<?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

 */





/**

 * 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;

	}	//	function __construct()



}	//	class bestFit

