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/branches/v2.25-scarificateur/jrest/lib/PHPExcel/Classes/PHPExcel/Shared/trend/trendClass.php
New file
0,0 → 1,156
<?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/linearBestFitClass.php';
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/logarithmicBestFitClass.php';
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/exponentialBestFitClass.php';
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/powerBestFitClass.php';
require_once PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/polynomialBestFitClass.php';
 
 
/**
* PHPExcel_trendClass
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2013 PHPExcel (http://www.codeplex.com/PHPExcel)
*/
class trendClass
{
const TREND_LINEAR = 'Linear';
const TREND_LOGARITHMIC = 'Logarithmic';
const TREND_EXPONENTIAL = 'Exponential';
const TREND_POWER = 'Power';
const TREND_POLYNOMIAL_2 = 'Polynomial_2';
const TREND_POLYNOMIAL_3 = 'Polynomial_3';
const TREND_POLYNOMIAL_4 = 'Polynomial_4';
const TREND_POLYNOMIAL_5 = 'Polynomial_5';
const TREND_POLYNOMIAL_6 = 'Polynomial_6';
const TREND_BEST_FIT = 'Bestfit';
const TREND_BEST_FIT_NO_POLY = 'Bestfit_no_Polynomials';
 
/**
* Names of the best-fit trend analysis methods
*
* @var string[]
**/
private static $_trendTypes = array( self::TREND_LINEAR,
self::TREND_LOGARITHMIC,
self::TREND_EXPONENTIAL,
self::TREND_POWER
);
/**
* Names of the best-fit trend polynomial orders
*
* @var string[]
**/
private static $_trendTypePolyOrders = array( self::TREND_POLYNOMIAL_2,
self::TREND_POLYNOMIAL_3,
self::TREND_POLYNOMIAL_4,
self::TREND_POLYNOMIAL_5,
self::TREND_POLYNOMIAL_6
);
 
/**
* Cached results for each method when trying to identify which provides the best fit
*
* @var PHPExcel_Best_Fit[]
**/
private static $_trendCache = array();
 
 
public static function calculate($trendType=self::TREND_BEST_FIT, $yValues, $xValues=array(), $const=True) {
// Calculate number of points in each dataset
$nY = count($yValues);
$nX = count($xValues);
 
// Define X Values if necessary
if ($nX == 0) {
$xValues = range(1,$nY);
$nX = $nY;
} elseif ($nY != $nX) {
// Ensure both arrays of points are the same size
trigger_error("trend(): Number of elements in coordinate arrays do not match.", E_USER_ERROR);
}
 
$key = md5($trendType.$const.serialize($yValues).serialize($xValues));
// Determine which trend method has been requested
switch ($trendType) {
// Instantiate and return the class for the requested trend method
case self::TREND_LINEAR :
case self::TREND_LOGARITHMIC :
case self::TREND_EXPONENTIAL :
case self::TREND_POWER :
if (!isset(self::$_trendCache[$key])) {
$className = 'PHPExcel_'.$trendType.'_Best_Fit';
self::$_trendCache[$key] = new $className($yValues,$xValues,$const);
}
return self::$_trendCache[$key];
break;
case self::TREND_POLYNOMIAL_2 :
case self::TREND_POLYNOMIAL_3 :
case self::TREND_POLYNOMIAL_4 :
case self::TREND_POLYNOMIAL_5 :
case self::TREND_POLYNOMIAL_6 :
if (!isset(self::$_trendCache[$key])) {
$order = substr($trendType,-1);
self::$_trendCache[$key] = new PHPExcel_Polynomial_Best_Fit($order,$yValues,$xValues,$const);
}
return self::$_trendCache[$key];
break;
case self::TREND_BEST_FIT :
case self::TREND_BEST_FIT_NO_POLY :
// If the request is to determine the best fit regression, then we test each trend line in turn
// Start by generating an instance of each available trend method
foreach(self::$_trendTypes as $trendMethod) {
$className = 'PHPExcel_'.$trendMethod.'BestFit';
$bestFit[$trendMethod] = new $className($yValues,$xValues,$const);
$bestFitValue[$trendMethod] = $bestFit[$trendMethod]->getGoodnessOfFit();
}
if ($trendType != self::TREND_BEST_FIT_NO_POLY) {
foreach(self::$_trendTypePolyOrders as $trendMethod) {
$order = substr($trendMethod,-1);
$bestFit[$trendMethod] = new PHPExcel_Polynomial_Best_Fit($order,$yValues,$xValues,$const);
if ($bestFit[$trendMethod]->getError()) {
unset($bestFit[$trendMethod]);
} else {
$bestFitValue[$trendMethod] = $bestFit[$trendMethod]->getGoodnessOfFit();
}
}
}
// Determine which of our trend lines is the best fit, and then we return the instance of that trend class
arsort($bestFitValue);
$bestFitType = key($bestFitValue);
return $bestFit[$bestFitType];
break;
default :
return false;
}
} // function calculate()
 
