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af_sort_bayes: move to -attic repo, not really suitable for production use

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Andrew Dolgov 2015-11-25 21:07:24 +03:00
parent f0ebb41b27
commit 74a752879b
14 changed files with 0 additions and 1850 deletions
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plugins/af_sort_bayes/init.js
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function bayesTrain(id, train_up, event) {
try {
event.stopPropagation();
var query = "backend.php?op=pluginhandler&plugin=af_sort_bayes&method=trainArticle&article_id=" + param_escape(id) +
"&train_up=" + param_escape(train_up);
notify_progress("Loading, please wait...");
new Ajax.Request("backend.php", {
parameters: query,
onComplete: function(transport) {
notify(transport.responseText);
updateScore(id);
} });
} catch (e) {
exception_error("showTrgmRelated", e);
}
}
function bayesClearDatabase() {
try {
if (confirm(__("Clear classifier database?"))) {
var query = "backend.php?op=pluginhandler&plugin=af_sort_bayes&method=clearDatabase";
new Ajax.Request("backend.php", {
parameters: query,
onComplete: function (transport) {
notify(transport.responseText);
bayesUpdateUI();
}
});
}
} catch (e) {
exception_error("showTrgmRelated", e);
}
}
function bayesUpdateUI() {
try {
var query = "backend.php?op=pluginhandler&plugin=af_sort_bayes&method=renderPrefsUI";
new Ajax.Request("backend.php", {
parameters: query,
onComplete: function (transport) {
dijit.byId("af_sort_bayes_prefs").attr("content", transport.responseText);
}
});
} catch (e) {
exception_error("showTrgmRelated", e);
}
}
function bayesShow(id) {
try {
if (dijit.byId("bayesShowDlg"))
dijit.byId("bayesShowDlg").destroyRecursive();
var query = "backend.php?op=pluginhandler&plugin=af_sort_bayes&method=showArticleStats&article_id=" + param_escape(id);
dialog = new dijit.Dialog({
id: "bayesShowDlg",
title: __("Classifier information"),
style: "width: 600px",
href: query});
dialog.show();
} catch (e) {
exception_error("shareArticle", e);
}
}

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plugins/af_sort_bayes/init.php
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<?php
class Af_Sort_Bayes extends Plugin {
private $host;
private $filters = array();
private $dbh;
private $score_modifier = 50;
private $sql_prefix = "ttrss_plugin_af_sort_bayes";
private $auto_categorize_threshold = 10000;
private $max_document_length = 3000; // classifier can't rescale output for very long strings apparently
function about() {
return array(1.0,
"Bayesian classifier for tt-rss (WIP)",
"fox");
}
function init($host) {
require_once __DIR__ . "/lib/class.naivebayesian.php";
//require_once __DIR__ . "/lib/class.naivebayesian_ngram.php";
require_once __DIR__ . "/lib/class.naivebayesianstorage.php";
$this->host = $host;
$this->dbh = Db::get();
$this->init_database();
$host->add_hook($host::HOOK_ARTICLE_FILTER, $this);
$host->add_hook($host::HOOK_PREFS_TAB, $this);
$host->add_hook($host::HOOK_ARTICLE_BUTTON, $this);
}
function trainArticle() {
$article_id = (int) $_REQUEST["article_id"];
$train_up = sql_bool_to_bool($_REQUEST["train_up"]);
//$category = $train_up ? "GOOD" : "UGLY";
$dst_category = "UGLY";
$nbs = new NaiveBayesianStorage($_SESSION["uid"]);
$nb = new NaiveBayesian($nbs);
$result = $this->dbh->query("SELECT score, guid, title, content FROM ttrss_entries, ttrss_user_entries WHERE ref_id = id AND id = " .
$article_id . " AND owner_uid = " . $_SESSION["uid"]);
if ($this->dbh->num_rows($result) != 0) {
$guid = $this->dbh->fetch_result($result, 0, "guid");
$title = $this->dbh->fetch_result($result, 0, "title");
$content = mb_substr(mb_strtolower($title . " " . strip_tags($this->dbh->fetch_result($result, 0, "content"))), 0, $this->max_document_length);
$score = $this->dbh->fetch_result($result, 0, "score");
$this->dbh->query("BEGIN");
$ref = $nbs->getReference($guid, false);
if (isset($ref['category_id'])) {
$current_category = $nbs->getCategoryById($ref['category_id']);
} else {
$current_category = "UGLY";
}
// set score to fixed value for now
if ($train_up) {
switch ($current_category) {
case "UGLY":
$dst_category = "GOOD";
$score = $this->score_modifier;
break;
case "BAD":
$dst_category = "UGLY";
$score = 0;
break;
case "GOOD":
$dst_category = "GOOD";
break;
}
} else {
switch ($current_category) {
case "UGLY":
$dst_category = "BAD";
$score = -$this->score_modifier;
break;
case "BAD":
$dst_category = "BAD";
break;
case "GOOD":
$dst_category = "UGLY";
$score = 0;
break;
}
}
$nb->untrain($guid, $content);
$nb->train($guid, $nbs->getCategoryByName($dst_category), $content);
$this->dbh->query("UPDATE ttrss_user_entries SET score = '$score' WHERE ref_id = $article_id AND owner_uid = " . $_SESSION["uid"]);
$nb->updateProbabilities();
$this->dbh->query("COMMIT");
}
print "$article_id :: $dst_category :: $score";
}
function get_js() {
return file_get_contents(__DIR__ . "/init.js");
}
function get_prefs_js() {
return file_get_contents(__DIR__ . "/init.js");
}
function hook_article_button($line) {
return "<img src=\"plugins/af_sort_bayes/thumb_up.png\"
style=\"cursor : pointer\" style=\"cursor : pointer\"
onclick=\"bayesTrain(".$line["id"].", true, event)\"
class='tagsPic' title='".__('+1')."'>" .
