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朝樱

铁虫 (初入文坛)

[求助] 拉丁超立方试验设计

最优拉丁超立方试验设计应该怎么取样本点
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erickregal

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引用回帖:
2楼: Originally posted by dbb627 at 2013-08-28 09:14:22
matlab 中有个 lhsdesign,不知是否满足你的要求
LHSDESIGN Generate a latin hypercube sample.
    X=LHSDESIGN(N,P) generates a latin hypercube sample X containing N
    values on each of P variables ...

这个LHSDESIGN,怎么确定变量的水平呢。。。比如5<x1<7,10<x2<12,怎么把这个变量的范围加进去?
4楼2014-09-24 11:05:25
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dbb627

荣誉版主 (著名写手)

【答案】应助回帖

感谢参与,应助指数 +1
matlab 中有个 lhsdesign,不知是否满足你的要求
LHSDESIGN Generate a latin hypercube sample.
    X=LHSDESIGN(N,P) generates a latin hypercube sample X containing N
    values on each of P variables.  For each column, the N values are
    randomly distributed with one from each interval (0,1/N), (1/N,2/N),
    ..., (1-1/N,1), and they are randomly permuted.

    X=LHSDESIGN(...,'PARAM1',val1,'PARAM2',val2,...) specifies parameter
    name/value pairs to control the sample generation.  Valid parameters
    are the following:

       Parameter    Value
       'smooth'     'on' (the default) to produce points as above, or
                    'off' to produces points at the midpoints of
                    the above intervals:  .5/N, 1.5/N, ..., 1-.5/N.
       'iterations' The maximum number of iterations to perform in an
                    attempt to improve the design (default=5)
       'criterion'  The criterion to use to measure design improvement,
                    chosen from 'maximin' (the default) to maximize the
                    minimum distance between points, 'correlation' to
                    reduce correlation, or 'none' to do no iteration.

    Latin hypercube designs are useful when you need a sample that is
    random but that is guaranteed to be relatively uniformly distributed
    over each dimension.
The more you learn, the more you know, the more you know, and the more you forget. The more you forget, the less you know. So why bother to learn.
2楼2013-08-28 09:14:22
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lyh127

新虫 (初入文坛)

亲,你的拉丁超立方做的怎么样啦,我最近也在做这一块,相当茫然,求指教
3楼2014-06-12 15:48:07
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