Subversion Repositories eFlore/Applications.cel

<?php

/**

 * PHPExcel

 *

 * Copyright (c) 2006 - 2013 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 - 2013 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 - 2013 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