"<img src=\"plugins/af_sort_bayes/thumb_down.png\"
style=\"cursor : pointer\" style=\"cursor : pointer\"
onclick=\"bayesTrain(".$line["id"].", false, event)\"
class='tagsPic' title='".__('-1')."'>" .
"<img src=\"plugins/af_sort_bayes/chart_bar.png\"
style=\"cursor : pointer\" style=\"cursor : pointer\"
onclick=\"bayesShow(".$line["id"].")\"
class='tagsPic' title='".__('Show classifier info')."'>";
}
function init_database() {
$prefix = $this->sql_prefix;
// TODO there probably should be a way for plugins to determine their schema version to upgrade tables
/*$this->dbh->query("DROP TABLE IF EXISTS ${prefix}_wordfreqs", false);
$this->dbh->query("DROP TABLE IF EXISTS ${prefix}_references", false);
$this->dbh->query("DROP TABLE IF EXISTS ${prefix}_categories", false);*/
$this->dbh->query("BEGIN");
// PG only for the time being
if (DB_TYPE == "mysql") {
$this->dbh->query("CREATE TABLE IF NOT EXISTS ${prefix}_categories (
id INTEGER NOT NULL PRIMARY KEY auto_increment,
category varchar(100) NOT NULL DEFAULT '',
probability DOUBLE NOT NULL DEFAULT '0',
owner_uid INTEGER NOT NULL,
FOREIGN KEY (owner_uid) REFERENCES ttrss_users(id) ON DELETE CASCADE,
word_count BIGINT NOT NULL DEFAULT '0') ENGINE=InnoDB");
$this->dbh->query("CREATE TABLE IF NOT EXISTS ${prefix}_references (
id INTEGER NOT NULL PRIMARY KEY auto_increment,
document_id VARCHAR(255) NOT NULL,
category_id INTEGER NOT NULL,
FOREIGN KEY (category_id) REFERENCES ${prefix}_categories(id) ON DELETE CASCADE,
owner_uid INTEGER NOT NULL,
FOREIGN KEY (owner_uid) REFERENCES ttrss_users(id) ON DELETE CASCADE) ENGINE=InnoDB");
$this->dbh->query("CREATE TABLE IF NOT EXISTS ${prefix}_wordfreqs (
word varchar(100) NOT NULL DEFAULT '',
category_id INTEGER NOT NULL,
FOREIGN KEY (category_id) REFERENCES ${prefix}_categories(id) ON DELETE CASCADE,
owner_uid INTEGER NOT NULL,
FOREIGN KEY (owner_uid) REFERENCES ttrss_users(id) ON DELETE CASCADE,
count BIGINT NOT NULL DEFAULT '0') ENGINE=InnoDB");
} else {
$this->dbh->query("CREATE TABLE IF NOT EXISTS ${prefix}_categories (
id SERIAL NOT NULL PRIMARY KEY,
category varchar(100) NOT NULL DEFAULT '',
probability DOUBLE PRECISION NOT NULL DEFAULT '0',
owner_uid INTEGER NOT NULL REFERENCES ttrss_users(id) ON DELETE CASCADE,
word_count BIGINT NOT NULL DEFAULT '0')");
$this->dbh->query("CREATE TABLE IF NOT EXISTS ${prefix}_references (
id SERIAL NOT NULL PRIMARY KEY,
document_id VARCHAR(255) NOT NULL,
category_id INTEGER NOT NULL REFERENCES ${prefix}_categories(id) ON DELETE CASCADE,
owner_uid INTEGER NOT NULL REFERENCES ttrss_users(id) ON DELETE CASCADE)");
$this->dbh->query("CREATE TABLE IF NOT EXISTS ${prefix}_wordfreqs (
word varchar(100) NOT NULL DEFAULT '',
category_id INTEGER NOT NULL REFERENCES ${prefix}_categories(id) ON DELETE CASCADE,
owner_uid INTEGER NOT NULL REFERENCES ttrss_users(id) ON DELETE CASCADE,
count BIGINT NOT NULL DEFAULT '0')");
}
$owner_uid = @$_SESSION["uid"];
if ($owner_uid) {
$result = $this->dbh->query("SELECT id FROM ${prefix}_categories WHERE owner_uid = $owner_uid LIMIT 1");
if ($this->dbh->num_rows($result) == 0) {
$this->dbh->query("INSERT INTO ${prefix}_categories (category, owner_uid) VALUES ('GOOD', $owner_uid)");
$this->dbh->query("INSERT INTO ${prefix}_categories (category, owner_uid) VALUES ('BAD', $owner_uid)");
$this->dbh->query("INSERT INTO ${prefix}_categories (category, owner_uid) VALUES ('UGLY', $owner_uid)");
}
}
$this->dbh->query("COMMIT");
}
function renderPrefsUI() {
$result = $this->dbh->query("SELECT category, probability, word_count,
(SELECT COUNT(id) FROM {$this->sql_prefix}_references WHERE
category_id = {$this->sql_prefix}_categories.id) as doc_count
FROM {$this->sql_prefix}_categories WHERE owner_uid = " . $_SESSION["uid"]);
print "<h3>" . __("Statistics") . "</h3>";
