33 lines
1.6 KiB
Python
33 lines
1.6 KiB
Python
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
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# Spack Project Developers. See the top-level COPYRIGHT file for details.
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#
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# SPDX-License-Identifier: (Apache-2.0 OR MIT)
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from spack import *
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class RRsvd(RPackage):
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"""Low-rank matrix decompositions are fundamental tools and widely used for
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data analysis, dimension reduction, and data compression. Classically,
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highly accurate deterministic matrix algorithms are used for this task.
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However, the emergence of large-scale data has severely challenged our
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computational ability to analyze big data. The concept of randomness has
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been demonstrated as an effective strategy to quickly produce approximate
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answers to familiar problems such as the singular value decomposition
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(SVD). The rsvd package provides several randomized matrix algorithms such
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as the randomized singular value decomposition (rsvd), randomized principal
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component analysis (rpca), randomized robust principal component analysis
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(rrpca), randomized interpolative decomposition (rid), and the randomized
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CUR decomposition (rcur). In addition several plot functions are provided.
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The methods are discussed in detail by Erichson et al. (2016)
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<arXiv:1608.02148>."""
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homepage = "https://github.com/erichson/rSVD"
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url = "https://cloud.r-project.org/src/contrib/rsvd_1.0.2.tar.gz"
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list_url = "https://cloud.r-project.org/src/contrib/Archive/rsvd"
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version('1.0.2', sha256='c8fe5c18bf7bcfe32604a897e3a7caae39b49e47e93edad9e4d07657fc392a3a')
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depends_on('r@3.2.2:', type=('build', 'run'))
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depends_on('r-matrix', type=('build', 'run'))
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