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pyDOE2包pyDOE2...
Extension of Latin hypercube samples with correlated variables A procedure for extending the size of a Latin hypercube sample (LHS) with rank correlated variables is described and illustrated. The extension procedure s... CJ Sallaberry,JC Helton,SC Hora - 《Reliability Engineering & System Safety》...
X= lhsnorm(mu,sigma,n)returns a numeric matrixXcontaining a Latin hypercube sample of sizenfrom a multivariate normal distribution with mean vectormuand covariance matrixsigma. The size ofXisn-by-d, wheredis the size ofmu.Xis similar to a random sample generated from the multivariate normal ...
In this paper we propose and discuss a new algorithm to build a Latin hypercube sample (LHS) taking into account inequality constraints between the sampled variables. This technique, called constrained Latin hypercube sampling (cLHS), consists in doing permutations on an initial LHS to honor the ...
Given an established probabilistic description of hydraulic conductivity, realizations of the hydraulic conductivity field can be generated using a lattice sampling technique, a special case of Latin hypercube sampling, and subsequently input to a groundwater flow and transport model. Realizations of depend...
print(binary_sample) 在这个改写后的代码中,latin_hypercube_sampling函数使用了最大最小思想进行优化的拉丁超立方采样,生成了指定数量和维度的采样点。variable_to_binary_array函数将每个变量转换为指定位数的二进制数组。然后,我们可以使用生成的采样点进行后续操作。
rr-packagelhslatin-hypercubeorthogonal-arrayslatin-hypercube-samplinglatin-hypercube-sample UpdatedJun 30, 2024 C++ tstran155/Optimization-of-building-energy-consumption Star23 This repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate...
Sample Latin Properties Hypercube of Simulations Using SamplingStein, Michael
Latin hypercube sampling (LHS) uses a stratified sampling scheme to improve on the coverage of the k-dimensional input space for such computer models. This means that a single sample will provide useful information when some input variable(s) dominate certain responses (or certain time intervals)...
Results showed that Latin hypercube sampling can capture more variability in the sample space than simple random sampling especially when the number of simulations is small. Application results showed that LANDIS simulation results at the landscape level (species percent area and their spatial pattern ...