47 lines
1.7 KiB
Ruby
47 lines
1.7 KiB
Ruby
class Libsvm < Formula
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desc "Library for support vector machines"
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homepage "https://www.csie.ntu.edu.tw/~cjlin/libsvm/"
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# Upstream deletes old downloads, so we need to mirror it ourselves
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url "https://www.csie.ntu.edu.tw/~cjlin/libsvm/libsvm-3.23.tar.gz"
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mirror "https://dl.bintray.com/homebrew/mirror/libsvm-3.23.tar.gz"
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sha256 "257aed630dc0a0163e12cb2a80aea9c7dc988e55f28d69c945a38b9433c0ea4a"
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bottle do
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cellar :any
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sha256 "c2c9525f4cdff0654a5a805dc60aa09880454f0fa5ab92eb1e4c0287cd738c96" => :catalina
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sha256 "75d440e35a774490aea6cec6fd514779069d3ffa55febce89a3f1eb8bad45337" => :mojave
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sha256 "661d867329c2851e84d02e78d2debc78357c9aa0d576223a1011b4d5533a7391" => :high_sierra
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sha256 "e78ffd8fb5a4c430e206462619ef419cde99f48728d09baaf250dc1cbc121abc" => :sierra
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end
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def install
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system "make", "CFLAGS=#{ENV.cflags}"
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system "make", "lib"
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bin.install "svm-scale", "svm-train", "svm-predict"
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lib.install "libsvm.so.2" => "libsvm.2.dylib"
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lib.install_symlink "libsvm.2.dylib" => "libsvm.dylib"
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MachO::Tools.change_dylib_id("#{lib}/libsvm.2.dylib", "#{lib}/libsvm.2.dylib")
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include.install "svm.h"
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end
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test do
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(testpath/"train_classification.txt").write <<~EOS
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+1 201:1.2 3148:1.8 3983:1 4882:1
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-1 874:0.3 3652:1.1 3963:1 6179:1
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+1 1168:1.2 3318:1.2 3938:1.8 4481:1
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+1 350:1 3082:1.5 3965:1 6122:0.2
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-1 99:1 3057:1 3957:1 5838:0.3
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EOS
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(testpath/"train_regression.txt").write <<~EOS
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0.23 201:1.2 3148:1.8 3983:1 4882:1
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0.33 874:0.3 3652:1.1 3963:1 6179:1
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-0.12 1168:1.2 3318:1.2 3938:1.8 4481:1
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EOS
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system "#{bin}/svm-train", "-s", "0", "train_classification.txt"
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system "#{bin}/svm-train", "-s", "3", "train_regression.txt"
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end
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end
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