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<?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