<?php
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								require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php';
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								/**
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								 * PHPExcel_Power_Best_Fit
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								 *
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								 * Copyright (c) 2006 - 2015 PHPExcel
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								 *
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								 * This library is free software; you can redistribute it and/or
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								 * modify it under the terms of the GNU Lesser General Public
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								 * License as published by the Free Software Foundation; either
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								 * version 2.1 of the License, or (at your option) any later version.
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								 *
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								 * This library is distributed in the hope that it will be useful,
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								 * but WITHOUT ANY WARRANTY; without even the implied warranty of
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								 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
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								 * Lesser General Public License for more details.
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								 *
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								 * You should have received a copy of the GNU Lesser General Public
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								 * License along with this library; if not, write to the Free Software
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								 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
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								 *
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								 * @category   PHPExcel
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								 * @package    PHPExcel_Shared_Trend
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								 * @copyright  Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
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								 * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt    LGPL
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								 * @version    ##VERSION##, ##DATE##
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								 */
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								class PHPExcel_Power_Best_Fit extends PHPExcel_Best_Fit
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								{
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								    /**
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								     * Algorithm type to use for best-fit
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								     * (Name of this trend class)
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								     *
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								     * @var    string
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								     **/
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								    protected $bestFitType        = 'power';
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								    /**
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								     * Return the Y-Value for a specified value of X
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								     *
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								     * @param     float        $xValue            X-Value
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								     * @return     float                        Y-Value
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								     **/
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								    public function getValueOfYForX($xValue)
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								    {
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								        return $this->getIntersect() * pow(($xValue - $this->xOffset), $this->getSlope());
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								    }
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								    /**
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								     * Return the X-Value for a specified value of Y
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								     *
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								     * @param     float        $yValue            Y-Value
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								     * @return     float                        X-Value
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								     **/
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								    public function getValueOfXForY($yValue)
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								    {
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								        return pow((($yValue + $this->yOffset) / $this->getIntersect()), (1 / $this->getSlope()));
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								    }
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								    /**
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								     * Return the Equation of the best-fit line
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								     *
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								     * @param     int        $dp        Number of places of decimal precision to display
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								     * @return     string
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								     **/
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								    public function getEquation($dp = 0)
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								    {
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								        $slope = $this->getSlope($dp);
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								        $intersect = $this->getIntersect($dp);
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								        return 'Y = ' . $intersect . ' * X^' . $slope;
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								    }
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								    /**
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								     * Return the Value of X where it intersects Y = 0
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								     *
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								     * @param     int        $dp        Number of places of decimal precision to display
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								     * @return     string
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								     **/
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								    public function getIntersect($dp = 0)
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								    {
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								        if ($dp != 0) {
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								            return round(exp($this->intersect), $dp);
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								        }
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								        return exp($this->intersect);
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								    }
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								    /**
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								     * Execute the regression and calculate the goodness of fit for a set of X and Y data values
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								     *
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								     * @param     float[]    $yValues    The set of Y-values for this regression
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								     * @param     float[]    $xValues    The set of X-values for this regression
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								     * @param     boolean    $const
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								     */
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								    private function powerRegression($yValues, $xValues, $const)
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								    {
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								        foreach ($xValues as &$value) {
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								            if ($value < 0.0) {
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								                $value = 0 - log(abs($value));
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								            } elseif ($value > 0.0) {
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								                $value = log($value);
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								            }
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								        }
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								        unset($value);
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								        foreach ($yValues as &$value) {
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								            if ($value < 0.0) {
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								                $value = 0 - log(abs($value));
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								            } elseif ($value > 0.0) {
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								                $value = log($value);
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								            }
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								        }
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								        unset($value);
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								        $this->leastSquareFit($yValues, $xValues, $const);
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								    }
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								    /**
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								     * Define the regression and calculate the goodness of fit for a set of X and Y data values
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								     *
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								     * @param     float[]    $yValues    The set of Y-values for this regression
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								     * @param     float[]    $xValues    The set of X-values for this regression
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								     * @param     boolean    $const
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								     */
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								    public function __construct($yValues, $xValues = array(), $const = true)
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								    {
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								        if (parent::__construct($yValues, $xValues) !== false) {
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								            $this->powerRegression($yValues, $xValues, $const);
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								        }
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								    }
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								}
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