pkgsrc/math/TinySVM, branch pkgsrc-2016Q1 [no description] https://git.osdyson.ru/mirror/pkgsrc/atom?h=pkgsrc-2016Q1 2015-11-03T23:33:26Z Add SHA512 digests for distfiles for math category 2015-11-03T23:33:26Z agc agc@pkgsrc.org 2015-11-03T23:33:26Z urn:sha1:8a412f76baea769849ffaefc9d8d54e24e1c2607 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. Set MAINTAINER to pkgsrc-users. 2015-03-15T17:20:19Z hiramatsu hiramatsu@pkgsrc.org 2015-03-15T17:20:19Z urn:sha1:a4fa66c6878ff4344da51203421b5d2eda543f06 "user-destdir" is default these days 2012-09-11T23:04:15Z asau asau@pkgsrc.org 2012-09-11T23:04:15Z urn:sha1:b0feb9f5b186a62a131121fe59db9ccdc0a844bd Honor CFLAGS/CXXFLAGS and avoid to override the optimizer levels. 2012-03-01T05:01:15Z obache obache@pkgsrc.org 2012-03-01T05:01:15Z urn:sha1:b3b032bc58f0be7bcd8d91faa7bcf09723812260 Add comment. 2012-03-01T04:49:51Z obache obache@pkgsrc.org 2012-03-01T04:49:51Z urn:sha1:537c12fb32210f0234eb8b80c2edb7fa5c5ac85f Remove stupid test for Sun make from configure. 2011-09-14T17:12:18Z hans hans@pkgsrc.org 2011-09-14T17:12:18Z urn:sha1:bfbc557f1b40b52045c9af7fe424b9555191da3a Import TinySVM-0.09 as math/TinySVM. 2010-05-19T11:47:15Z obache obache@pkgsrc.org 2010-05-19T11:47:15Z urn:sha1:db9b4a64bbcef373b43d092f4ab9c882daf8ebfa 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.