source: sourcecode/application/libraries/PHPExcel/Shared/trend/polynomialBestFitClass.php @ 1

<?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    1.8.0, 2014-03-02
 */


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;
                        }
                }
        }       //      function __construct()

}       //      class polynomialBestFit