<?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	bool
     **/
    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 = [];

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

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

    /**
     * Y-value series of best-fit values
     *
     * @var	float[]
     **/
    protected $_yBestFitValues = [];

    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 (0 != $dp) {
            return round($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 (0 != $dp) {
            return round($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 (0 != $dp) {
            return round($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 (0 != $dp) {
            return round($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 (0 != $dp) {
            return round($this->_goodnessOfFit, $dp);
        }

        return $this->_goodnessOfFit;
    }

    //	function getGoodnessOfFit()

    public function getGoodnessOfFitPercent($dp = 0)
    {
        if (0 != $dp) {
            return round($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 (0 != $dp) {
            return round($this->_stdevOfResiduals, $dp);
        }

        return $this->_stdevOfResiduals;
    }

    //	function getStdevOfResiduals()

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

        return $this->_SSRegression;
    }

    //	function getSSRegression()

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

        return $this->_SSResiduals;
    }

    //	function getSSResiduals()

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

        return $this->_DFResiduals;
    }

    //	function getDFResiduals()

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

        return $this->_F;
    }

    //	function getF()

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

        return $this->_covariance;
    }

    //	function getCovariance()

    public function getCorrelation($dp = 0)
    {
        if (0 != $dp) {
            return round($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 (0.0 == $this->_DFResiduals) {
            $this->_stdevOfResiduals = 0.0;
        } else {
            $this->_stdevOfResiduals = sqrt($SSres / $this->_DFResiduals);
        }
        if ((0.0 == $SStot) || ($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 (0.0 != $this->_SSResiduals) {
            if (0.0 == $this->_DFResiduals) {
                $this->_F = 0.0;
            } else {
                $this->_F = $this->_SSRegression / ($this->_SSResiduals / $this->_DFResiduals);
            }
        } else {
            if (0.0 == $this->_DFResiduals) {
                $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	bool		$const
     */
    public function __construct($yValues, $xValues = [], $const = true)
    {
        //	Calculate number of points
        $nY = count($yValues);
        $nX = count($xValues);

        //	Define X Values if necessary
        if (0 == $nX) {
            $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;
    }

    //	function __construct()
}	//	class bestFit
