テノニッキ

<?php
require_once('Cache/Lite/Function.php');
class CacheTest{
var $_cacheopts;
function CacheTest(){
$this->_cacheopts = array(
"lifeTime"               => 3600,// cacheの有効時間(秒)
"hashedDirectoryLevel"   => 2,  // cache階層
"debugCacheLiteFunction" => true,// デバッグ出力 (標準出力に "Hit" or "fail"表示)
);
}
function test($param = 0){
$cache  =& new Cache_Lite_Function($this->_cacheopts);
// 自身のメソッド"process"を呼び出す。引数は$param
$result = $cache->call(array(&$this, "process"), $param);
return $result;
}
/**
  * 比較的重い処理 (1度呼び出した結果を保管したい対象の処理)
  * @param $init 
  */
function process($init){
$resp = $init;
for($i=0; $i<5000000; $i++){
$resp++;
}
return $resp;
}
}
$cachetest = new CacheTest();
// 開始時間メモ
$stime = explode(' ', microtime());
$stime = $stime[1] + $stime[0];
print "start = ".$stime."\n";
// 処理実行
$resp = $cachetest->test($argv[1]);
// 終了時間から実行時間算出
$etime = explode(' ', microtime());
$etime = $etime[1] + $etime[0];
$time   = round(($etime - $stime), 4);
printf("%f seconds\n", $time);
printf("CacheTest::test response = %s\n", $resp);
?>

な具合である。
ここでは例として、コマンドライン引数で渡した値を初期値としてそこから500万回インクリメントするという重い処理の結果をキャッシュさせている。
で、これを実行すると1回目はキャッシュが効かないので普通に実行される。

/Users/hideack/tmp% php -q test.php 10
start = 1238689332.75
Cache missed !
1.634000 seconds
CacheTest::test response = 5000010

次にもう一度同じことを実行してみる

/Users/hideack/tmp% php -q test.php 10
start = 1238689377.62
Cache hit !
0.001300 seconds
CacheTest::test response = 5000010

今度はキャッシュから引かれたので実行時間が大幅に短くなっている。
こんな具合。

あぁ、久しぶりに技術的なこと書いた。

<?php
/**
 * Naivebayes.php 
 *
 * This package was ported from Perl's Algorithm::NaiveBayes (frequency model only)
 * http://search.cpan.org/~kwilliams/Algorithm-NaiveBayes-0.04/lib/Algorithm/NaiveBayes.pm
 * 
 * @category  Algorithm 
 * @package   Naivebayes
 * @author    hideack
 * @license   http://www.php.net/license/3_01.txt The PHP License, version 3.01
 * @version   0.1 
 */
class Naivebayes{
private $modeltype;
private $instances;
private $trainingdata;
private $model;
public function __construct(){
$this->trainingdata = array(
"attributes" => array(),
"labels"     => array(),
);
$this->instances = 0;
$this->modeltype = "";	// Perl版では切り替え可能
}
public function addInstance($attributes, $label){
$this->instances++;
foreach($attributes as $keyword => $count){
if(isset($this->trainingdata['attributes'][$keyword])){
$this->trainingdata['attributes'][$keyword] += $count;
}
else{
$this->trainingdata['attributes'][$keyword] = $count;
}
}
foreach($label as $labelword){
if(isset($this->trainingdata['labels'][$labelword]['count'])){
$this->trainingdata['labels'][$labelword]['count']++;
}
else{
$this->trainingdata['labels'][$labelword]['count'] = 1;
}
foreach($attributes as $keyword => $count){
if(isset($this->trainingdata[$keyword])){
$this->trainingdata['labels'][$labelword]['attributes'][$keyword] += $count;
}
else{
$this->trainingdata['labels'][$labelword]['attributes'][$keyword] = $count;
}
}
}
}
public function train(){
$m = array();
$labels = $this->trainingdata['labels'];
$m['attributes'] = $this->trainingdata['attributes'];
$vocab_size = count($m['attributes']);
foreach($labels as $label => $info){
$m['prior_probs'][$label] = log($info['count'] / $this->instances);
$label_tokens = 0;
foreach($info['attributes'] as $word => $count){
$label_tokens += $count;
}
$m['smoother'][$label] = -log($label_tokens + $vocab_size);
$denominator = log($label_tokens + $vocab_size);
foreach($info['attributes'] as $attribute => $count){
$m['probs'][$label][$attribute] = log($count + 1) - $denominator;
}
}
$this->model = $m;
}
public function predict($newattrs){
$scores = $this->model['prior_probs'];
foreach($newattrs as $feature => $value){
foreach($this->model['probs'] as $label => $attribute){
$tmpscore = 0.0;
if($attribute[$feature] == 0.0){
$tmpscore = $this->model['smoother'][$label];
}
else{
$tmpscore = $attribute[$feature];
}
$scores[$label] += $tmpscore * $value;
}
}
$scores = $this->rescale($scores);
return $scores;
}
public function labels(){
$labels = array();
foreach($this->trainingdata['labels'] as $label => $value){
$labels[] = $label;
}
return $labels;
}
public function doPurge(){
// 未実装...
}
private function rescale($scores){
$total = 0;
$max  = max($scores);
$rescalescore = $scores;
foreach($rescalescore as $key => $val){
$val = exp($val - $max);
$total += pow($val, 2);
$rescalescore[$key] = $val;
}
$total = sqrt($total);
foreach($rescalescore as $key => $val){
$rescalescore[$key] /= $total;
}
return $rescalescore;
}
}
?>