拉丁超立方采样(Latin Hypercube Sampling, LHS)是一种统计采样技术,用于生成一组样本点,这些样本点在参数空间中均匀分布,并且满足每个维度只有一个样本点落在该维度的取值范围内。LHS常用于参数优化和模型验证等领域。在Python中,有几种常用的包可以进行拉丁超立方采样,下面介绍两种常用的方法。1. 使用
by Latin Hypercube Sampling, the optimal solutions found by IPOPT in each subspace are added to the sampling set. LHS is performed with the pyDOE (v0.3.8) package in python and R(R Core Team, 2016) package ‘lhs’(Carnell, 2016) via Python-R interface ‘RPy2’ (v2.8.5) in python....
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This is an implementation of Deutsch and Deutsch, "Latin hypercube sampling with multidimensional uniformity", Journal of Statistical Planning and Inference 142 (2012) , 763-772 python statistics python3 sampling latin-hypercube latin-hypercube-sampling Updated Aug 7, 2020 HTML ...