forked from bruno/pabx-app
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
222 lines
6.8 KiB
222 lines
6.8 KiB
<?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; |
|
} |
|
} |
|
} |
|
}
|
|
|