php下贝叶斯算法实例

由于数据是从数据库内读取 故不可能直接照搬 大体思路是利用之前分享的贝叶斯算法实现该机制，在实现过程中发现该算法对于培养的数据要求较高，由于我自己是随意分类，所以结果很差，不过也算是对于该方法的一个实现吧，只要修改了关键词以及分类应该可以得到较好的结果。

<?php
    include_once('config1.php');
    include_once('Bayes.php');
    $sunKindrow=11;//sunkind列 正式程序改为7
    $result = mysql_query(" SELECT count(1) FROM `test`  ");//获取样本个数
    $row=mysql_fetch_array($result);
    $count = $row['count(1)'];    
    $sunkind=array();//存储各个关键词对应的各个类别的比例
    $keyword=array("编辑","工程");//关键词
    $keyprecent=array();//关键词比例
    //初始化 sunkind
    for($i=0;$i<count($keyword);$i++)
    {
        for($j=0;$j<$sunKindrow;$j++)
        {
            $sunkind[$i][$j]=0;
        }
    }
    //计算keyprecent sunkind
    for($i=0;$i<count($keyword);$i++)
    {
        //echo $keyword[$i];
        $result1 = mysql_query(" SELECT count(1) FROM `test` WHERE `title` LIKE '%" . $keyword[$i] . "%'");
        $row1 = mysql_fetch_array($result1);
        $count1 = $row1['count(1)'];    
        $keyprecent[$i] = $count1/$count;
        $result2 = mysql_query(" SELECT * FROM `test` WHERE `title` LIKE '%" . $keyword[$i] . "%'");
        while($row2 = mysql_fetch_array($result2))
        {
            $sunkind[$i][$row2['kindid']]=$sunkind[$i][$row2['kindid']]+1;
        }
        for($j=0;$j<count($sunkind[$i]);$j++)
        {
            $sunkind[$i][$j]=$sunkind[$i][$j]/$count1;
            //去除小概率数据
            if($sunkind[$i][$j]<0.1)
            $sunkind[$i][$j]=0;
        }
    }
    //以下为显示部分
    $disease_labels = array("编辑", "工程");

    // Where patient is assessed as to whether they exhibit symptom 1 and/or symptom 2
    $symptom_labels = array("0","1","2","3","4","5","6","7","8","9","10","11"); 
    $bayes = new Bayes($keyprecent, $sunkind);
    $bayes->getPosterior();

    echo "<p>";
    echo "The probability of each disease type given the presence or absence of symptoms is: ";
    echo "</p>";

    $bayes->setRowLabels($symptom_labels);    // i.e., evidence
    $bayes->setColumnLabels($disease_labels); // i.e., hypothesis
    $bayes->toHTML();

    echo "<br />";
    echo "<p>";
    echo "Here is what a dump of the Bayes object looks: ";
    echo "</p>";

    echo "<pre>";
    print_r($bayes);
    echo "</pre>";
?>

以下为所用的Bayes.php代码

<?php

/**
* Bayes
*
* @author  Paul Meagher <paul@datavore.com> 
* @license PHP License v3.0     
*
* Calculates posterior probabilities for m hypotheses and n evidence  
* alternatives.  The code was inspired by a procedural TrueBasic version 
* (Bayes.tru) bundled with Grimstead and Snell's excellent online 
* textbook "Introduction to Probability". 
*
* @see http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/book.html
*
*/

class Bayes {

  /**
  * Number of evidence alternatives (i.e., number of rows).
  */
  var $m;

  /**
  * Number of hypothesis alternatives (i.e., number of columns).
  */
  var $n;

  /**
  * Output labels for evidence alternatives.
  */
  var $row_labels = array();
  
  /**
  * Output labels for hypothesis alternatives.
  */  
  var $column_labels = array();  

  /**
  * Vector container for prior probabilities.
  */
  var $priors = array();

  /**
  * Matrix container for likelihood of evidence e given hypothesis h.
  */
  var $likelihoods = array();         

  /**
  * Matrix container for posterior probabilties.
  */
  var $posterior = array();

  /**
  * Vector container for evidence probabilties.
  */
  var $evidence = array();  
  /**
  * Initialize the Bayes algorithm by setting the priors, likelihoods and 
  * dimensions of the likelihood and posterior matrices.
  */
  function Bayes($priors, $likelihoods) {
    $this->priors = $priors;
    $this->likelihoods = $likelihoods;
    $this->m = count($this->likelihoods);  // num rows
    $this->n = count($this->likelihoods[0]); // num cols   
    return true;
  }
  
  /**
  * Output method for setting row labels prior to display.
  */
  function setRowLabels($row_labels) {
    $this->row_labels = $row_labels;
    return true;
  }

  /**
  * Output method for setting column labels prior to display.
  */
  function setColumnLabels($column_labels) {
    $this->column_labels = $column_labels;
    return true;
  }

  /**
  * Compute the posterior probability matrix given the priors and likelihoods.
  *
  * The first set of loops computes the denominator of the canonical Bayes 
  * equation. The probability appearing in the denominator serves a normalizing 
  * role in the computation - it ensures that posterior probabilities sum to 1.  
  *
  * The second set of loops: 
  *
  *   1. multiplies the prior[$h] by the likelihood[$h][$e]
  *   2. divides the result by the denominator
  *   3. assigns the result to the posterior[$e][$h] probability matrix
  */              
  function getPosterior() {
    // Find probability of evidence e
    for($e=0; $e < $this->n; $e++)
    {
        $this->evidence[$e]=0;
    }
    for($e=0; $e < $this->n; $e++) {
      for ($h=0; $h < $this->m; $h++) {
        $this->evidence[$e] += $this->priors[$h] * $this->likelihoods[$h][$e];
      }
    }
    // Find probability of hypothesis given evidence
    for($e=0; $e < $this->n; $e++) {
      for ($h=0; $h < $this->m; $h++) {     
        $this->posterior[$e][$h] = $this->priors[$h] * $this->likelihoods[$h][$e] / $this->evidence[$e];
      }
    }
    return true;    
  }
  
  /**
  * Output method for displaying posterior probability matrix
  */
  function toHTML($number_format="%01.3f") {
    ?>
    <table border='1' cellpadding='5' cellspacing='0'>
      <tr>
        <td>&nbsp;</td>
        <?php
        for ($h=0; $h < $this->m; $h++) {         
          ?>
          <td align='center'><b><?php echo $this->column_labels[$h] ?></b></td>
          <?php
        }  
        ?>
      </tr>
      <?php    
      for($e=0; $e < $this->n; $e++) {
        ?>
        <tr>
          <td><b><?php echo $this->row_labels[$e] ?></b></td>
          <?php
          for ($h=0; $h < $this->m; $h++) {     
            ?>
            <td align='right'><?php printf($number_format, $this->posterior[$e][$h]) ?></td>
            <?php
          }
          ?>
        </tr>
        <?php
      }       
      ?>  
    </table>
    <?php
  }
}

?>