print "<p>".T_sprintf("Required UGLY word count for automatic matching: %d", $this->auto_categorize_threshold)."</p>";
print "<table>";
print "<tr><th>Category</th><th>Probability</th><th>Words</th><th>Articles</th></tr>";
while ($line = $this->dbh->fetch_assoc($result)) {
print "<tr>";
foreach ($line as $k => $v) {
if ($k == "probability") $v = sprintf("%.3f", $v);
print "<td>$v</td>";
}
print "</tr>";
}
print "</table>";
print "<h3>" . __("Last matched articles") . "</h3>";
$result = $this->dbh->query("SELECT te.title, category, tf.title AS feed_title
FROM ttrss_entries AS te, ttrss_user_entries AS tu, ttrss_feeds AS tf, {$this->sql_prefix}_references AS tr, {$this->sql_prefix}_categories AS tc
WHERE tf.id = tu.feed_id AND tu.ref_id = te.id AND tc.id = tr.category_id AND tr.document_id = te.guid ORDER BY te.id DESC LIMIT 20");
print "<ul class=\"browseFeedList\" style=\"border-width : 1px\">";
while ($line = $this->dbh->fetch_assoc($result)) {
print "<li>" . $line["category"] . ": " . $line["title"] . " (" . $line["feed_title"] . ")</li>";
}
print "</ul>";
print "<button dojoType=\"dijit.form.Button\" onclick=\"return bayesUpdateUI()\">".
__('Refresh')."</button> ";
print "<button dojoType=\"dijit.form.Button\" onclick=\"return bayesClearDatabase()\">".
__('Clear database')."</button> ";
//
}
function hook_prefs_tab($args) {
if ($args != "prefPrefs") return;
print "<div id=\"af_sort_bayes_prefs\" dojoType=\"dijit.layout.AccordionPane\" title=\"".__('Bayesian classifier (af_sort_bayes)')."\">";
$this->renderPrefsUI();
print "</div>";
}
function hook_article_filter($article) {
$owner_uid = $article["owner_uid"];
// guid already includes owner_uid so we don't need to include it
$result = $this->dbh->query("SELECT id FROM {$this->sql_prefix}_references WHERE
document_id = '" . $this->dbh->escape_string($article['guid_hashed']) . "'");
if (db_num_rows($result) != 0) {
_debug("bayes: article already categorized");
return $article;
}
$nbs = new NaiveBayesianStorage($owner_uid);
$nb = new NaiveBayesian($nbs);
$categories = $nbs->getCategories();
if (count($categories) > 0) {
$count_neutral = 0;
$id_good = 0;
$id_ugly = 0;
$id_bad = 0;
foreach ($categories as $id => $cat) {
if ($cat["category"] == "GOOD") {
$id_good = $id;
} else if ($cat["category"] == "UGLY") {
$id_ugly = $id;
$count_neutral += $cat["word_count"];
} else if ($cat["category"] == "BAD") {
$id_bad = $id;
}
}
$dst_category = $id_ugly;
$bayes_content = mb_substr(mb_strtolower($article["title"] . " " . strip_tags($article["content"])), 0, $this->max_document_length);
if ($count_neutral >= $this->auto_categorize_threshold) {
// enable automatic categorization
$result = $nb->categorize($bayes_content);
//print_r($result);
if (count($result) == 3) {
$prob_good = $result[$id_good];
$prob_bad = $result[$id_bad];
if (!is_nan($prob_good) && $prob_good > 0.90) {
$dst_category = $id_good;
$article["score_modifier"] += $this->score_modifier;
} else if (!is_nan($prob_bad) && $prob_bad > 0.90) {
$dst_category = $id_bad;
$article["score_modifier"] -= $this->score_modifier;
}
}
_debug("bayes, dst category: $dst_category");
}
$nb->train($article["guid_hashed"], $dst_category, $bayes_content);
$nb->updateProbabilities();
}
return $article;
}
function clearDatabase() {
$prefix = $this->sql_prefix;
$this->dbh->query("BEGIN");
$this->dbh->query("DELETE FROM ${prefix}_references WHERE owner_uid = " . $_SESSION["uid"]);
$this->dbh->query("DELETE FROM ${prefix}_wordfreqs WHERE owner_uid = " . $_SESSION["uid"]);
$this->dbh->query("COMMIT");
$nbs = new NaiveBayesianStorage($_SESSION["uid"]);
$nb = new NaiveBayesian($nbs);
$nb->updateProbabilities();
}
function showArticleStats() {
$article_id = (int) $_REQUEST["article_id"];
$result = $this->dbh->query("SELECT score, guid, title, content FROM ttrss_entries, ttrss_user_entries WHERE ref_id = id AND id = " .
