Latin Hypercubic sampling¶
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class
stats_arrays.
LatinHypercubeRNG
(params, seed=None, samples=10, **kwargs)¶ A random number generator that pre-calculates a sample space to draw from.
Inputs
- params : A Parameter array which gives parameters for distributions (one distribution per row).
- seed : An integer (or array of integers) to seed the NumPy random number generator.
- samples : An integer number of samples to construct for each distribution.
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__init__
(params, seed=None, samples=10, **kwargs)¶ x.__init__(…) initializes x; see help(type(x)) for signature
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build_hypercube
()¶ Build an array, of shape self.length rows by self.samples columns, which contains the sample space to be drawn from when doing Latin Hypercubic sampling.
Each row represents a different data point and distribution. The final sample space is self.hypercube. All distributions from uncertainty_choices are usable, and bounded distributions are also fine.
Builds
self.hypercube : Numpy array with dimensions self.length by self.samples.
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next
()¶ Draw directly from pre-computed sample space.