bestFitClass.php in Loft Data Grids 7.2

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vendor/phpoffice/phpexcel/Classes/PHPExcel/Shared/trend/bestFitClass.php

<?php

/**
 * PHPExcel
 *
 * Copyright (c) 2006 - 2014 PHPExcel
 *
 * This library is free software; you can redistribute it and/or
 * modify it under the terms of the GNU Lesser General Public
 * License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 *
 * This library is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with this library; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
 *
 * @category   PHPExcel
 * @package    PHPExcel_Shared_Trend
 * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
 * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt	LGPL
 * @version    ##VERSION##, ##DATE##
 */

/**
 * PHPExcel_Best_Fit
 *
 * @category   PHPExcel
 * @package    PHPExcel_Shared_Trend
 * @copyright  Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
 */
class PHPExcel_Best_Fit {

  /**
   * Indicator flag for a calculation error
   *
   * @var	boolean
   **/
  protected $_error = False;

  /**
   * Algorithm type to use for best-fit
   *
   * @var	string
   **/
  protected $_bestFitType = 'undetermined';

  /**
   * Number of entries in the sets of x- and y-value arrays
   *
   * @var	int
   **/
  protected $_valueCount = 0;

  /**
   * X-value dataseries of values
   *
   * @var	float[]
   **/
  protected $_xValues = array();

  /**
   * Y-value dataseries of values
   *
   * @var	float[]
   **/
  protected $_yValues = array();

  /**
   * Flag indicating whether values should be adjusted to Y=0
   *
   * @var	boolean
   **/
  protected $_adjustToZero = False;

  /**
   * Y-value series of best-fit values
   *
   * @var	float[]
   **/
  protected $_yBestFitValues = array();
  protected $_goodnessOfFit = 1;
  protected $_stdevOfResiduals = 0;
  protected $_covariance = 0;
  protected $_correlation = 0;
  protected $_SSRegression = 0;
  protected $_SSResiduals = 0;
  protected $_DFResiduals = 0;
  protected $_F = 0;
  protected $_slope = 0;
  protected $_slopeSE = 0;
  protected $_intersect = 0;
  protected $_intersectSE = 0;
  protected $_Xoffset = 0;
  protected $_Yoffset = 0;
  public function getError() {
    return $this->_error;
  }

  //	function getBestFitType()
  public function getBestFitType() {
    return $this->_bestFitType;
  }

  //	function getBestFitType()

  /**
   * Return the Y-Value for a specified value of X
   *
   * @param	 float		$xValue			X-Value
   * @return	 float						Y-Value
   */
  public function getValueOfYForX($xValue) {
    return False;
  }

  //	function getValueOfYForX()

  /**
   * Return the X-Value for a specified value of Y
   *
   * @param	 float		$yValue			Y-Value
   * @return	 float						X-Value
   */
  public function getValueOfXForY($yValue) {
    return False;
  }

  //	function getValueOfXForY()

  /**
   * Return the original set of X-Values
   *
   * @return	 float[]				X-Values
   */
  public function getXValues() {
    return $this->_xValues;
  }

  //	function getValueOfXForY()

  /**
   * Return the Equation of the best-fit line
   *
   * @param	 int		$dp		Number of places of decimal precision to display
   * @return	 string
   */
  public function getEquation($dp = 0) {
    return False;
  }

  //	function getEquation()

  /**
   * Return the Slope of the line
   *
   * @param	 int		$dp		Number of places of decimal precision to display
   * @return	 string
   */
  public function getSlope($dp = 0) {
    if ($dp != 0) {
      return round($this->_slope, $dp);
    }
    return $this->_slope;
  }

  //	function getSlope()

  /**
   * Return the standard error of the Slope
   *
   * @param	 int		$dp		Number of places of decimal precision to display
   * @return	 string
   */
  public function getSlopeSE($dp = 0) {
    if ($dp != 0) {
      return round($this->_slopeSE, $dp);
    }
    return $this->_slopeSE;
  }

