1 | # $Id$ |
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2 | |
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3 | PortSystem 1.0 |
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4 | PortGroup python26 1.0 |
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5 | |
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6 | name py26-mdp-toolkit |
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7 | version 2.6 |
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8 | categories-append science |
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9 | platforms darwin |
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10 | maintainers mnick |
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11 | description Modular toolkit for Data Processing. |
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12 | long_description From the user's perspective, MDP is a collection of \ |
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13 | supervised and unsupervised learning algorithms and \ |
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14 | other data processing units that can be combined into \ |
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15 | data processing sequences and more complex feed-forward \ |
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16 | network architectures. \ |
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17 | From the scientific developer's perspective, MDP is a \ |
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18 | modular framework, which can easily be expanded. The \ |
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19 | implementation of new algorithms is easy and intuitive. \ |
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20 | The new implemented units are then automatically \ |
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21 | integrated with the rest of the library. \ |
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22 | The base of available algorithms is steadily increasing \ |
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23 | and includes, to name but the most common, Principal \ |
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24 | Component Analysis (PCA and NIPALS), several Independent \ |
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25 | Component Analysis algorithms (CuBICA, FastICA, TDSEP, JADE\ |
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26 | , and XSFA), Slow Feature Analysis, Gaussian Classifiers, \ |
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27 | Restricted Boltzmann Machine, and Locally Linear Embedding. |
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28 | |
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29 | homepage http://mdp-toolkit.sourceforge.net/ |
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30 | master_sites sourceforge:mdp-toolkit |
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31 | distname MDP-${version} |
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32 | |
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33 | checksums md5 508ec7c97c9a25dd450fcc65bd13d925 \ |
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34 | sha1 15df0c075d0095c443b0c23b114a85eccde62337 \ |
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35 | rmd160 50e136918be04258a9ec231d4e153f09de8b4ed3 |
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36 | |
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37 | depends_lib-append port:py26-numpy \ |
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38 | port:py26-distribute |
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39 | |
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40 | depends_build-append port:py26-nose |
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41 | |
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42 | test.run yes |
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43 | test.cmd nosetests-2.6 |
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44 | test.target mdp/test |
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