bayes takes a document (piece of text), and tells you what category that document belongs to.

This library was ported from a nodejs lib @ https://github.com/ttezel/bayes

• Proven and popular classifier in nodejs - https://www.npmjs.com/package/bayes
• We kept the json serialization signature so you can simply use the learned/trained json output from both PHP and nodejs library.

You can use this for categorizing any text content into any arbitrary set of categories. For example:

• is an email spam, or not spam ?
• is a news article about technology, politics, or sports ?
• is a piece of text expressing positive emotions, or negative emotions?

composer require niiknow/bayes

$classifier = new \Niiknow\Bayes();

// teach it positive phrases

$classifier->learn('amazing, awesome movie!! Yeah!! Oh boy.', 'positive');
$classifier->learn('Sweet, this is incredibly, amazing, perfect, great!!', 'positive');

// teach it a negative phrase

$classifier->learn('terrible, shitty thing. Damn. Sucks!!', 'negative');

// now ask it to categorize a document it has never seen before

$classifier->categorize('awesome, cool, amazing!! Yay.');
// => 'positive'

// serialize the classifier's state as a JSON string.
$stateJson = $classifier->toJson();

// load the classifier back from its JSON representation.
$classifier->fromJson($stateJson);

Returns an instance of a Naive-Bayes Classifier.

Pass in an optional options object to configure the instance. If you specify a tokenizer function in options, it will be used as the instance's tokenizer.

Teach your classifier what category the text belongs to. The more you teach your classifier, the more reliable it becomes. It will use what it has learned to identify new documents that it hasn't seen before.

Returns the category it thinks text belongs to. Its judgement is based on what you have taught it with .learn().

Extract the probabilities for each known category.

Returns the JSON representation of a classifier.

Returns a classifier instance from the JSON representation. Use this with the JSON representation obtained from $classifier->toJson()

You can pass in your own tokenizer function in the constructor. Example:

// array containing stopwords
$stopwords = array("der", "die", "das", "the");

// escape the stopword array and implode with pipe
$s = '~^\W*('.implode("|", array_map("preg_quote", $stopwords)).')\W+\b|\b\W+(?1)\W*$~i';

$options['tokenizer'] = function($text) use ($s) {
            // convert everything to lowercase
            $text = mb_strtolower($text);

            // remove stop words
            $text = preg_replace($s, '', $text);

            // split the words
            preg_match_all('/[[:alpha:]]+/u', $text, $matches);

            // first match list of words
            return $matches[0];
        };

$classifier = new \niiknow\Bayes($options);