，由于我们切除了尾巴，因此最终分布的均值不再是0.5.如果排除某些值，则正态分布"不能保持正态".但是然后您没有指定您想要的分布...

Note that the mean of the final distribution is no longer 0.5 since we cut off the tail. A 'normal distribution' cannot remain 'normal' if you exclude certain values. But then you didn't specify what distribution you wanted...

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