$article_id . " AND owner_uid = " . $_SESSION["uid"]);
if ($this->dbh->num_rows($result) != 0) {
$guid = $this->dbh->fetch_result($result, 0, "guid");
$title = $this->dbh->fetch_result($result, 0, "title");
$content = mb_substr(mb_strtolower($title . " " . strip_tags($this->dbh->fetch_result($result, 0, "content"))), 0, $this->max_document_length);
print "<h2>" . $title . "</h2>";
$nbs = new NaiveBayesianStorage($_SESSION["uid"]);
$nb = new NaiveBayesian($nbs);
$categories = $nbs->getCategories();
$ref = $nbs->getReference($guid, false);
$current_cat = isset($ref["category_id"]) ? $categories[$ref["category_id"]]["category"] : "N/A";
print "<p>" . T_sprintf("Currently stored as: %s", $current_cat) . "</p>";
$result = $nb->categorize($content);
print "<h3>" . __("Classifier result") . "</h3>";
print "<table>";
print "<tr><th>Category</th><th>Probability</th></tr>";
foreach ($result as $k => $v) {
print "<tr>";
print "<td>" . $categories[$k]["category"] . "</td>";
print "<td>" . $v . "</td>";
print "</tr>";
}
print "</table>";
} else {
print_error("Article not found");
}
print "<div align='center'>";
print "<button dojoType=\"dijit.form.Button\" onclick=\"return dijit.byId('bayesShowDlg').hide()\">".
__('Close this window')."</button>";
print "</div>";
}
function api_version() {
return 2;
}
}
?>

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plugins/af_sort_bayes/lib/COPYING
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GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.
59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
Everyone is permitted to copy and distribute verbatim copies
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8. If the distribution and/or use of the Program is restricted in
certain countries either by patents or by copyrighted interfaces, the
original copyright holder who places the Program under this License
may add an explicit geographical distribution limitation excluding
those countries, so that distribution is permitted only in or among
countries not thus excluded. In such case, this License incorporates
the limitation as if written in the body of this License.
9. The Free Software Foundation may publish revised and/or new versions
of the General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the Program
specifies a version number of this License which applies to it and "any
later version", you have the option of following the terms and conditions
either of that version or of any later version published by the Free
Software Foundation. If the Program does not specify a version number of
this License, you may choose any version ever published by the Free Software
Foundation.
10. If you wish to incorporate parts of the Program into other free
programs whose distribution conditions are different, write to the author
to ask for permission. For software which is copyrighted by the Free
Software Foundation, write to the Free Software Foundation; we sometimes
make exceptions for this. Our decision will be guided by the two goals
of preserving the free status of all derivatives of our free software and
of promoting the sharing and reuse of software generally.
NO WARRANTY
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
REPAIR OR CORRECTION.
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS

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2003/11/02 - Sortie de la version initiale 1.0

339
plugins/af_sort_bayes/lib/LICENSE
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GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The licenses for most software are designed to take away your
freedom to share and change it. By contrast, the GNU General Public
License is intended to guarantee your freedom to share and change free
software--to make sure the software is free for all its users. This
General Public License applies to most of the Free Software
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using it. (Some other Free Software Foundation software is covered by
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your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
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To protect your rights, we need to make restrictions that forbid
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We protect your rights with two steps: (1) copyright the software, and
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The precise terms and conditions for copying, distribution and
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TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
0. This License applies to any program or other work which contains
a notice placed by the copyright holder saying it may be distributed
under the terms of this General Public License. The "Program", below,
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either verbatim or with modifications and/or translated into another
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the term "modification".) Each licensee is addressed as "you".
Activities other than copying, distribution and modification are not
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and give any other recipients of the Program a copy of this License
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It is not the purpose of this section to induce you to infringe any
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integrity of the free software distribution system, which is
implemented by public license practices. Many people have made
generous contributions to the wide range of software distributed
through that system in reliance on consistent application of that
system; it is up to the author/donor to decide if he or she is willing
to distribute software through any other system and a licensee cannot
impose that choice.
This section is intended to make thoroughly clear what is believed to
be a consequence of the rest of this License.
8. If the distribution and/or use of the Program is restricted in
certain countries either by patents or by copyrighted interfaces, the
original copyright holder who places the Program under this License
may add an explicit geographical distribution limitation excluding
those countries, so that distribution is permitted only in or among
countries not thus excluded. In such case, this License incorporates
the limitation as if written in the body of this License.
9. The Free Software Foundation may publish revised and/or new versions
of the General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the Program
specifies a version number of this License which applies to it and "any
later version", you have the option of following the terms and conditions
either of that version or of any later version published by the Free
Software Foundation. If the Program does not specify a version number of
this License, you may choose any version ever published by the Free Software
Foundation.
