我从站点获取贝叶斯代码http://www.ibm.com/developerworks/library/wa-bayes3/
我无法从选择查询中删除未定义的错误。这是代码：
<?php
/**
* @package NaiveBayes
* @author Paul Meagher <paul@datavore.com>
* @license PHP License v3.0
* @version 0.2
*
* This class must be supplied with training example data, attribute names,
* and class names. Once this information is supplied, you can then invoke
* the learn and classify methods.
*/
class NaiveBayes {
/**
* Database table to use.
*/
var $table = null;
/**
* 1D array of attributes names. Attribute names should
* correspond to field names in the specified database
* table.
*/
var $attributes = array();
/**
* 1D array of attribute values to classify.
*/
var $attribute_values = null;
/**
* Specifies table column holding classification values.
*/
var $class = array();
/**
* Specifies allowable classification values.
*/
var $class_values = array();
/**
* 3D array containing joint frequency infomation about
* how class names and attribute names covary. Used to
* derive the likelihood array in the classify method.
*/
var $joint_frequency = array();
/**
* 1D array containing prior probability of each class name.
*/
var $priors = array();
/**
* 1D array containing likeliood of the data for each class.
*/
var $likelihoods = array();
/**
* 1D array containing the posterior probability of each class
* name given the supplied attribute values.
*/
var $posterior = array();
/**
* Denotes the number of training examples used for learning.
*/
var $n = 0;
/**
* When the classifier method is called, $predict is set to
* class name with the highest posterior probability.
*/
var $predict = null;
/**
* Set database table to use.
*/
function setTable($table)
{
$this->table = $table;
}
/**
* Set attribute columns to use. The attribute columns should
* correspond to fields in your database table.
*/
function setAttributes($columns)
{
foreach($columns as $column)
{
$this->attributes[] = $column;
}
}
/**
* Set classification column names to use.
*/
function setClass($column) {
$this->class = $column;
}
/**
* Set classification names to use.
*/
function setClassValues($values) {
foreach($values as $value) {
$this->class_values[] = $value;
}
}
function inimat($lr, $ur, $lc, $uc, &$a, $x){
for(; $lr<=$ur; $lr++)
for($j=$lc; $j<=$uc; $j++) $a[$lr][$j]=$x;
}
/**
* Learn the prior probability of each class and the
* joint frequency of each class and attribute.
*/
function learn()
{
// include connection file
include("connect.php");
// parse array
foreach ($this->attributes as $attribute)
// get field list
$field_list = substr($attribute, 0, -1);
// parse array
foreach ($this->class_values as $class) {
// make an sql query
$sql = "SELECT $field_list FROM " . $this->table . " WHERE " . $this->class . "='$class'";
// execute sql query
$result = mysql_query($sql);
$this->priors["$class"] = mysql_num_rows($result);
while ($row = mysql_fetch_assoc($result))
{
foreach ($this->attributes as $attribute) {
// if row attribute haven't any data then set default value
$attribute_value = isset($row[$attribute]) ? $row[$attribute] : 0;
$this->joint_frequency[$class][$attribute][$attribute_value]++;
}
}
}
}
/**
* Given a set of attribute values, this routine will
* predict the class they most likley belong to.
*/
function classify($attribute_values) {
$this->attribute_values = $attribute_values;
$this->n = array_sum($this->priors);
$this->max = 0;
foreach($this->class_values as $class) {
$counter = 0;
$this->likelihoods[$class] = 1;
foreach($this->attributes as $attribute) {
$attribute_value = $attribute_values[$counter];
$joint_freq = $this->joint_frequency[$class][$attribute][$attribute_value];
$likelihood = $joint_freq / $this->priors[$class];
if ($joint_freq > 0) {
$this->likelihoods[$class] = $this->likelihoods[$class] * $likelihood;
}
$counter++;
}
$prior = $this->priors[$class] / $this->n;
$this->posterior[$class] = $this->likelihoods[$class] * $prior;
if ($this->posterior[$class] > $this->max) {
$this->predict = $class;
$this->max = $this->posterior[$class];
}
}
}
/**
* Output the posterior probabilities that were computed by
* the classify method.
*/
function toHTML() {
foreach($this->class_values as $class) {
$equation = "P($this->class = ". $class ." | ";
$counter = 0;
foreach($this->attributes as $attribute) {
$attribute_value = $this->attribute_values[$counter];
$equation .= $attribute ."=". $attribute_value ." & ";
$counter++;
}
$equation = substr($equation, 0, -7);
$equation .= ") = ". $this->posterior[$class];
if ($class == $this->predict) {
$equation .= " <sup>*</sup>";
}
echo $equation ."<br />";
}
}
}
?>
它给出错误通知：未定义的索引：第 130 行 C:\wamp\www\Bayes3\NaiveBayes.php 中的 q1：$attribute_value = $row[$attribute];
注意：未定义的索引：0 在 C:\wamp\www\Bayes3\NaiveBayes.php 第 131 行：
$this->joint_frequency[$class][$attribute][$attribute_value]++;
注意：未定义的索引：C:\wamp\www\Bayes3\NaiveBayes.php 中的 150 行：
$joint_freq = $this->joint_frequency[$class][$attribute][$attribute_value];
请帮助我