PHP实现机器学习之朴素贝叶斯算法详解

时间:2018-01-16

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

更多关于PHP相关内容感兴趣的读者可查看本站专题:《PHP数据结构与算法教程》、《php程序设计算法总结》、《php字符串(string)用法总结》、《PHP数组(Array)操作技巧大全》、《PHP常用遍历算法与技巧总结》及《PHP数学运算技巧总结》

希望本文所述对大家PHP程序设计有所帮助。

  • 共4页:
  • 上一页
  • 4/4下一篇
    上一篇:PHP实现单链表翻转操作示例 下一篇:PHP中利用Telegram的接口实现免费的消息通知功能

    相关文章

    最新文章