10. If you wish to incorporate parts of the Program into other free
programs whose distribution conditions are different, write to the author
to ask for permission. For software which is copyrighted by the Free
Software Foundation, write to the Free Software Foundation; we sometimes
make exceptions for this. Our decision will be guided by the two goals
of preserving the free status of all derivatives of our free software and
of promoting the sharing and reuse of software generally.
NO WARRANTY
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
REPAIR OR CORRECTION.
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
{description}
Copyright (C) {year} {fullname}
This program 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.
This program 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 this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, the commands you use may
be called something other than `show w' and `show c'; they could even be
mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the program, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
{signature of Ty Coon}, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.

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PHP Naive Bayesian Filter
============================================================
This library implements Naive Bayes classifier. Original Project developed by Loic d'Anterroches [loic xhtml.net]. This Library is very Usefull but is not Published now. so Salvage from [Internet Archive] and create Github Repositry.
see more information to Original Readme.txt and [Original Page].
And If there is a problem with the publication of this repository, I will close this repository.
[loic xhtml.net]: <http://www.xhtml.net/php/PHPNaiveBayesianFilter>
[Internet Archive]: <https://archive.org/>
[Original Page]: <https://web.archive.org/web/20111211215027/http://www.xhtml.net/php/PHPNaiveBayesianFilter>
Writing by Japanese
------------------------------------------------------------
このライブラリは単純ベイズ分類器を実装したライブラリです。元のプロジェクトはLoic d'Anterrochesが作成しています。非常に有益なライブラリですが、現在は公開されていないようです。そこでインターネットアーカイブからライブラリをサルベージし、Githubのレポジトリを作成しました。
より詳しい情報はオリジナルのReadme.txtを参照してください。
もし、このレポジトリの公開に問題があるようならば、このレポジトリを削除します。
extract Original Readme
------------------------------------------------------------
> This file is part of PHP Naive Bayesian Filter.
>
> The Initial Developer of the Original Code is
> Loic d'Anterroches [loic xhtml.net].
> Portions created by the Initial Developer are Copyright (C) 2003
> the Initial Developer. All Rights Reserved.
>
> 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

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/*
***** BEGIN LICENSE BLOCK *****
This file is part of PHP Naive Bayesian Filter.
The Initial Developer of the Original Code is
Loic d'Anterroches [loic xhtml.net].
Portions created by the Initial Developer are Copyright (C) 2003
the Initial Developer. All Rights Reserved.
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
***** END LICENSE BLOCK *****
*/
** Presentation **
Voici une implementation generale d'un filtre reposant sur le theoreme de Bayes.
L'application la plus connue est le filtre anti-spam. Vous pouvez aussi
l'utiliser pour faire de la classification automatique de documents.
Ce programme se base sur la version simplifiee du theoreme de Bayes comme
decrite par Ken Williams, ken@mathforum.org sur la page
http://mathforum.org/~ken/bayes/bayes.html au 31/10/2003.
Le systeme permet de maniere generale de faire la classification de documents
textes dans differentes categories. Si vous voulez l'utiliser pour une
classification de vos messages entre spam et non-spam, alors il vous faudra 2
categories, une "spam" et une "nonspam".
J'ai cree ce script car c'est une sujet a la mode en ce moment. Particulierement
pour filtrer les commentaires et les trackbacks dans les blogs. Le systeme
propose ici permet d'avoir plus que deux categories spam et non spam. Cela permet
donc theoriquement de l'utiliser pour la classification dans de multiples
categories.
Un petit script 'index.php' vous permet de tester le systeme, ensuite vous
pouvez inclure la classe dans vos scripts. Les fichiers class.naivebayesian.php
et class.naivebayesianstorage.php peuvent aussi etre utilises avec la licence
GNU Lesser General Public License Version 2.1 ou ulterieure.
** Fonctionnalites **
- Une classe avec la logique de base, une autre qui est l'interface de stockage.
- Stockage des donnees dans une base de données pour le moment MySQL mais
vous pouvez utiliser celle que vous voulez via l'interface de stockage.
- Apprentissage
- Desapprentissage
- Archivage automatique des documents "reference"
- L'interface de stockage par defaut utilise MySQL et repose sur deux classes
d'Olivier Meunier.
** Utilisation **
Regardez le code de index.php
Pour une bonne utilisation il vous faut creer une autre classe qui herite de
NaiveBayesian pour avoir votre propre fonction pour ignorer les mots qui ne
portent pas de sens particulier. Ceci n'est pas fait dans 'index.php'
class votreclass extends NaiveBayesian
{
function getIgnoreList()
{
return array('the', 'that', 'you', 'for', 'and');
}
}
** Des questions **
Pouvez me contacter par email a loic xhtml.net, ou venir sur http://www.xhtml.net/

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1.0

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<?php
/*
***** 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 {
/** min token length for it to be taken into consideration */
var $min_token_length = 3;
/** max token length for it to be taken into consideration */
var $max_token_length = 15;
/** list of token to ignore
@see getIgnoreList()
*/
var $ignore_list = array();
/** storage object
@see class NaiveBayesianStorage
*/
var $nbs = null;
function NaiveBayesian($nbs) {
$this->nbs = $nbs;
return true;
}
/** categorize a document.
