PHP实现简单线性回归之数学库的重要性_PHP

<CODE class=section> 

<?php  

// Copyright 2003, Paul Meagher  

// Distributed under GPL    

class SimpleLinearRegression {  

  var $n;  

  var $X = array(); 

  var $Y = array();   

  var $ConfInt;   

  var $Alpha; 

  var $XMean; 

  var $YMean; 

  var $SumXX; 

  var $SumXY; 

  var $SumYY;   

  var $Slope; 

  var $YInt;   

  var $PredictedY   = array(); 

  var $Error        = array(); 

  var $SquaredError = array(); 

  var $TotalError;   

  var $SumError; 

  var $SumSquaredError;   

  var $ErrorVariance; 

  var $StdErr; 

  var $SlopeStdErr;   

  var $SlopeVal;   // T value of Slope  

  var $YIntStdErr;     

  var $YIntTVal;   // T value for Y Intercept 

  var $R; 

  var $RSquared;     

  var $DF;         // Degrees of Freedom 

  var $SlopeProb;  // Probability of Slope Estimate 

  var $YIntProb;   // Probability of Y Intercept Estimate 

  var $AlphaTVal;  // T Value for given alpha setting 

  var $ConfIntOfSlope;         

   

  var $RPath  = "/usr/local/bin/R";  // Your path here 

   

  var $format = "
.2f"; // Used for formatting output  

   

} 

?> 

</CODE>

<CODE class=section> 

<?php  

// Copyright 2003, Paul Meagher  

// Distributed under GPL    

function SimpleLinearRegression($X, $Y, $ConfidenceInterval="95") { 

  $numX = count($X); 

  $numY = count($Y); 

  if ($numX != $numY) { 

    die("Error: Size of X and Y vectors must be the same."); 

  }  

  if ($numX <= 1) {  

    die("Error: Size of input array must be at least 2."); 

  } 

   

  $this->n               = $numX; 

  $this->X               = $X; 

  $this->Y               = $Y;   

   

  $this->ConfInt         = $ConfidenceInterval;  

  $this->Alpha           = (1   ($this->ConfInt / 100) ) / 2; 

  $this->XMean           = $this->getMean($this->X); 

  $this->YMean           = $this->getMean($this->Y); 

  $this->SumXX           = $this->getSumXX(); 

  $this->SumYY           = $this->getSumYY(); 

  $this->SumXY           = $this->getSumXY();     

  $this->Slope           = $this->getSlope(); 

  $this->YInt            = $this->getYInt(); 

  $this->PredictedY      = $this->getPredictedY(); 

  $this->Error           = $this->getError(); 

  $this->SquaredError    = $this->getSquaredError(); 

  $this->SumError        = $this->getSumError(); 

  $this->TotalError      = $this->getTotalError();     

  $this->SumSquaredError = $this->getSumSquaredError(); 

  $this->ErrorVariance   = $this->getErrorVariance(); 

  $this->StdErr          = $this->getStdErr();   

  $this->SlopeStdErr     = $this->getSlopeStdErr();      

  $this->YIntStdErr      = $this->getYIntStdErr();          

  $this->SlopeTVal       = $this->getSlopeTVal();             

  $this->YIntTVal        = $this->getYIntTVal();                 

  $this->R               = $this->getR();    

  $this->RSquared        = $this->getRSquared(); 

  $this->DF              = $this->getDF();           

  $this->SlopeProb       = $this->getStudentProb($this->SlopeTVal, $this->DF); 

  $this->YIntProb        = $this->getStudentProb($this->YIntTVal, $this->DF); 

  $this->AlphaTVal       = $this->getInverseStudentProb($this->Alpha, $this->DF); 

  $this->ConfIntOfSlope  = $this->getConfIntOfSlope();  

  return true; 

} 

?> 

</CODE>

<CODE class=section> 

$PredictedY[$i] = $YIntercept   $Slope * $X[$i] 

</CODE>

<CODE class=section> 

<?php  

// Copyright 2003, Paul Meagher  

// Distributed under GPL    

class SimpleLinearRegression {  

    

  var $RPath  = "/usr/local/bin/R";  // Your path here 

  function getStudentProb($T, $df) {     

    $Probability = 0.0;    

    $cmd = "echo 'dt($T, $df)' | $this->RPath --slave";  

    $result = shell_exec($cmd);     

    list($LineNumber, $Probability) = explode(" ", trim($result));  

    return $Probability; 

  } 

  function getInverseStudentProb($alpha, $df) {   

    $InverseProbability = 0.0;  

    $cmd = "echo 'qt($alpha, $df)' | $this->RPath --slave";  

    $result = shell_exec($cmd);   

    list($LineNumber, $InverseProbability) = explode(" ", trim($result));  

    return $InverseProbability; 

  } 

} 

?></CODE>

<CODE class=section> 

<?php  

// BurnoutStudy.php 

// Copyright 2003, Paul Meagher  

// Distributed under GPL    

include "SimpleLinearRegression.php";  

// Load data from burnout study  

$Concentration   = array(20,60,38,88,79,87,  

                         68,12,35,70,80,92,  

                         77,86,83,79,75,81,  

                         75,77,77,77,17,85,96);   

                           

$ExhaustionIndex = array(100,525,300,980,310,900,  

                         410,296,120,501,920,810,  

                         506,493,892,527,600,855,  

                         709,791,718,684,141,400,970);   

                           

$slr = new SimpleLinearRegression($Concentration, $ExhaustionIndex);   

$YInt      = sprintf($slr->format, $slr->YInt); 

$Slope     = sprintf($slr->format, $slr->Slope);     

$SlopeTVal = sprintf($slr->format, $slr->SlopeTVal);     

$SlopeProb = sprintf("
.6f", $slr->SlopeProb);     

?> 

<table border='1' cellpadding='5'> 

  <tr> 

    <th align='right'>Equation:</th> 

    <td></td> 

  </tr> 

  <tr> 

    <th align='right'>T:</th> 

    <td></td> 

  </tr> 

  <tr> 

    <th align='right'>Prob > T:</th> 

    <td><td> 

  </tr> 

</table> 

</CODE>