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gnu: python-umap-learn: Update to 0.5.3.
* gnu/packages/machine-learning.scm (python-umap-learn): Update to 0.5.3. [source]: Use git. [arguments]: New field. [native-inputs]: Delete python-joblib and python-nose. Add python-pytest. [propagated-inputs]: Add python-pynndescent and python-tqdm. [synopsis]: Fix indentation. [description]: Fix typo and re-indent.
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@ -2796,26 +2796,40 @@ These include a barrier, broadcast, and allreduce.")
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(define-public python-umap-learn
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(define-public python-umap-learn
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(package
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(package
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(name "python-umap-learn")
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(name "python-umap-learn")
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(version "0.3.10")
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(version "0.5.3")
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(source
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(source
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(origin
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(origin
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(method url-fetch)
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(method git-fetch) ;no tests in pypi release
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(uri (pypi-uri "umap-learn" version))
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(uri (git-reference
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(url "https://github.com/lmcinnes/umap")
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(commit version)))
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(file-name (git-file-name name version))
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(sha256
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(sha256
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(base32
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(base32
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"02ada2yy6km6zgk2836kg1c97yrcpalvan34p8c57446finnpki1"))))
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"1315jkb0h1b579y9m59632f0nnpksilm01nxx46in0rq8zna8vsb"))))
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(build-system python-build-system)
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(build-system python-build-system)
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(native-inputs
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(arguments
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(list python-joblib python-nose))
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(list
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#:phases
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#~(modify-phases %standard-phases
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(replace 'check
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(lambda* (#:key tests? #:allow-other-keys)
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(when tests?
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(setenv "HOME" "/tmp")
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(invoke "pytest" "-vv" "umap")))))))
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(native-inputs (list python-pytest))
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(propagated-inputs
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(propagated-inputs
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(list python-numba python-numpy python-scikit-learn python-scipy))
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(list python-numba
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python-numpy
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python-pynndescent
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python-scikit-learn
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python-scipy
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python-tqdm))
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(home-page "https://github.com/lmcinnes/umap")
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(home-page "https://github.com/lmcinnes/umap")
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(synopsis
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(synopsis "Uniform Manifold Approximation and Projection")
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"Uniform Manifold Approximation and Projection")
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(description "Uniform Manifold Approximation and Projection is a dimension
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(description
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reduction technique that can be used for visualization similarly to t-SNE, but
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"Uniform Manifold Approximation and Projection is a dimension reduction
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also for general non-linear 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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(license license:bsd-3)))
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(define-public nnpack
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(define-public nnpack
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