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<?php require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php'; require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/JAMA/Matrix.php'; /** * PHPExcel_Polynomial_Best_Fit * * Copyright (c) 2006 - 2015 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 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel) * @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL * @version ##VERSION##, ##DATE## */ 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; } /** * 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; } /** * 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(); } /** * 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; } /** * 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 $this->_slope; } public function getCoefficients($dp = 0) { return array_merge(array($this->getIntersect($dp)), $this->getSlope($dp)); } /** * 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 $const */ private function polynomialRegression($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); } } /** * 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 $const */ public function __construct($order, $yValues, $xValues = array(), $const = true) { if (parent::__construct($yValues, $xValues) !== false) { if ($order < $this->valueCount) { $this->bestFitType .= '_'.$order; $this->order = $order; $this->polynomialRegression($order, $yValues, $xValues, $const); if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) { $this->_error = true; } } else { $this->_error = true; } } } }
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