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

Name: sahilm89/lhsmdu: Latin Hypercube Sampling with Multi-Dimensional Uniformity (LHSMDU): Speed Boost minor compatibility fixes
Creator: Sahil Moza
Keywords: Latin hypercube sampling, multi dimensional sampling, uniform sampling

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 03 Jul 2020Publisher:Zenodo

Authors: Sahil Moza;

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

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

- Summary
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- Related research
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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.

Related Organizations

National Center for Biological Sciences (NCBS), Bangalore, India
India

Keywords

Latin hypercube sampling, multi dimensional sampling, uniform sampling

1 Research products, page 1 of 1

sahilm89/lhsmdu: First release for this code
2019HasVersion

Impact byBIP!

	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).	7
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%