Get list of categories in which the document can be categorized
with a score for each category.
@return array keys = category ids, values = scores
@param string document
*/
function categorize($document) {
$scores = array();
$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)) {
$total_words += $data['word_count'];
$ncat++;
}
reset($categories);
while (list($category, $data) = each($categories)) {
$scores[$category] = $data['probability'];
// small probability for a word not in the category
// maybe putting 1.0 as a 'no effect' word can also be good
if ($data['word_count'] > 0)
$small_proba = 1.0 / ($data['word_count'] * 2);
else
$small_proba = 0;
reset($tokens);
while (list($token, $count) = each($tokens)) {
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] *= pow($proba, $count) * pow($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.
@see updateProbabilities()
@see untrain()
@return bool success
@param string document id, must be unique
@param string category_id the category id in which the document should be
@param string content of the document
*/
function train($doc_id, $category_id, $content) {
$ret = false;
// if this doc_id already trained, no trained
if (!$this->nbs->getReference($doc_id, false)) {
$tokens = $this->_getTokens($content);
while (list($token, $count) = each($tokens)) {
$this->nbs->updateWord($token, $count, $category_id);
}
$this->nbs->saveReference($doc_id, $category_id, $content);
$ret = true;
}
else {
$ret = false;
}
return $ret;
}
/** untraining of a document.
To remove just one document from the references.
@see updateProbabilities()
@see untrain()
@return bool success
@param string document id, must be unique
*/
function untrain($doc_id) {
$ref = $this->nbs->getReference($doc_id);
if (isset($ref['content'])) {
$tokens = $this->_getTokens($ref['content']);
while (list($token, $count) = each($tokens)) {
$this->nbs->removeWord($token, $count, $ref['category_id']);
}
$this->nbs->removeReference($doc_id);
return true;
} else {
return false;
}
}
/** rescale the results between 0 and 1.
@author Ken Williams, ken@mathforum.org
@see categorize()
@return array normalized scores (keys => category, values => scores)
@param array scores (keys => category, values => scores)
*/
function _rescale($scores) {
// 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)) {
if ($score >= $max)
$max = $score;
}
reset($scores);
while (list($cat, $score) = each($scores)) {
$scores[$cat] = (float) exp($score - $max);
$total += (float) pow($scores[$cat], 2);
}
$total = (float) sqrt($total);
reset($scores);
while (list($cat, $score) = each($scores)) {
$scores[$cat] = (float) $scores[$cat] / $total;
}
reset($scores);
return $scores;
}
/** update the probabilities of the categories and word count.
This function must be run after a set of training
@see train()
@see untrain()
@return bool sucess
*/
function updateProbabilities() {
// this function is really only database manipulation
// that is why all is done in the NaiveBayesianStorage
return $this->nbs->updateProbabilities();
}
/** Get the list of token to ignore.
@return array ignore list
*/
function getIgnoreList() {
//return array('the', 'that', 'you', 'for', 'and');
// https://en.wikipedia.org/wiki/Most_common_words_in_English
return array('the', 'be', 'to', 'of', 'and', 'a', 'in', 'that', 'have', 'I', 'it', 'for', 'not', 'on', 'with',
'he', 'as', 'you', 'do', 'at', 'this', 'but', 'his', 'by', 'from', 'they', 'we', 'say', 'her',
'she', 'or', 'an', 'will', 'my', 'one', 'all', 'would', 'there', 'their', 'what', 'so', 'up',
'out', 'if', 'about', 'who', 'get', 'which', 'go', 'me', 'when', 'make', 'can', 'like', 'time',
'no', 'just', 'him', 'know', 'take', 'people', 'into', 'year', 'your', 'good', 'some', 'could',
'them', 'see', 'other', 'than', 'then', 'now', 'look', 'only', 'come', 'its', 'over', 'think',
'also', 'back', 'after', 'use', 'two', 'how', 'our', 'work', 'first', 'well', 'way', 'even',
'new', 'want', 'because', 'any', 'these', 'give', 'day', 'most', 'us', 'read', 'more');
}
/** get the tokens from a string
@author James Seng. [http://james.seng.cc/] (based on his perl version)
@return array tokens
@param string the string to get the tokens from
*/
function _getTokens($string) {
$rawtokens = array();
$tokens = array();
//$string = $this->_cleanString($string);
if (count(0 >= $this->ignore_list)) {
$this->ignore_list = $this->getIgnoreList();
}
$rawtokens = preg_split("/[\(\),:\.;\t\r\n ]/", $string, -1, PREG_SPLIT_NO_EMPTY);
// remove some tokens
while (list(, $token) = each($rawtokens)) {
$token = trim($token);
if (!(('' == $token) || (mb_strpos($token, "&") !== FALSE) || (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 $tokens;
}
/** clean a string from the diacritics
@author Antoine Bajolet [phpdig_at_toiletoine.net]
@author SPIP [http://uzine.net/spip/]
@return string clean string
@param string string with accents
*/
function _cleanString($string) {
$diac = /* A */ chr(192) . chr(193) . chr(194) . chr(195) . chr(196) . chr(197) .