} // class trendClass
/branches/v2.25-scarificateur/jrest/lib/PHPExcel/Classes/PHPExcel/Shared/trend/powerBestFitClass.php
New file
0,0 → 1,142
<?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';
 
 
/**
* PHPExcel_Power_Best_Fit
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2013 PHPExcel (http://www.codeplex.com/PHPExcel)
*/
class PHPExcel_Power_Best_Fit extends PHPExcel_Best_Fit
{
/**
* Algorithm type to use for best-fit
* (Name of this trend class)
*
* @var string
**/
protected $_bestFitType = 'power';
 
 
/**
* Return the Y-Value for a specified value of X
*
* @param float $xValue X-Value
* @return float Y-Value
**/
public function getValueOfYForX($xValue) {
return $this->getIntersect() * pow(($xValue - $this->_Xoffset),$this->getSlope());
} // 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 pow((($yValue + $this->_Yoffset) / $this->getIntersect()),(1 / $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);
 
return 'Y = '.$intersect.' * X^'.$slope;
} // function getEquation()
 
 
/**
* Return the Value of X where it intersects Y = 0
*
* @param int $dp Number of places of decimal precision to display
* @return string
**/
public function getIntersect($dp=0) {
if ($dp != 0) {
return round(exp($this->_intersect),$dp);
}
return exp($this->_intersect);
} // function getIntersect()
 
 
/**
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param boolean $const
*/
private function _power_regression($yValues, $xValues, $const) {
foreach($xValues as &$value) {
if ($value < 0.0) {
$value = 0 - log(abs($value));
} elseif ($value > 0.0) {
$value = log($value);
}
}
unset($value);
foreach($yValues as &$value) {
if ($value < 0.0) {
$value = 0 - log(abs($value));
} elseif ($value > 0.0) {
$value = log($value);
}
}
unset($value);
 
$this->_leastSquareFit($yValues, $xValues, $const);
} // function _power_regression()
 
 
/**
* Define the regression and calculate the goodness of fit for a set of X and Y data values
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param boolean $const
*/
function __construct($yValues, $xValues=array(), $const=True) {
if (parent::__construct($yValues, $xValues) !== False) {
$this->_power_regression($yValues, $xValues, $const);
}
} // function __construct()
 
} // class powerBestFit
/branches/v2.25-scarificateur/jrest/lib/PHPExcel/Classes/PHPExcel/Shared/trend/bestFitClass.php
New file
0,0 → 1,432
<?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##
*/
 
 
/**
* PHPExcel_Best_Fit
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2013 PHPExcel (http://www.codeplex.com/PHPExcel)
*/
class PHPExcel_Best_Fit
{
/**
* Indicator flag for a calculation error
*
* @var boolean
**/
protected $_error = False;
 
/**
* Algorithm type to use for best-fit
*
* @var string
**/
protected $_bestFitType = 'undetermined';
 
/**
* Number of entries in the sets of x- and y-value arrays
*
* @var int
**/
protected $_valueCount = 0;
 
/**
* X-value dataseries of values
*
* @var float[]
**/
protected $_xValues = array();
 
/**
* Y-value dataseries of values
*
* @var float[]
**/
protected $_yValues = array();
 
/**
* Flag indicating whether values should be adjusted to Y=0
*
* @var boolean
**/
protected $_adjustToZero = False;
 
