mirror of
https://github.com/NixOS/nixpkgs.git
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4f0dadbf38
After final improvements to the official formatter implementation,
this commit now performs the first treewide reformat of Nix files using it.
This is part of the implementation of RFC 166.
Only "inactive" files are reformatted, meaning only files that
aren't being touched by any PR with activity in the past 2 months.
This is to avoid conflicts for PRs that might soon be merged.
Later we can do a full treewide reformat to get the rest,
which should not cause as many conflicts.
A CI check has already been running for some time to ensure that new and
already-formatted files are formatted, so the files being reformatted here
should also stay formatted.
This commit was automatically created and can be verified using
nix-build a08b3a4d19.tar.gz \
--argstr baseRev b32a094368
result/bin/apply-formatting $NIXPKGS_PATH
58 lines
1.9 KiB
Nix
58 lines
1.9 KiB
Nix
{
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lib,
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stdenv,
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fetchurl,
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automake,
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autoconf,
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bzip2,
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libtar,
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libtool,
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pkg-config,
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autoconf-archive,
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libxml2,
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languageMachines,
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}:
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let
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release = lib.importJSON ./release-info/LanguageMachines-timblserver.json;
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in
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stdenv.mkDerivation {
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pname = "timblserver";
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version = release.version;
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src = fetchurl {
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inherit (release) url sha256;
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name = "timblserver-${release.version}.tar.gz";
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};
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nativeBuildInputs = [
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pkg-config
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automake
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autoconf
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];
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buildInputs = [
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bzip2
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libtar
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libtool
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autoconf-archive
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libxml2
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languageMachines.ticcutils
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languageMachines.timbl
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];
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preConfigure = "sh bootstrap.sh";
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meta = with lib; {
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description = "This server for TiMBL implements several memory-based learning algorithms";
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homepage = "https://github.com/LanguageMachines/timblserver/";
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license = licenses.gpl3;
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platforms = platforms.all;
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maintainers = with maintainers; [ roberth ];
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longDescription = ''
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This implements a server for TiMBL. TiMBL is an open source software package implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision-tree approximation of IB1-IG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases.
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For over fifteen years TiMBL has been mostly used in natural language processing as a machine learning classifier component, but its use extends to virtually any supervised machine learning domain. Due to its particular decision-tree-based implementation, TiMBL is in many cases far more efficient in classification than a standard k-nearest neighbor algorithm would be.
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'';
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};
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}
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