/* a */ chr(224) . chr(225) . chr(226) . chr(227) . chr(228) . chr(229) .
/* O */ chr(210) . chr(211) . chr(212) . chr(213) . chr(214) . chr(216) .
/* o */ chr(242) . chr(243) . chr(244) . chr(245) . chr(246) . chr(248) .
/* E */ chr(200) . chr(201) . chr(202) . chr(203) .
/* e */ chr(232) . chr(233) . chr(234) . chr(235) .
/* Cc */ chr(199) . chr(231) .
/* I */ chr(204) . chr(205) . chr(206) . chr(207) .
/* i */ chr(236) . chr(237) . chr(238) . chr(239) .
/* U */ chr(217) . chr(218) . chr(219) . chr(220) .
/* u */ chr(249) . chr(250) . chr(251) . chr(252) .
/* yNn */ chr(255) . chr(209) . chr(241);
return strtolower(strtr($string, $diac, 'AAAAAAaaaaaaOOOOOOooooooEEEEeeeeCcIIIIiiiiUUUUuuuuyNn'));
}
}

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plugins/af_sort_bayes/lib/class.naivebayesian_ngram.php
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<?php
class NaiveBayesianNgram extends NaiveBayesian {
var $N = 2;
/**
* add Parameter for ngram
*
* @param NaiveBayesianStorage $nbs
* @param ngram's N $n
* @return boolean
*/
function __construct($nbs, $n = 2) {
parent::__construct($nbs);
$this->N = $n;
return true;
}
/**
* override method for ngram
*
* @param string $string
* @return multiple
*/
function _getTokens($string) {
$tokens = array();
if (mb_strlen($string)) {
for ($i = 0; $i < mb_strlen($string) - $this->N; $i++) {
$wd = mb_substr($string, $i, $this->N);
if (mb_strlen($wd) == $this->N) {
if (!array_key_exists($wd, $tokens)) {
$tokens[$wd] = 0;
}
$tokens[$wd]++;
}
}
}
if (count($tokens)) {
// remove empty value
$tokens = array_filter($tokens);
}
return $tokens;
}
}

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plugins/af_sort_bayes/lib/class.naivebayesianstorage.php
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<?php
/*
***** 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):
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 *****
*/
/** Access to the storage of the data for the filter.
To avoid dependency with respect to any database, this class handle all the
access to the data storage. You can provide your own class as long as
all the methods are available. The current one rely on a MySQL database.
methods:
- array getCategories()
- bool wordExists(string $word)
- array getWord(string $word, string $categoryid)
*/
class NaiveBayesianStorage {
var $con = null;
var $owner_uid = null;
var $max_document_length = 3000; // classifier can't rescale output for very long strings apparently
function NaiveBayesianStorage($owner_uid) {
$this->con = Db::get();
$this->owner_uid = $owner_uid;
return true;
}
/** get the list of categories with basic data.
@return array key = category ids, values = array(keys = 'probability', 'word_count')
*/
function getCategories() {
$categories = array();
$rs = $this->con->query('SELECT * FROM ttrss_plugin_af_sort_bayes_categories WHERE owner_uid = ' . $this->owner_uid);
while ($line = $this->con->fetch_assoc($rs)) {
$categories[$line['id']] = array('probability' => $line['probability'],
'category' => $line['category'],
'word_count' => $line['word_count']
);
}
return $categories;
}
function getCategoryByName($category) {
$rs = $this->con->query("SELECT id FROM ttrss_plugin_af_sort_bayes_categories WHERE category = '" .
$this->con->escape_string($category) . "' AND owner_uid = " . $this->owner_uid);
if ($this->con->num_rows($rs) != 0) {
return $this->con->fetch_result($rs, 0, "id");
}
return false;
}
function getCategoryById($category_id) {
$rs = $this->con->query("SELECT category FROM ttrss_plugin_af_sort_bayes_categories WHERE id = '" .
(int)$category_id . "' AND owner_uid = " . $this->owner_uid);
if ($this->con->num_rows($rs) != 0) {
return $this->con->fetch_result($rs, 0, "category");
}
return false;
}
/** see if the word is an already learnt word.
@return bool
@param string word
*/
function wordExists($word) {
$rs = $this->con->query("SELECT * FROM ttrss_plugin_af_sort_bayes_wordfreqs WHERE word='" . $this->con->escape_string($word) . "' AND
owner_uid = " . $this->owner_uid);
return $this->con->num_rows($rs) != 0;
}
/** get details of a word in a category.
@return array ('count' => count)
@param string word
@param string category id
*/
function getWord($word, $category_id) {
$details = array();
$rs = $this->con->query("SELECT * FROM ttrss_plugin_af_sort_bayes_wordfreqs WHERE word='" .
