PHP实战：PHP机器学习库php-ml的简单测试和使用方法

<?php
include_once './src/jpgraph.php';
include_once './src/jpgraph_line.php';
$g = new Graph(1920,1080);//jpgraph的绘制操作
$g->SetScale("textint");
$g->title->Set('data');
//文件的处理
$file = fopen('data.txt','r');
$labels = array();
while(!feof($file)){
 $data = explode(' ',fgets($file));  
 $data[1] = str_replace(',','.',$data[1]);//数据处理,将数据中的逗号修正为小数点
 $labels[(int)$data[0]] = (float)$data[1];//这里将数据以键值的方式存入数组,方便我们根据键来排序
} 
ksort($labels);//按键的大小排序
$x = array();//x轴的表示数据
$y = array();//y轴的表示数据
foreach($labels as $key=>$value){
 array_push($x,$key);
 array_push($y,$value);
}
$linePlot = new LinePlot($y);
$g->xaxis->SetTickLabels($x); 
$linePlot->SetLegend('data');
$g->Add($linePlot);
$g->Stroke();

<?php
 require 'vendor/autoload.php';
 use Phpml\Regression\LeastSquares;
 use Phpml\ModelManager;
 $file = fopen('data.txt','r');
 $samples = array();
 $labels = array();
 $i = 0;
 while(!feof($file)){
  $data = explode(' ',fgets($file));
  $samples[$i][0] = (int)$data[0];
  $data[1] = str_replace(',','.',$data[1]);
  $labels[$i] = (float)$data[1];
  $i ++;
 } 
 fclose($file);
 $regression = new LeastSquares();
 $regression->train($samples,$labels);
 //这个a数组是根据我们对原数据处理后的x值给出的,做测试用.
 $a = [0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20,22,23,24,25,26,27,29,30,31,37,40,41,45,48,53,55,57,60,61,108,124];
 for($i = 0; $i < count($a); $i ++){
  file_put_contents("putput.txt",($regression->predict([$a[$i]]))."\n",FILE_APPEND); //以追加的方式存入文件  
 }

<?php
include_once './src/jpgraph.php';
include_once './src/jpgraph_line.php';
$g = new Graph(1920,1080);
$g->SetScale("textint");
$g->title->Set('data');
$file = fopen('putput.txt','r');
$y = array();
$i = 0;
while(!feof($file)){
 $y[$i] = (float)(fgets($file));
 $i ++;   
} 
$x = [0,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20,22,23,24,25,26,27,29,30,31,37,40,41,45,48,53,55,57,60,61,108,124];
$linePlot = new LinePlot($y);
$g->xaxis->SetTickLabels($x); 
$linePlot->SetLegend('data');
$g->Add($linePlot);
$g->Stroke();

<?php
require 'vendor/autoload.php';
use Phpml\Classification\SVC;
use Phpml\SupportVectorMachine\Kernel;
use Phpml\Dataset\CsvDataset;
$dataset = new CsvDataset('Iris.csv' , 4, false);
$classifier = new SVC(Kernel::LINEAR,$cost = 1000);
$classifier->train($dataset->getSamples(),$dataset->getTargets());
echo $classifier->predict([$argv[1],$argv[2],$argv[3],$argv[4]]);//$argv是命令行参数,调试这种程序使用命令行较方便