为了预测语句的类型，这个算法应该计算所给定语句的两个类型的概率。像上面一样，我们定义一个P（Type,sentence）。得出概率高的类型将会是Classifier类中算法返回的结果。
为了计算P（Type,sentence）,算法当中将用到贝叶斯定理。算法像这样被定义：P（Type,sentence）= P（Type）* P（sentence,Type）/ P（sentence）。这意味着给定语句的类型概率和给定类型语句概率除以语句的概率的结果是相同的。
那么算法在计算每一个相同语句的P（Tyoe,sentence），P（sentence）是保持一样的。这意味着算法就可以省略其他因素，我们只需要关心最高的概率而不是实际的值。计算就像这样：P（Type,sentence） = P（Type）* P（sentence,Type）。
最后，为了计算P（sentence,Type），我们可以为语句中的每个词添加一条链式规则。所以在一条语句中如果有n个词的话，它将会和P（word_1,Type）* P（word_2,Type）* P（word_3,Type）* .....*P（word_n,Type）是一样的。每一个词计算结果的概率使用了我们前面看到的定义。
好了，所有的都说完了，是时候在php中实际操作一下了：
function guess($statement) { $words = $this->getWords($statement); // 得到单词 $best_likelihood = 0; $best_type = null; foreach ($this->types as $type) { $likelihood = $this->pTotal($type); //计算 P(Type) foreach ($words as $word) { $likelihood *= $this->p($word, $type); // 计算 P(word, Type) } if ($likelihood > $best_likelihood) { $best_likelihood = $likelihood; $best_type = $type; } } return $best_type; }
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