$this->con->escape_string($word) . "' AND category_id=" . (int)$category_id);
if ($this->con->num_rows($rs) == 0 ) {
$details['count'] = 0;
} else {
$details['count'] = $this->con->fetch_result($rs, 0, "count");
}
return $details;
}
/** update a word in a category.
If the word is new in this category it is added, else only the count is updated.
@return bool success
@param string word
@param int count
@paran string category id
*/
function updateWord($word, $count, $category_id) {
$oldword = $this->getWord($word, $category_id);
if (0 == $oldword['count']) {
return $this->con->query("INSERT INTO ttrss_plugin_af_sort_bayes_wordfreqs (word, category_id, count, owner_uid)
VALUES ('" . $this->con->escape_string($word) . "', '" .
(int)$category_id . "', '" .
(int)$count . "', '".
$this->owner_uid . "')");
}
else {
return $this->con->query("UPDATE ttrss_plugin_af_sort_bayes_wordfreqs SET count = count + " . (int) $count . " WHERE category_id = '" . $this->con->escape_string($category_id) . "' AND word = '" . $this->con->escape_string($word) . "'");
}
}
/** remove a word from a category.
@return bool success
@param string word
@param int count
@param string category id
*/
function removeWord($word, $count, $category_id) {
$oldword = $this->getWord($word, $category_id);
if (0 != $oldword['count'] && 0 >= ($oldword['count'] - $count)) {
return $this->con->query("DELETE FROM ttrss_plugin_af_sort_bayes_wordfreqs WHERE word='" .
$this->con->escape_string($word) . "' AND category_id='" .
$this->con->escape_string($category_id) . "'");
}
else {
return $this->con->query("UPDATE ttrss_plugin_af_sort_bayes_wordfreqs SET count = count - " .
(int) $count . " WHERE category_id = '" . $this->con->escape_string($category_id) . "'
AND word = '" . $this->con->escape_string($word) . "'");
}
}
/** update the probabilities of the categories and word count.
This function must be run after a set of training
@return bool sucess
*/
function updateProbabilities() {
// first update the word count of each category
$rs = $this->con->query("SELECT SUM(count) AS total FROM ttrss_plugin_af_sort_bayes_wordfreqs WHERE owner_uid = ".$this->owner_uid);
$total_words = $this->con->fetch_result($rs, 0, "total");
if ($total_words == 0) {
$this->con->query("UPDATE ttrss_plugin_af_sort_bayes_categories SET word_count=0, probability=0 WHERE owner_uid = " . $this->owner_uid);
return true;
}
$rs = $this->con->query("SELECT tc.id AS category_id, SUM(count) AS total FROM ttrss_plugin_af_sort_bayes_categories AS tc
LEFT JOIN ttrss_plugin_af_sort_bayes_wordfreqs AS tw ON (tc.id = tw.category_id) WHERE tc.owner_uid = ".$this->owner_uid." GROUP BY tc.id");
while ($line = $this->con->fetch_assoc($rs)) {
$proba = (int)$line['total'] / $total_words;
$this->con->query("UPDATE ttrss_plugin_af_sort_bayes_categories SET word_count=" . (int) $line['total'] .
", probability=" . $proba . " WHERE id = '" . $line['category_id'] . "'");
}
return true;
}
/** save a reference in the database.
@return bool success
@param string reference if, must be unique
@param string category id
@param string content of the reference
*/
function saveReference($doc_id, $category_id, $content) {
return $this->con->query("INSERT INTO ttrss_plugin_af_sort_bayes_references (document_id, category_id, owner_uid) VALUES
('" . $this->con->escape_string($doc_id) . "', '" .
(int)$category_id . "', " .
(int)$this->owner_uid . ")");
}
/** get a reference from the database.
@return array reference( category_id => ...., content => ....)
@param string id
*/
function getReference($doc_id, $include_content = true)
{
$ref = array();
$rs = $this->con->query("SELECT * FROM ttrss_plugin_af_sort_bayes_references WHERE document_id='" .
$this->con->escape_string($doc_id) . "' AND owner_uid = " . $this->owner_uid);
if ($this->con->num_rows($rs) == 0) {
return $ref;
}
$ref['category_id'] = $this->con->fetch_result($rs, 0, 'category_id');
$ref['id'] = $this->con->fetch_result($rs, 0, 'id');
$ref['document_id'] = $this->con->fetch_result($rs, 0, 'document_id');
if ($include_content) {
$rs = $this->con->query("SELECT content, title FROM ttrss_entries WHERE guid = '" .
$this->con->escape_string($ref['document_id']) . "'");
if ($this->con->num_rows($rs) != 0) {
$ref['content'] = mb_substr(mb_strtolower($this->con->fetch_result($rs, 0, 'title') . ' ' . strip_tags($this->con->fetch_result($rs, 0, 'content'))), 0,
$this->max_document_length);
}
}
return $ref;
}
/** remove a reference from the database
@return bool sucess
@param string reference id
*/
function removeReference($doc_id) {
return $this->con->query("DELETE FROM ttrss_plugin_af_sort_bayes_references WHERE document_id='" . $this->con->escape_string($doc_id) . "' AND owner_uid = " . $this->owner_uid);
}
}

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