  //	function getSlopeSE()

  /**
   * Return the Value of X where it intersects Y = 0
   *
   * @param	 int		$dp		Number of places of decimal precision to display
   * @return	 string
   */
  public function getIntersect($dp = 0) {
    if ($dp != 0) {
      return round($this->_intersect, $dp);
    }
    return $this->_intersect;
  }

  //	function getIntersect()

  /**
   * Return the standard error of the Intersect
   *
   * @param	 int		$dp		Number of places of decimal precision to display
   * @return	 string
   */
  public function getIntersectSE($dp = 0) {
    if ($dp != 0) {
      return round($this->_intersectSE, $dp);
    }
    return $this->_intersectSE;
  }

  //	function getIntersectSE()

  /**
   * Return the goodness of fit for this regression
   *
   * @param	 int		$dp		Number of places of decimal precision to return
   * @return	 float
   */
  public function getGoodnessOfFit($dp = 0) {
    if ($dp != 0) {
      return round($this->_goodnessOfFit, $dp);
    }
    return $this->_goodnessOfFit;
  }

  //	function getGoodnessOfFit()
  public function getGoodnessOfFitPercent($dp = 0) {
    if ($dp != 0) {
      return round($this->_goodnessOfFit * 100, $dp);
    }
    return $this->_goodnessOfFit * 100;
  }

  //	function getGoodnessOfFitPercent()

  /**
   * Return the standard deviation of the residuals for this regression
   *
   * @param	 int		$dp		Number of places of decimal precision to return
   * @return	 float
   */
  public function getStdevOfResiduals($dp = 0) {
    if ($dp != 0) {
      return round($this->_stdevOfResiduals, $dp);
    }
    return $this->_stdevOfResiduals;
  }

  //	function getStdevOfResiduals()
  public function getSSRegression($dp = 0) {
    if ($dp != 0) {
      return round($this->_SSRegression, $dp);
    }
    return $this->_SSRegression;
  }

  //	function getSSRegression()
  public function getSSResiduals($dp = 0) {
    if ($dp != 0) {
      return round($this->_SSResiduals, $dp);
    }
    return $this->_SSResiduals;
  }

  //	function getSSResiduals()
  public function getDFResiduals($dp = 0) {
    if ($dp != 0) {
      return round($this->_DFResiduals, $dp);
    }
    return $this->_DFResiduals;
  }

  //	function getDFResiduals()
  public function getF($dp = 0) {
    if ($dp != 0) {
      return round($this->_F, $dp);
    }
    return $this->_F;
  }

  //	function getF()
  public function getCovariance($dp = 0) {
    if ($dp != 0) {
      return round($this->_covariance, $dp);
    }
    return $this->_covariance;
  }

  //	function getCovariance()
  public function getCorrelation($dp = 0) {
    if ($dp != 0) {
      return round($this->_correlation, $dp);
    }
    return $this->_correlation;
  }

  //	function getCorrelation()
  public function getYBestFitValues() {
    return $this->_yBestFitValues;
  }

