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   1  <?php
   2  /**
   3   * PHPExcel
   4   *
   5   * Copyright (c) 2006 - 2014 PHPExcel
   6   *
   7   * This library is free software; you can redistribute it and/or
   8   * modify it under the terms of the GNU Lesser General Public
   9   * License as published by the Free Software Foundation; either
  10   * version 2.1 of the License, or (at your option) any later version.
  11   *
  12   * This library is distributed in the hope that it will be useful,
  13   * but WITHOUT ANY WARRANTY; without even the implied warranty of
  14   * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  15   * Lesser General Public License for more details.
  16   *
  17   * You should have received a copy of the GNU Lesser General Public
  18   * License along with this library; if not, write to the Free Software
  19   * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
  20   *
  21   * @category   PHPExcel
  22   * @package    PHPExcel_Shared_Trend
  23   * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
  24   * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt    LGPL
  25   * @version    ##VERSION##, ##DATE##
  26   */
  27  
  28  
  29  require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php';
  30  require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/JAMA/Matrix.php';
  31  
  32  
  33  /**
  34   * PHPExcel_Polynomial_Best_Fit
  35   *
  36   * @category   PHPExcel
  37   * @package    PHPExcel_Shared_Trend
  38   * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
  39   */
  40  class PHPExcel_Polynomial_Best_Fit extends PHPExcel_Best_Fit
  41  {
  42      /**
  43       * Algorithm type to use for best-fit
  44       * (Name of this trend class)
  45       *
  46       * @var    string
  47       **/
  48      protected $_bestFitType        = 'polynomial';
  49  
  50      /**
  51       * Polynomial order
  52       *
  53       * @protected
  54       * @var    int
  55       **/
  56      protected $_order            = 0;
  57  
  58  
  59      /**
  60       * Return the order of this polynomial
  61       *
  62       * @return     int
  63       **/
  64  	public function getOrder() {
  65          return $this->_order;
  66      }    //    function getOrder()
  67  
  68  
  69      /**
  70       * Return the Y-Value for a specified value of X
  71       *
  72       * @param     float        $xValue            X-Value
  73       * @return     float                        Y-Value
  74       **/
  75  	public function getValueOfYForX($xValue) {
  76          $retVal = $this->getIntersect();
  77          $slope = $this->getSlope();
  78          foreach($slope as $key => $value) {
  79              if ($value != 0.0) {
  80                  $retVal += $value * pow($xValue, $key + 1);
  81              }
  82          }
  83          return $retVal;
  84      }    //    function getValueOfYForX()
  85  
  86  
  87      /**
  88       * Return the X-Value for a specified value of Y
  89       *
  90       * @param     float        $yValue            Y-Value
  91       * @return     float                        X-Value
  92       **/
  93  	public function getValueOfXForY($yValue) {
  94          return ($yValue - $this->getIntersect()) / $this->getSlope();
  95      }    //    function getValueOfXForY()
  96  
  97  
  98      /**
  99       * Return the Equation of the best-fit line
 100       *
 101       * @param     int        $dp        Number of places of decimal precision to display
 102       * @return     string
 103       **/
 104  	public function getEquation($dp=0) {
 105          $slope = $this->getSlope($dp);
 106          $intersect = $this->getIntersect($dp);
 107  
 108          $equation = 'Y = '.$intersect;
 109          foreach($slope as $key => $value) {
 110              if ($value != 0.0) {
 111                  $equation .= ' + '.$value.' * X';
 112                  if ($key > 0) {
 113                      $equation .= '^'.($key + 1);
 114                  }
 115              }
 116          }
 117          return $equation;
 118      }    //    function getEquation()
 119  
 120  
 121      /**
 122       * Return the Slope of the line
 123       *
 124       * @param     int        $dp        Number of places of decimal precision to display
