XoopsModules25x / xhelp

class/NaiveBayesian.php

<?php declare(strict_types=1);

namespace XoopsModules\Xhelp;

/*
 ***** BEGIN LICENSE BLOCK *****
 This file is part of PHP Naive Bayesian Filter.

 The Initial Developer of the Original Code is
 Loic d'Anterroches [loic_at_xhtml.net].
 Portions created by the Initial Developer are Copyright (C) 2003
 the Initial Developer. All Rights Reserved.

 Contributor(s):
 See the source

 PHP Naive Bayesian Filter is free software; you can redistribute it
 and/or modify it under the terms of the GNU General Public License as
 published by the Free Software Foundation; either version 2 of
 the License, or (at your option) any later version.

 PHP Naive Bayesian Filter is distributed in the hope that it will
 be useful, but WITHOUT ANY WARRANTY; without even the implied
 warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 See the GNU General Public License for more details.

 You should have received a copy of the GNU General Public License
 along with Foobar; if not, write to the Free Software
 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA

 Alternatively, the contents of this file may be used under the terms of
 the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
 in which case the provisions of the LGPL are applicable instead
 of those above.

 ***** END LICENSE BLOCK *****
 */

/**
 * class NaiveBayesian
 */
class NaiveBayesian
{
    /** min token length for it to be taken into consideration */
    public $min_token_length = 3;
    /** max token length for it to be taken into consideration */
    public $max_token_length = 15;
    /** list of token to ignore
     * @see getIgnoreList()
     */
    public $ignore_list = [];
    /** storage object
     * @see class NaiveBayesianStorage
     */
    public $nbs;

    /**
     * Xhelp\NaiveBayesian constructor.
     * @param NaiveBayesianStorage $nbs
     */
    public function __construct(NaiveBayesianStorage $nbs)
    {
        $this->nbs = $nbs;
    }

    /** categorize a document.
     * Get list of categories in which the document can be categorized
     * with a score for each category.
     *
     * @param mixed $document
     * @return array keys = category ids, values = scores
     */
    public function categorize($document): array
    {
        $scores     = [];
        $categories = $this->nbs->getCategories();
        $tokens     = $this->getTokens($document);
        // calculate the score in each category
        $total_words = 0;
        $ncat        = 0;
        //        while (list($category, $data) = each($categories)) {
        foreach ($categories as $category => $data) {
            $total_words += $data['word_count'];
            ++$ncat;
        }
        //        reset($categories);
        //        while (list($category, $data) = each($categories)) {
        foreach ($categories as $category => $data) {
            $scores[$category] = $data['probability'];
            // small probability for a word not in the category
            // maybe putting 1.0 as a 'no effect' word can also be good
            $small_proba = 1.0 / ($data['word_count'] * 2);
            //            reset($tokens);
            //            while (list($token, $count) = each($tokens)) {
            foreach ($tokens as $token => $count) {
                if ($this->nbs->wordExists($token)) {
                    $word = $this->nbs->getWord($token, $category);
                    if ($word['count']) {
                        $proba = $word['count'] / $data['word_count'];
                    } else {
                        $proba = $small_proba;
                    }
                    $scores[$category] *= ($proba ** $count) * (($total_words / $ncat) ** $count);
                    // pow($total_words/$ncat, $count) is here to avoid underflow.
                }
            }
        }

        return $this->rescale($scores);
    }

    /** training against a document.
     * Set a document as being in a specific category. The document becomes a reference
     * and is saved in the table of references. After a set of training is done
     * the updateProbabilities() function must be run.
     *
     * @param mixed $doc_id
     * @param mixed $category_id
     * @param mixed $content
     * @return bool success
     * @see updateProbabilities()
     * @see untrain()
     */
    public function train($doc_id, $category_id, $content): bool
    {
        $tokens = $this->getTokens($content);
        //            while (list($token, $count) = each($tokens)) {
        foreach ($tokens as $token => $count) {
            $this->nbs->updateWord($token, $count, $category_id);
        }
        $this->nbs->saveReference($doc_id, $category_id, $content);

        return true;
    }

    /** untraining of a document.
     * To remove just one document from the references.
     *
     * @param mixed $doc_id
     * @return bool success
     * @see updateProbabilities()
     * @see untrain()
     */
    public function untrain($doc_id): bool
    {
        $ref    = $this->nbs->getReference($doc_id);
        $tokens = $this->getTokens($ref['content']);
        //            while (list($token, $count) = each($tokens)) {
        foreach ($tokens as $token => $count) {
            $this->nbs->removeWord($token, $count, $ref['category_id']);
        }
        $this->nbs->removeReference($doc_id);

