cLHS: Conditioned Latin Hypercube Sampling
Conditioned Latin Hypercube Sampling in Python.
This code is based on the conditioned LHS method of Minasny & McBratney (2006). It follows some of the code from the R package clhs of Roudier et al.
In short, this code attempts to create a Latin Hypercube sample by selecting only from input data. It uses simulated annealing to force the sampling to converge more rapidly, and also allows for setting a stopping criterion on the objective function described in Minasny & McBratney (2006).
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Release files for clhs 1.0.2
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Release files / clhs-1.0.2.tar.gz
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