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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;
}
}
}
}