        return true;
    }

    /** rescale the results between 0 and 1.
     *
     * @param mixed $scores
     * @return array normalized scores (keys => category, values => scores)
     * @author Ken Williams, ken@mathforum.org
     * @see    categorize()
     */
    public function rescale($scores): array
    {
        // Scale everything back to a reasonable area in
        // logspace (near zero), un-loggify, and normalize
        $total = 0.0;
        $max   = 0.0;
        //        reset($scores);
        //        while (list($cat, $score) = each($scores)) {
        foreach ($scores as $cat => $score) {
            if ($score >= $max) {
                $max = $score;
            }
        }
        //        reset($scores);
        //        while (list($cat, $score) = each($scores)) {
        foreach ($scores as $cat => $score) {
            $scores[$cat] = \exp($score - $max);
            $total        += $scores[$cat] ** 2;
        }
        $total = \sqrt($total);
        //        reset($scores);
        //        while (list($cat, $score) = each($scores)) {
        foreach ($scores as $cat => $score) {
            $scores[$cat] = (float)$score / $total;
        }
        \reset($scores);

        return $scores;
    }

    /** update the probabilities of the categories and word count.
     * This function must be run after a set of training
     *
     * @return bool sucess
     * @see untrain()
     * @see train()
     */
    public function updateProbabilities(): bool
    {
        // this function is really only database manipulation
        // that is why all is done in the NaiveBayesianStorage
        return $this->nbs->updateProbabilities();
    }

    /** Get the list of token to ignore.
     * @return array ignore list
     */
    public function getIgnoreList(): array
    {
        global $xhelp_noise_words;
        $helper = Helper::getInstance();
        @$helper->loadLanguage('noise_words');

        return $xhelp_noise_words;
    }

    /** get the tokens from a string
     *
     * @author   James Seng. [https://james.seng.cc/] (based on his perl version)
     *
     *
     * @param string $string
     * @return array tokens
     * @internal param the $string string to get the tokens from
     */
    private function getTokens(string $string): array
    {
        $rawtokens = [];

The assignment to $rawtokens is dead and can be removed.

        $tokens    = [];
        $string    = $this->cleanString($string);
        if (0 == \count($this->ignore_list)) {
            $this->ignore_list = $this->getIgnoreList();
        }
        $rawtokens = \preg_split('[^-_A-Za-z0-9]+', $string);
        // remove some tokens
        //        while (list(, $token) = each($rawtokens)) {
        foreach ($rawtokens as $key => $token) {
            $token = \trim($token);
            if (!isset($tokens[$token])) {
                $tokens[$token] = 0;
            }
            if (!(('' == $token) || (mb_strlen($token) < $this->min_token_length)
                  || (mb_strlen($token) > $this->max_token_length)
                  || \preg_match('/^[0-9]+$/', $token)
                  || \in_array($token, $this->ignore_list))) {
                $tokens[$token]++;
            }
        }

        return $tokens;
    }

    /** clean a string from the diacritics
     *
     * @param mixed $string
     * @return string clean string
     * @author Antoine Bajolet [phpdig_at_toiletoine.net]
     * @author SPIP [https://uzine.net/spip/]
     */
    private function cleanString($string): string
    {
        $diac = /* A */
            \chr(192) . \chr(193) . \chr(194) . \chr(195) . \chr(196) . \chr(197) . /* a */
            \chr(224) . \chr(225) . \chr(226) . \chr(227) . \chr(228) . \chr(229) . /* O */
            \chr(210) . \chr(211) . \chr(212) . \chr(213) . \chr(214) . \chr(216) . /* o */
            \chr(242) . \chr(243) . \chr(244) . \chr(245) . \chr(246) . \chr(248) . /* E */
            \chr(200) . \chr(201) . \chr(202) . \chr(203) . /* e */
            \chr(232) . \chr(233) . \chr(234) . \chr(235) . /* Cc */
            \chr(199) . \chr(231) . /* I */
            \chr(204) . \chr(205) . \chr(206) . \chr(207) . /* i */
            \chr(236) . \chr(237) . \chr(238) . \chr(239) . /* U */
            \chr(217) . \chr(218) . \chr(219) . \chr(220) . /* u */
            \chr(249) . \chr(250) . \chr(251) . \chr(252) . /* yNn */
            \chr(255) . \chr(209) . \chr(241);

        return \mb_strtolower(strtr($string, $diac, 'AAAAAAaaaaaaOOOOOOooooooEEEEeeeeCcIIIIiiiiUUUUuuuuyNn'));
    }
}