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sahilm89/lhsmdu: Latin Hypercube Sampling with Multi-Dimensional Uniformity (LHSMDU): Speed Boost minor compatibility fixes

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 03 Jul 2020Publisher:Zenodo
Authors: Moza, Sahil;

doi: 10.5281/zenodo.2578780 , 10.5281/zenodo.3929531

sahilm89/lhsmdu: Latin Hypercube Sampling with Multi-Dimensional Uniformity (LHSMDU): Speed Boost minor compatibility fixes

Abstract

1.1.1 Small compatibility fixes between Python 2 and 3. 1.1.0 The lhsmdu library is now much faster (e.g.15-20 times faster for 500 samples). The code has been refactored and optimized for speed. No backward-compatibility problems expected, and no other functionality has been added in this release.

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Keywords

Latin hypercube sampling, multi dimensional sampling, uniform sampling

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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