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https://github.com/sigmasternchen/php-doc-en
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git-svn-id: https://svn.php.net/repository/phpdoc/en/trunk@311951 c90b9560-bf6c-de11-be94-00142212c4b1
112 lines
3.2 KiB
XML
112 lines
3.2 KiB
XML
<?xml version="1.0" encoding="utf-8"?>
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<!-- $Revision$ -->
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<chapter xml:id="svm.examples" xmlns="http://docbook.org/ns/docbook" xmlns:xlink="http://www.w3.org/1999/xlink">
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&reftitle.examples;
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<para>
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The basic process is to define parameters, supply training data to generate a
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model on, then make predictions based on the model. There are a default set
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of parameters that should get some results with most any input, so we'll start
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by looking at the data.
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</para>
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<para>
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Data is supplied in either a file, a stream, or as an array. If supplied in
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a file or a stream, it must contain one line per training example, which must
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be formatted as an integer class (usually 1 and -1) followed by a series of
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feature/value pairs, in increasing feature order. The features are integers,
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the values floats, usually scaled 0-1. For example:
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</para>
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<para>
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-1 1:0.43 3:0.12 9284:0.2
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</para>
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<para>
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In a document classification problem, say a spam checker, each line would
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represent a document. There would be two classes, -1 for spam, 1 for ham.
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Each feature would represent some word, and the value would represent that
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importance of that word to the document (perhaps the frequency count, with
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the total scaled to unit length). Features that were 0 (e.g. the word did
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not appear in the document at all) would simply not be included.
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</para>
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<para>
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In array mode, the data must be passed as an array of arrays. Each sub-array
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must have the class as the first element, then key => value sets for the
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feature values pairs.
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</para>
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<para>
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This data is passed to the SVM class's train function, which will return an
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SVM model is successful.
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</para>
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<para>
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Once a model has been generated, it can be used to make predictions about
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previously unseen data. This can be passed as an array to the model's
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predict function, in the same format as before, but without the label.
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The response will be the class.
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</para>
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<para>
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Models can be saved and restored as required, using the save and load
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functions, which both take a file location.
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</para>
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<para>
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<example>
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<title>Train from array</title>
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<programlisting role="php">
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<![CDATA[
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<?php
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$data = array(
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array(-1, 1 => 0.43, 3 => 0.12, 9284 => 0.2),
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array(1, 1 => 0.22, 5 => 0.01, 94 => 0.11),
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);
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$svm = new SVM();
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$model = $svm->train($data);
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$data = array(1 => 0.43, 3 => 0.12, 9284 => 0.2);
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$result = $model->predict($data);
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var_dump($result);
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$model->save('model.svm');
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?>
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]]>
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</programlisting>
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&example.outputs.similar;
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<screen>
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<![CDATA[
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int(-1)
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]]>
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</screen>
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</example>
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<example>
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<title>Train from a file</title>
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<programlisting role="php">
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<![CDATA[
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<?php
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$svm = new SVM();
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$model = $svm->train("traindata.txt");
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?>
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]]>
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</programlisting>
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</example>
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</para>
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</chapter>
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<!-- Keep this comment at the end of the file
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Local variables:
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mode: sgml
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sgml-omittag:t
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sgml-shorttag:t
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sgml-minimize-attributes:nil
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sgml-always-quote-attributes:t
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sgml-indent-step:1
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sgml-indent-data:t
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indent-tabs-mode:nil
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sgml-parent-document:nil
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sgml-default-dtd-file:"~/.phpdoc/manual.ced"
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sgml-exposed-tags:nil
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sgml-local-catalogs:nil
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sgml-local-ecat-files:nil
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End:
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vim600: syn=xml fen fdm=syntax fdl=2 si
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vim: et tw=78 syn=sgml
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vi: ts=1 sw=1
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-->
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