/**
* Y-value series of best-fit values
*
* @var float[]
**/
protected $_yBestFitValues = array();
 
protected $_goodnessOfFit = 1;
 
protected $_stdevOfResiduals = 0;
 
protected $_covariance = 0;
 
protected $_correlation = 0;
 
protected $_SSRegression = 0;
 
protected $_SSResiduals = 0;
 
protected $_DFResiduals = 0;
 
protected $_F = 0;
 
protected $_slope = 0;
 
protected $_slopeSE = 0;
 
protected $_intersect = 0;
 
protected $_intersectSE = 0;
 
protected $_Xoffset = 0;
 
protected $_Yoffset = 0;
 
 
public function getError() {
return $this->_error;
} // function getBestFitType()
 
 
public function getBestFitType() {
return $this->_bestFitType;
} // function getBestFitType()
 
 
/**
* Return the Y-Value for a specified value of X
*
* @param float $xValue X-Value
* @return float Y-Value
*/
public function getValueOfYForX($xValue) {
return False;
} // 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 False;
} // function getValueOfXForY()
 
 
/**
* Return the original set of X-Values
*
* @return float[] X-Values
*/
public function getXValues() {
return $this->_xValues;
} // 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) {
return False;
} // 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 ($dp != 0) {
return round($this->_slope,$dp);
}
return $this->_slope;
} // function getSlope()
 
 
/**
* Return the standard error of the Slope
*
* @param int $dp Number of places of decimal precision to display
* @return string
*/
public function getSlopeSE($dp=0) {
if ($dp != 0) {
return round($this->_slopeSE,$dp);
}
return $this->_slopeSE;
} // function getSlopeSE()
 
 
/**
* Return the Value of X where it intersects Y = 0
*
* @param int $dp Number of places of decimal precision to display
* @return string
*/
public function getIntersect($dp=0) {
if ($dp != 0) {
return round($this->_intersect,$dp);
}
return $this->_intersect;
} // function getIntersect()
 
 
/**
* Return the standard error of the Intersect
*
* @param int $dp Number of places of decimal precision to display
* @return string
*/
public function getIntersectSE($dp=0) {
if ($dp != 0) {
return round($this->_intersectSE,$dp);
}
return $this->_intersectSE;
} // function getIntersectSE()
 
 
/**
* Return the goodness of fit for this regression
*
* @param int $dp Number of places of decimal precision to return
* @return float
*/
public function getGoodnessOfFit($dp=0) {
if ($dp != 0) {
return round($this->_goodnessOfFit,$dp);
}
return $this->_goodnessOfFit;
} // function getGoodnessOfFit()
 
 
public function getGoodnessOfFitPercent($dp=0) {
if ($dp != 0) {
return round($this->_goodnessOfFit * 100,$dp);
}
return $this->_goodnessOfFit * 100;
} // function getGoodnessOfFitPercent()
 
 
/**
* Return the standard deviation of the residuals for this regression
*
* @param int $dp Number of places of decimal precision to return
* @return float
*/
public function getStdevOfResiduals($dp=0) {
if ($dp != 0) {
return round($this->_stdevOfResiduals,$dp);
}
return $this->_stdevOfResiduals;
} // function getStdevOfResiduals()
 
 
public function getSSRegression($dp=0) {
if ($dp != 0) {
return round($this->_SSRegression,$dp);
}
return $this->_SSRegression;
} // function getSSRegression()
 
 
public function getSSResiduals($dp=0) {
if ($dp != 0) {
return round($this->_SSResiduals,$dp);
}
return $this->_SSResiduals;
} // function getSSResiduals()
 
 
public function getDFResiduals($dp=0) {
if ($dp != 0) {
return round($this->_DFResiduals,$dp);
}
return $this->_DFResiduals;
} // function getDFResiduals()
 
 
public function getF($dp=0) {
if ($dp != 0) {
return round($this->_F,$dp);
}
return $this->_F;
} // function getF()
 
 
public function getCovariance($dp=0) {
if ($dp != 0) {
return round($this->_covariance,$dp);
}
return $this->_covariance;
} // function getCovariance()
 
 
public function getCorrelation($dp=0) {
if ($dp != 0) {
return round($this->_correlation,$dp);
}
return $this->_correlation;
} // function getCorrelation()
 
 
public function getYBestFitValues() {
return $this->_yBestFitValues;
} // function getYBestFitValues()
 
 
protected function _calculateGoodnessOfFit($sumX,$sumY,$sumX2,$sumY2,$sumXY,$meanX,$meanY, $const) {
$SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;
foreach($this->_xValues as $xKey => $xValue) {
$bestFitY = $this->_yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
 
