本文内容全部来自《集体智慧编程》一书，原书采用的是python，因为没有python编程环境，所以用PHP实现
PHP代码
- <?php
- //filename:test_collecting_preferences
- //数据和代码来自《集体智慧编程》
- //原文采用python实现，尝试用PHP进行转换
- //@description 搜集用户偏好寻找相近用户
- $datalist = array(
- 'Lisa Rose' => array(
- 'Lady in the Water' => 2.5,
- 'Snake on a Plane' => 3.5,
- 'Just My Luck' => 3.0,
- 'Superman Returns' => 3.5,
- 'You, Me and Dupree' => 2.5,
- 'The Night Listener'=> 3.0
- ),
- 'Gene Seymour' => array(
- 'Lady in the Water' => 3.0,
- 'Snake on a Plane' => 3.5,
- 'Just My Luck' => 1.5,
- 'Superman Returns' => 5.0,
- 'You, Me and Dupree' => 3.5,
- 'The Night Listener'=> 3.0
- ),
- 'Michael Phillips' => array(
- 'Lady in the Water' => 2.5,
- 'Snake on a Plane' => 3.0,
- 'Superman Returns' => 3.5,
- 'The Night Listener'=> 4.0
- ),
- 'Claudia Puig' => array(
- 'Snake on a Plane' => 3.5,
- 'Just My Luck' =>3.0,
- 'Superman Returns' => 4.0,
- 'You, Me and Dupree' => 2.5,
- 'The Night Listener'=>4.5
- ),
- 'Mick LaSalle' => array(
- 'Lady in the Water' => 3.0,
- 'Snake on a Plane' => 4.0,
- 'Just My Luck' => 2.0,
- 'Superman Returns' => 3.0,
- 'You, Me and Dupree' => 2.0,
- 'The Night Listener'=> 3.0
- ),
- 'Jack Matthews' => array(
- 'Lady in the Water' => 3.0,
- 'Snake on a Plane' => 4.0,
- 'Superman Returns' => 5.0,
- 'You, Me and Dupree' => 3.5,
- 'The Night Listener'=> 3.0
- ),
- 'Toby' => array(
- 'Snake on a Plane' => 4.5,
- 'Superman Returns' => 4.0,
- 'You, Me and Dupree' => 1.0,
- ),
- );
- //欧几里德距离
- //它以经过人们的一致评价的物品为坐标轴，然后将参与评价的人绘制到图上，并考查他们彼此间的距离远近。
- //偏好越相似的人，距离越近。不过我们还需要一个函数来对偏好越相近的情况给出越大的值，
- //为此我们可以将函数值加1（这样可以避免遇到被零整除的错误），并取其倒数
- //公式是 1 / (1 + sqrt ( pow( data[a][1] - data[b][1] .... ) ))
- function sim_distance ( $datalist , $person1 , $person2)
- {
- $si = array();
- foreach ( $datalist[$person1] as $moviename => $grade ){
- if( array_key_exists( $moviename, $datalist[$person2] )){
- $si[$moviename] = 1;
- }
- }
- if( emptyempty( $si )){
- return 0;
- }
- $powers = 0;
- foreach ( $si as $moviename=>$val ){
- $powers += pow( ($datalist[$person1][$moviename] - $datalist[$person2][$moviename] ), 2 );//两者影评分数相减的平方值
- }
- return 1 / (1+ sqrt($powers));
- }
- //测试 'Lisa Rose' 和 'Gene Seymour' 的相似度评价
- //原书上求出来是 0.29429805508554946 , PHP 的结果是 0.29429805508555，默认精度没有python高
- echo( sim_distance( $datalist , 'Lisa Rose' , 'Gene Seymour') );
- echo( '<br/>' );
- //皮尔逊相关系数
- //该相关系统是判断两组数据与某一直线拟合程序的一种度量。对应的公司比欧几里德距离评价的计算公式要复杂
- //但是它在数据不是很规范时（如影评者对影片的评价总是相对于平均水平偏离很大），会倾向于给出更好的结果
- //皮尔逊相关度评价法首先会找出两位评论者都曾评过的物品
- //计算两者的评分总和与平方和，并求得评分的乘积之和，最后，利用这个结果计算出相关系数
- function sim_person ( $datalist ,$person1 , $person2)
- {
- $si = array();
- foreach ( $datalist[$person1] as $moviename => $grade ){
- if( array_key_exists( $moviename, $datalist[$person2] )){
- $si[$moviename] = 1;
- }
- }
- if( emptyempty( $si )){
- return 1;
- }
- $n = count( $si );
- $sum1 = $sum1Sq = $sum2 = $sum2Sq = $pSum = 0;
- foreach ( $si as $moviename => $val ){
- $sum1 += $datalist[$person1][$moviename]; //个人影评分数累加
- $sum1Sq += pow( $datalist[$person1][$moviename], 2 );//个人影评分数平方的累加
- $sum2 += $datalist[$person2][$moviename];
- $sum2Sq += pow( $datalist[$person2][$moviename], 2 );
- $pSum += ( $datalist[$person1][$moviename] * $datalist[$person2][$moviename]);//两人影评之乘积
- }
- $num = $pSum - ( $sum1 * $sum2 / $n); // 正常情况下，我怎么都觉得这是1吧？
- $den = sqrt( ( $sum1Sq - pow( $sum1, 2 ) / $n) * ( $sum2Sq - pow( $sum2, 2 ) / $n) );
- if ( $den == 0 ){
- return 0;
- }
- return ($num / $den );
- }
- //继续测试 'Lisa Rose' 和 'Gene Seymour' 的相似度评价
- //原书上求出来是 0.396059017191 , PHP 的结果是 0.39605901719067，这回。。。位数超过了python
- echo( sim_person( $datalist , 'Lisa Rose' , 'Gene Seymour') );
- ?>
有点长，随便看看吧
