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Add SHA512 digests for distfiles for math category Problems found locating distfiles: Package dfftpack: missing distfile dfftpack-20001209.tar.gz Package eispack: missing distfile eispack-20001130.tar.gz Package fftpack: missing distfile fftpack-20001130.tar.gz Package linpack: missing distfile linpack-20010510.tar.gz Package minpack: missing distfile minpack-20001130.tar.gz Package odepack: missing distfile odepack-20001130.tar.gz Package py-networkx: missing distfile networkx-1.10.tar.gz Package py-sympy: missing distfile sympy-0.7.6.1.tar.gz Package quadpack: missing distfile quadpack-20001130.tar.gz Otherwise, existing SHA1 digests verified and found to be the same on the machine holding the existing distfiles (morden). All existing SHA1 digests retained for now as an audit trail.
2015-11-04 00:33:26 +01:00
$NetBSD: distinfo,v 1.5 2015/11/03 23:33:30 agc Exp $
Import TinySVM-0.09 as math/TinySVM. TinySVM is an implementation of Support Vector Machines (SVMs) [Vapnik 95], [Vapnik 98] for the problem of pattern recognition. Support Vector Machines is a new generation learning algorithms based on recent advances in statistical learning theory, and applied to large number of real-world applications, such as text categorization, hand-written character recognition.
2010-05-19 13:47:15 +02:00
SHA1 (TinySVM-0.09.tar.gz) = 9c3c36454c475180ef6646d059376f35549cad08
RMD160 (TinySVM-0.09.tar.gz) = d44586810d4776a251cda4a08b1aa9148108c8e6
Add SHA512 digests for distfiles for math category Problems found locating distfiles: Package dfftpack: missing distfile dfftpack-20001209.tar.gz Package eispack: missing distfile eispack-20001130.tar.gz Package fftpack: missing distfile fftpack-20001130.tar.gz Package linpack: missing distfile linpack-20010510.tar.gz Package minpack: missing distfile minpack-20001130.tar.gz Package odepack: missing distfile odepack-20001130.tar.gz Package py-networkx: missing distfile networkx-1.10.tar.gz Package py-sympy: missing distfile sympy-0.7.6.1.tar.gz Package quadpack: missing distfile quadpack-20001130.tar.gz Otherwise, existing SHA1 digests verified and found to be the same on the machine holding the existing distfiles (morden). All existing SHA1 digests retained for now as an audit trail.
2015-11-04 00:33:26 +01:00
SHA512 (TinySVM-0.09.tar.gz) = 457a528c9b9cae5652781034d449806c69b79c9abcb1b9d4d40c47f081971baecebc845497836b6077290154fb8e29af1439bbeb9f5958d06ea6317942125f02
Import TinySVM-0.09 as math/TinySVM. TinySVM is an implementation of Support Vector Machines (SVMs) [Vapnik 95], [Vapnik 98] for the problem of pattern recognition. Support Vector Machines is a new generation learning algorithms based on recent advances in statistical learning theory, and applied to large number of real-world applications, such as text categorization, hand-written character recognition.
2010-05-19 13:47:15 +02:00
Size (TinySVM-0.09.tar.gz) = 317636 bytes
SHA1 (patch-aa) = bbeefe45ecf7f447eb8b6f692d035b74f01cb7a8
Honor CFLAGS/CXXFLAGS and avoid to override the optimizer levels.
2012-03-01 06:01:15 +01:00
SHA1 (patch-ab) = 4d8214b35c6422ba04a5e5d1007057363a704978
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