  //	function getYBestFitValues()
  protected function _calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const) {
    $SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;
    foreach ($this->_xValues as $xKey => $xValue) {
      $bestFitY = $this->_yBestFitValues[$xKey] = $this
        ->getValueOfYForX($xValue);
      $SSres += ($this->_yValues[$xKey] - $bestFitY) * ($this->_yValues[$xKey] - $bestFitY);
      if ($const) {
        $SStot += ($this->_yValues[$xKey] - $meanY) * ($this->_yValues[$xKey] - $meanY);
      }
      else {
        $SStot += $this->_yValues[$xKey] * $this->_yValues[$xKey];
      }
      $SScov += ($this->_xValues[$xKey] - $meanX) * ($this->_yValues[$xKey] - $meanY);
      if ($const) {
        $SSsex += ($this->_xValues[$xKey] - $meanX) * ($this->_xValues[$xKey] - $meanX);
      }
      else {
        $SSsex += $this->_xValues[$xKey] * $this->_xValues[$xKey];
      }
    }
    $this->_SSResiduals = $SSres;
    $this->_DFResiduals = $this->_valueCount - 1 - $const;
    if ($this->_DFResiduals == 0.0) {
      $this->_stdevOfResiduals = 0.0;
    }
    else {
      $this->_stdevOfResiduals = sqrt($SSres / $this->_DFResiduals);
    }
    if ($SStot == 0.0 || $SSres == $SStot) {
      $this->_goodnessOfFit = 1;
    }
    else {
      $this->_goodnessOfFit = 1 - $SSres / $SStot;
    }
    $this->_SSRegression = $this->_goodnessOfFit * $SStot;
    $this->_covariance = $SScov / $this->_valueCount;
    $this->_correlation = ($this->_valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->_valueCount * $sumX2 - pow($sumX, 2)) * ($this->_valueCount * $sumY2 - pow($sumY, 2)));
    $this->_slopeSE = $this->_stdevOfResiduals / sqrt($SSsex);
    $this->_intersectSE = $this->_stdevOfResiduals * sqrt(1 / ($this->_valueCount - $sumX * $sumX / $sumX2));
    if ($this->_SSResiduals != 0.0) {
      if ($this->_DFResiduals == 0.0) {
        $this->_F = 0.0;
      }
      else {
        $this->_F = $this->_SSRegression / ($this->_SSResiduals / $this->_DFResiduals);
      }
    }
    else {
      if ($this->_DFResiduals == 0.0) {
        $this->_F = 0.0;
      }
      else {
        $this->_F = $this->_SSRegression / $this->_DFResiduals;
      }
    }
  }

  //	function _calculateGoodnessOfFit()
  protected function _leastSquareFit($yValues, $xValues, $const) {

    // calculate sums
    $x_sum = array_sum($xValues);
    $y_sum = array_sum($yValues);
    $meanX = $x_sum / $this->_valueCount;
    $meanY = $y_sum / $this->_valueCount;
    $mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
    for ($i = 0; $i < $this->_valueCount; ++$i) {
      $xy_sum += $xValues[$i] * $yValues[$i];
      $xx_sum += $xValues[$i] * $xValues[$i];
      $yy_sum += $yValues[$i] * $yValues[$i];
      if ($const) {
        $mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
        $mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
      }
      else {
        $mBase += $xValues[$i] * $yValues[$i];
        $mDivisor += $xValues[$i] * $xValues[$i];
      }
    }

    // calculate slope
    //		$this->_slope = (($this->_valueCount * $xy_sum) - ($x_sum * $y_sum)) / (($this->_valueCount * $xx_sum) - ($x_sum * $x_sum));
    $this->_slope = $mBase / $mDivisor;

    // calculate intersect
    //		$this->_intersect = ($y_sum - ($this->_slope * $x_sum)) / $this->_valueCount;
    if ($const) {
      $this->_intersect = $meanY - $this->_slope * $meanX;
    }
    else {
      $this->_intersect = 0;
    }
    $this
      ->_calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $const);
  }

  //	function _leastSquareFit()

  /**
   * Define the regression
   *
   * @param	float[]		$yValues	The set of Y-values for this regression
   * @param	float[]		$xValues	The set of X-values for this regression
   * @param	boolean		$const
   */
  function __construct($yValues, $xValues = array(), $const = True) {

    //	Calculate number of points
    $nY = count($yValues);
    $nX = count($xValues);

    //	Define X Values if necessary
    if ($nX == 0) {
      $xValues = range(1, $nY);
      $nX = $nY;
    }
    elseif ($nY != $nX) {

      //	Ensure both arrays of points are the same size
      $this->_error = True;
      return False;
    }
    $this->_valueCount = $nY;
    $this->_xValues = $xValues;
    $this->_yValues = $yValues;
  }

}

//	class bestFit

Classes

Name	Description
PHPExcel_Best_Fit	PHPExcel_Best_Fit