这就是所有的工作,现在算法可以预测语句的类型了。你要做的就是让你的算法开始学习:
$classifier = new Classifier(); $classifier->learn('Symfony is the best', Type::POSITIVE); $classifier->learn('PhpStorm is great', Type::POSITIVE); $classifier->learn('Iltar complains a lot', Type::NEGATIVE); $classifier->learn('No Symfony is bad', Type::NEGATIVE); var_dump($classifier->guess('Symfony is great')); // string(8) "positive" var_dump($classifier->guess('I complain a lot')); // string(8) "negative"
所有的代码我已经上传到了GIT上,https://github.com/yannickl88/blog-articles/blob/master/src/machine-learning-naive-bayes/Classifier.php
github上完整php代码如下:
<?php class Type { const POSITIVE = 'positive'; const NEGATIVE = 'negative'; } class Classifier { private $types = [Type::POSITIVE, Type::NEGATIVE]; private $words = [Type::POSITIVE => [], Type::NEGATIVE => []]; private $documents = [Type::POSITIVE => 0, Type::NEGATIVE => 0]; public function guess($statement) { $words = $this->getWords($statement); // get the words $best_likelihood = 0; $best_type = null; foreach ($this->types as $type) { $likelihood = $this->pTotal($type); // calculate P(Type) foreach ($words as $word) { $likelihood *= $this->p($word, $type); // calculate P(word, Type) } if ($likelihood > $best_likelihood) { $best_likelihood = $likelihood; $best_type = $type; } } return $best_type; } public function learn($statement, $type) { $words = $this->getWords($statement); foreach ($words as $word) { if (!isset($this->words[$type][$word])) { $this->words[$type][$word] = 0; } $this->words[$type][$word]++; // increment the word count for the type } $this->documents[$type]++; // increment the document count for the type } public function p($word, $type) { $count = 0; if (isset($this->words[$type][$word])) { $count = $this->words[$type][$word]; } return ($count + 1) / (array_sum($this->words[$type]) + 1); } public function pTotal($type) { return ($this->documents[$type] + 1) / (array_sum($this->documents) + 1); } public function getWords($string) { return preg_split('/\s+/', preg_replace('/[^A-Za-z0-9\s]/', '', strtolower($string))); } } $classifier = new Classifier(); $classifier->learn('Symfony is the best', Type::POSITIVE); $classifier->learn('PhpStorm is great', Type::POSITIVE); $classifier->learn('Iltar complains a lot', Type::NEGATIVE); $classifier->learn('No Symfony is bad', Type::NEGATIVE); var_dump($classifier->guess('Symfony is great')); // string(8) "positive" var_dump($classifier->guess('I complain a lot')); // string(8) "negative"
结束语
尽管我们只进行了很少的训练,但是算法还是应该能给出相对精确的结果。在真实环境,你可以让机器学习成百上千的记录,这样就可以给出更精准的结果。你可以下载查看这篇文章(英文):朴素贝叶斯已经被证明可以给出情绪统计的结果。
而且,朴素贝叶斯不仅仅可以运用到文本类的应用。希望通过这篇文章可以拉近你和机器学习的一点点距离。
原文地址:https://stovepipe.systems/post/machine-learning-naive-bayes
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