$SSres += ($this->_yValues[$xKey] - $bestFitY) * ($this->_yValues[$xKey] - $bestFitY);
if ($const) {
$SStot += ($this->_yValues[$xKey] - $meanY) * ($this->_yValues[$xKey] - $meanY);
} else {
$SStot += $this->_yValues[$xKey] * $this->_yValues[$xKey];
}
$SScov += ($this->_xValues[$xKey] - $meanX) * ($this->_yValues[$xKey] - $meanY);
if ($const) {
$SSsex += ($this->_xValues[$xKey] - $meanX) * ($this->_xValues[$xKey] - $meanX);
} else {
$SSsex += $this->_xValues[$xKey] * $this->_xValues[$xKey];
}
}
 
$this->_SSResiduals = $SSres;
$this->_DFResiduals = $this->_valueCount - 1 - $const;
 
if ($this->_DFResiduals == 0.0) {
$this->_stdevOfResiduals = 0.0;
} else {
$this->_stdevOfResiduals = sqrt($SSres / $this->_DFResiduals);
}
if (($SStot == 0.0) || ($SSres == $SStot)) {
$this->_goodnessOfFit = 1;
} else {
$this->_goodnessOfFit = 1 - ($SSres / $SStot);
}
 
$this->_SSRegression = $this->_goodnessOfFit * $SStot;
$this->_covariance = $SScov / $this->_valueCount;
$this->_correlation = ($this->_valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->_valueCount * $sumX2 - pow($sumX,2)) * ($this->_valueCount * $sumY2 - pow($sumY,2)));
$this->_slopeSE = $this->_stdevOfResiduals / sqrt($SSsex);
$this->_intersectSE = $this->_stdevOfResiduals * sqrt(1 / ($this->_valueCount - ($sumX * $sumX) / $sumX2));
if ($this->_SSResiduals != 0.0) {
if ($this->_DFResiduals == 0.0) {
$this->_F = 0.0;
} else {
$this->_F = $this->_SSRegression / ($this->_SSResiduals / $this->_DFResiduals);
}
} else {
if ($this->_DFResiduals == 0.0) {
$this->_F = 0.0;
} else {
$this->_F = $this->_SSRegression / $this->_DFResiduals;
}
}
} // function _calculateGoodnessOfFit()
 
 
protected function _leastSquareFit($yValues, $xValues, $const) {
// calculate sums
$x_sum = array_sum($xValues);
$y_sum = array_sum($yValues);
$meanX = $x_sum / $this->_valueCount;
$meanY = $y_sum / $this->_valueCount;
$mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
for($i = 0; $i < $this->_valueCount; ++$i) {
$xy_sum += $xValues[$i] * $yValues[$i];
$xx_sum += $xValues[$i] * $xValues[$i];
$yy_sum += $yValues[$i] * $yValues[$i];
 
if ($const) {
$mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
$mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
} else {
$mBase += $xValues[$i] * $yValues[$i];
$mDivisor += $xValues[$i] * $xValues[$i];
}
}
 
// calculate slope
// $this->_slope = (($this->_valueCount * $xy_sum) - ($x_sum * $y_sum)) / (($this->_valueCount * $xx_sum) - ($x_sum * $x_sum));
$this->_slope = $mBase / $mDivisor;
 
// calculate intersect
// $this->_intersect = ($y_sum - ($this->_slope * $x_sum)) / $this->_valueCount;
if ($const) {
$this->_intersect = $meanY - ($this->_slope * $meanX);
} else {
$this->_intersect = 0;
}
 
$this->_calculateGoodnessOfFit($x_sum,$y_sum,$xx_sum,$yy_sum,$xy_sum,$meanX,$meanY,$const);
} // function _leastSquareFit()
 
 
/**
* Define the 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 $const
*/
function __construct($yValues, $xValues=array(), $const=True) {
// Calculate number of points
$nY = count($yValues);
$nX = count($xValues);
 
// Define X Values if necessary
if ($nX == 0) {
$xValues = range(1,$nY);
$nX = $nY;
} elseif ($nY != $nX) {
// Ensure both arrays of points are the same size
$this->_error = True;
return False;
}
 
