gnu: Add python-umap-learn.
* gnu/packages/machine-learning.scm (python-umap-learn): New variable.master
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@ -12,6 +12,7 @@
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;;; Copyright © 2018 Björn Höfling <bjoern.hoefling@bjoernhoefling.de>
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;;; Copyright © 2018 Björn Höfling <bjoern.hoefling@bjoernhoefling.de>
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;;; Copyright © 2019 Nicolas Goaziou <mail@nicolasgoaziou.fr>
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;;; Copyright © 2019 Nicolas Goaziou <mail@nicolasgoaziou.fr>
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;;; Copyright © 2019 Guillaume Le Vaillant <glv@posteo.net>
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;;; Copyright © 2019 Guillaume Le Vaillant <glv@posteo.net>
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;;; Copyright © 2019 Brett Gilio <brettg@gnu.org>
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;;;
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;;;
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;;; This file is part of GNU Guix.
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;;; This file is part of GNU Guix.
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;;;
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;;;
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@ -2088,3 +2089,31 @@ number of collective algorithms useful for machine learning applications.
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These include a barrier, broadcast, and allreduce.")
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These include a barrier, broadcast, and allreduce.")
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(home-page "https://github.com/facebookincubator/gloo")
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(home-page "https://github.com/facebookincubator/gloo")
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(license license:bsd-3))))
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(license license:bsd-3))))
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(define-public python-umap-learn
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(package
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(name "python-umap-learn")
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(version "0.3.10")
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(source
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(origin
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(method url-fetch)
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(uri (pypi-uri "umap-learn" version))
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(sha256
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(base32
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"02ada2yy6km6zgk2836kg1c97yrcpalvan34p8c57446finnpki1"))))
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(build-system python-build-system)
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(native-inputs
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`(("python-nose" ,python-nose)))
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(propagated-inputs
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`(("python-numba" ,python-numba)
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("python-numpy" ,python-numpy)
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("python-scikit-learn" ,python-scikit-learn)
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("python-scipy" ,python-scipy)))
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(home-page "https://github.com/lmcinnes/umap")
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(synopsis
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"Uniform Manifold Approximation and Projection")
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(description
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"Uniform Manifold Approximation and Projection is a dimension reduction
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technique that can be used for visualisation similarly to t-SNE, but also for
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general non-linear dimension reduction.")
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(license license:bsd-3)))
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