Description
===========
diversipy is a collection of algorithms dealing with three different but related
topics. The first topic is super-uniform sampling of the unit hypercube.
‘Super-uniform’ in this context means that the obtained point sample should be
more uniform than a random uniform sample, which is a desirable property in many
applications. One such application is the design of computer experiments, where
typically space-filling experimental designs are used. After creation, the
samples can be transformed from the unit hypercube to arbitrary cuboids.
The task of subset selection is defined as follows: suppose you have a set of
points in R^n and want to select a sample of them distributed as uniformly as
possible. This may be necessary because the original set is too large to be
processed entirely. The selection problem is related to clustering, with the
difference that when using clustering, you usually want to retain the
structure of the original point set.
Once one has created (or obtained from somewhere) a point set, one may want to
assess its properties. Therefore, diversipy contains several functions to
measure diversity and a few related concepts. Several different indicators are
offered because they have different advantages and disadvantages (in terms
of run time and what they measure).
Example
=======
>>> from diversipy import *
>>> design = transform_spread_out(lhd_matrix(50, 2)) # create latin hypercube design
>>> subset = psa_select(design, 10) # select subset, for whatever reason
>>> unanchored_L2_discrepancy(subset) # calculate discrepancy
Note that points are stored row-wise, in accordance with numpy convention.
Documentation
=============
The documentation is located at
https://www.simonwessing.de/diversipy/doc/
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"description": "Description\n===========\n\ndiversipy is a collection of algorithms dealing with three different but related\ntopics. The first topic is super-uniform sampling of the unit hypercube.\n\u2018Super-uniform\u2019 in this context means that the obtained point sample should be\nmore uniform than a random uniform sample, which is a desirable property in many\napplications. One such application is the design of computer experiments, where\ntypically space-filling experimental designs are used. After creation, the\nsamples can be transformed from the unit hypercube to arbitrary cuboids.\n\nThe task of subset selection is defined as follows: suppose you have a set of\npoints in R^n and want to select a sample of them distributed as uniformly as\npossible. This may be necessary because the original set is too large to be \nprocessed entirely. The selection problem is related to clustering, with the\ndifference that when using clustering, you usually want to retain the\nstructure of the original point set.\n\nOnce one has created (or obtained from somewhere) a point set, one may want to\nassess its properties. Therefore, diversipy contains several functions to\nmeasure diversity and a few related concepts. Several different indicators are\noffered because they have different advantages and disadvantages (in terms\nof run time and what they measure).\n\n\nExample\n=======\n\n>>> from diversipy import *\n>>> design = transform_spread_out(lhd_matrix(50, 2)) # create latin hypercube design\n>>> subset = psa_select(design, 10) # select subset, for whatever reason\n>>> unanchored_L2_discrepancy(subset) # calculate discrepancy\n\nNote that points are stored row-wise, in accordance with numpy convention.\n\n\nDocumentation\n=============\n\nThe documentation is located at \nhttps://www.simonwessing.de/diversipy/doc/\n",
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