 125       * @return     string
 126       **/
 127  	public function getSlope($dp=0) {
 128          if ($dp != 0) {
 129              $coefficients = array();
 130              foreach($this->_slope as $coefficient) {
 131                  $coefficients[] = round($coefficient,$dp);
 132              }
 133              return $coefficients;
 134          }
 135          return $this->_slope;
 136      }    //    function getSlope()
 137  
 138  
 139  	public function getCoefficients($dp=0) {
 140          return array_merge(array($this->getIntersect($dp)),$this->getSlope($dp));
 141      }    //    function getCoefficients()
 142  
 143  
 144      /**
 145       * Execute the regression and calculate the goodness of fit for a set of X and Y data values
 146       *
 147       * @param    int            $order        Order of Polynomial for this regression
 148       * @param    float[]        $yValues    The set of Y-values for this regression
 149       * @param    float[]        $xValues    The set of X-values for this regression
 150       * @param    boolean        $const
 151       */
 152  	private function _polynomial_regression($order, $yValues, $xValues, $const) {
 153          // calculate sums
 154          $x_sum = array_sum($xValues);
 155          $y_sum = array_sum($yValues);
 156          $xx_sum = $xy_sum = 0;
 157          for($i = 0; $i < $this->_valueCount; ++$i) {
 158              $xy_sum += $xValues[$i] * $yValues[$i];
 159              $xx_sum += $xValues[$i] * $xValues[$i];
 160              $yy_sum += $yValues[$i] * $yValues[$i];
 161          }
 162          /*
 163           *    This routine uses logic from the PHP port of polyfit version 0.1
 164           *    written by Michael Bommarito and Paul Meagher
 165           *
 166           *    The function fits a polynomial function of order $order through
 167           *    a series of x-y data points using least squares.
 168           *
 169           */
 170          for ($i = 0; $i < $this->_valueCount; ++$i) {
 171              for ($j = 0; $j <= $order; ++$j) {
 172                  $A[$i][$j] = pow($xValues[$i], $j);
 173              }
 174          }
 175          for ($i=0; $i < $this->_valueCount; ++$i) {
 176              $B[$i] = array($yValues[$i]);
 177          }
 178          $matrixA = new Matrix($A);
 179          $matrixB = new Matrix($B);
 180          $C = $matrixA->solve($matrixB);
 181  
 182          $coefficients = array();
 183          for($i = 0; $i < $C->m; ++$i) {
 184              $r = $C->get($i, 0);
 185              if (abs($r) <= pow(10, -9)) {
 186                  $r = 0;
 187              }
 188              $coefficients[] = $r;
 189          }
 190  
 191          $this->_intersect = array_shift($coefficients);
 192          $this->_slope = $coefficients;
 193  
 194          $this->_calculateGoodnessOfFit($x_sum,$y_sum,$xx_sum,$yy_sum,$xy_sum);
 195          foreach($this->_xValues as $xKey => $xValue) {
 196              $this->_yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
 197          }
 198      }    //    function _polynomial_regression()
 199  
 200  
 201      /**
 202       * Define the regression and calculate the goodness of fit for a set of X and Y data values
 203       *
 204       * @param    int            $order        Order of Polynomial for this regression
 205       * @param    float[]        $yValues    The set of Y-values for this regression
 206       * @param    float[]        $xValues    The set of X-values for this regression
 207       * @param    boolean        $const
 208       */
 209  	function __construct($order, $yValues, $xValues=array(), $const=True) {
 210          if (parent::__construct($yValues, $xValues) !== False) {
 211              if ($order < $this->_valueCount) {
 212                  $this->_bestFitType .= '_'.$order;
 213                  $this->_order = $order;
 214                  $this->_polynomial_regression($order, $yValues, $xValues, $const);
 215                  if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) {
 216                      $this->_error = True;
 217                  }
 218              } else {
 219                  $this->_error = True;
 220              }
 221          }
 222      }    //    function __construct()
 223  
 224  }    //    class polynomialBestFit