polynomialBestFitClass.php in Loft Data Grids 6.2

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vendor/phpoffice/phpexcel/Classes/PHPExcel/Shared/trend/polynomialBestFitClass.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##
 */
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php';
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/JAMA/Matrix.php';

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

  /**
   * Algorithm type to use for best-fit
   * (Name of this trend class)
   *
   * @var	string
   **/
  protected $_bestFitType = 'polynomial';

  /**
   * Polynomial order
   *
   * @protected
   * @var	int
   **/
  protected $_order = 0;

  /**
   * Return the order of this polynomial
   *
   * @return	 int
   **/
  public function getOrder() {
    return $this->_order;
  }

  //	function getOrder()

  /**
   * Return the Y-Value for a specified value of X
   *
   * @param	 float		$xValue			X-Value
   * @return	 float						Y-Value
   **/
  public function getValueOfYForX($xValue) {
    $retVal = $this
      ->getIntersect();
    $slope = $this
      ->getSlope();
    foreach ($slope as $key => $value) {
      if ($value != 0.0) {
        $retVal += $value * pow($xValue, $key + 1);
      }
    }
    return $retVal;
  }

  //	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 ($yValue - $this
      ->getIntersect()) / $this
      ->getSlope();
  }

  //	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) {
    $slope = $this
      ->getSlope($dp);
    $intersect = $this
      ->getIntersect($dp);
    $equation = 'Y = ' . $intersect;
    foreach ($slope as $key => $value) {
      if ($value != 0.0) {
        $equation .= ' + ' . $value . ' * X';
        if ($key > 0) {
          $equation .= '^' . ($key + 1);
        }
      }
    }
    return $equation;
  }

  //	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) {
      $coefficients = array();
      foreach ($this->_slope as $coefficient) {
        $coefficients[] = round($coefficient, $dp);
      }
      return $coefficients;
    }
    return $this->_slope;
  }

  //	function getSlope()
  public function getCoefficients($dp = 0) {
    return array_merge(array(
      $this
        ->getIntersect($dp),
    ), $this
      ->getSlope($dp));
  }

  //	function getCoefficients()

  /**
   * Execute the regression and calculate the goodness of fit for a set of X and Y data values
   *
   * @param	int			$order		Order of Polynomial for this 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		$const
   */
  private function _polynomial_regression($order, $yValues, $xValues, $const) {

    // calculate sums
    $x_sum = array_sum($xValues);
    $y_sum = array_sum($yValues);
    $xx_sum = $xy_sum = 0;
    for ($i = 0; $i < $this->_valueCount; ++$i) {
      $xy_sum += $xValues[$i] * $yValues[$i];
      $xx_sum += $xValues[$i] * $xValues[$i];
      $yy_sum += $yValues[$i] * $yValues[$i];
    }

    /*
     *	This routine uses logic from the PHP port of polyfit version 0.1
     *	written by Michael Bommarito and Paul Meagher
     *
     *	The function fits a polynomial function of order $order through
     *	a series of x-y data points using least squares.
     *
     */
    for ($i = 0; $i < $this->_valueCount; ++$i) {
      for ($j = 0; $j <= $order; ++$j) {
        $A[$i][$j] = pow($xValues[$i], $j);
      }
    }
    for ($i = 0; $i < $this->_valueCount; ++$i) {
      $B[$i] = array(
        $yValues[$i],
      );
    }
    $matrixA = new Matrix($A);
    $matrixB = new Matrix($B);
    $C = $matrixA
      ->solve($matrixB);
    $coefficients = array();
    for ($i = 0; $i < $C->m; ++$i) {
      $r = $C
        ->get($i, 0);
      if (abs($r) <= pow(10, -9)) {
        $r = 0;
      }
      $coefficients[] = $r;
    }
    $this->_intersect = array_shift($coefficients);
    $this->_slope = $coefficients;
    $this
      ->_calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum);
    foreach ($this->_xValues as $xKey => $xValue) {
      $this->_yBestFitValues[$xKey] = $this
        ->getValueOfYForX($xValue);
    }
  }

  //	function _polynomial_regression()

  /**
   * Define the regression and calculate the goodness of fit for a set of X and Y data values
   *
   * @param	int			$order		Order of Polynomial for this 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		$const
   */
  function __construct($order, $yValues, $xValues = array(), $const = True) {
    if (parent::__construct($yValues, $xValues) !== False) {
      if ($order < $this->_valueCount) {
        $this->_bestFitType .= '_' . $order;
        $this->_order = $order;
        $this
          ->_polynomial_regression($order, $yValues, $xValues, $const);
        if ($this
          ->getGoodnessOfFit() < 0.0 || $this
          ->getGoodnessOfFit() > 1.0) {
          $this->_error = True;
        }
      }
      else {
        $this->_error = True;
      }
    }
  }

}

//	class polynomialBestFit

Classes

Name	Description
PHPExcel_Polynomial_Best_Fit	PHPExcel_Polynomial_Best_Fit