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-<?php
-/**
- * PHPExcel
- *
- * Copyright (c) 2006 - 2012 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 - 2012 PHPExcel (http://www.codeplex.com/PHPExcel)
- * @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
- * @version 1.7.8, 2012-10-12
- */
-
-
-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 - 2012 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 \ No newline at end of file