$this->_valueCount = $nY;
$this->_xValues = $xValues;
$this->_yValues = $yValues;
} // function __construct()
 
} // class bestFit
/branches/v2.25-scarificateur/jrest/lib/PHPExcel/Classes/PHPExcel/Shared/trend/logarithmicBestFitClass.php
New file
0,0 → 1,120
<?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');
 
 
/**
* PHPExcel_Logarithmic_Best_Fit
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2013 PHPExcel (http://www.codeplex.com/PHPExcel)
*/
class PHPExcel_Logarithmic_Best_Fit extends PHPExcel_Best_Fit
{
/**
* Algorithm type to use for best-fit
* (Name of this trend class)
*
* @var string
**/
protected $_bestFitType = 'logarithmic';
 
 
/**
* Return the Y-Value for a specified value of X
*
* @param float $xValue X-Value
* @return float Y-Value
**/
public function getValueOfYForX($xValue) {
return $this->getIntersect() + $this->getSlope() * log($xValue - $this->_Xoffset);
} // 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 exp(($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);
 
return 'Y = '.$intersect.' + '.$slope.' * log(X)';
} // function getEquation()
 
 
/**
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param boolean $const
*/
private function _logarithmic_regression($yValues, $xValues, $const) {
foreach($xValues as &$value) {
if ($value < 0.0) {
$value = 0 - log(abs($value));
} elseif ($value > 0.0) {
$value = log($value);
}
}
unset($value);
 
$this->_leastSquareFit($yValues, $xValues, $const);
} // function _logarithmic_regression()
 
 
/**
* Define the regression and calculate the goodness of fit for a set of X and Y data values
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param boolean $const
*/
function __construct($yValues, $xValues=array(), $const=True) {
if (parent::__construct($yValues, $xValues) !== False) {
$this->_logarithmic_regression($yValues, $xValues, $const);
}
} // function __construct()
 
} // class logarithmicBestFit
/branches/v2.25-scarificateur/jrest/lib/PHPExcel/Classes/PHPExcel/Shared/trend/polynomialBestFitClass.php
New file
0,0 → 1,224
<?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
/branches/v2.25-scarificateur/jrest/lib/PHPExcel/Classes/PHPExcel/Shared/trend/exponentialBestFitClass.php
New file
0,0 → 1,148
<?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');
 
 
/**
* PHPExcel_Exponential_Best_Fit
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2013 PHPExcel (http://www.codeplex.com/PHPExcel)
*/
class PHPExcel_Exponential_Best_Fit extends PHPExcel_Best_Fit
{
/**
* Algorithm type to use for best-fit
* (Name of this trend class)
*
* @var string
**/
protected $_bestFitType = 'exponential';
 
 
/**
* Return the Y-Value for a specified value of X
*
* @param float $xValue X-Value
* @return float Y-Value
**/
public function getValueOfYForX($xValue) {
return $this->getIntersect() * pow($this->getSlope(),($xValue - $this->_Xoffset));
} // 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 log(($yValue + $this->_Yoffset) / $this->getIntersect()) / log($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);
 
return 'Y = '.$intersect.' * '.$slope.'^X';
} // 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 ($dp != 0) {
return round(exp($this->_slope),$dp);
}
return exp($this->_slope);
} // function getSlope()
 
 
/**
* Return the Value of X where it intersects Y = 0
*
* @param int $dp Number of places of decimal precision to display
* @return string
**/
public function getIntersect($dp=0) {
if ($dp != 0) {
return round(exp($this->_intersect),$dp);
}
return exp($this->_intersect);
} // function getIntersect()
 
 
/**
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param boolean $const
*/
private function _exponential_regression($yValues, $xValues, $const) {
foreach($yValues as &$value) {
if ($value < 0.0) {
$value = 0 - log(abs($value));
} elseif ($value > 0.0) {
$value = log($value);
}
}
unset($value);
 
$this->_leastSquareFit($yValues, $xValues, $const);
} // function _exponential_regression()
 
 
/**
* Define the regression and calculate the goodness of fit for a set of X and Y data values
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param boolean $const
*/
function __construct($yValues, $xValues=array(), $const=True) {
if (parent::__construct($yValues, $xValues) !== False) {
$this->_exponential_regression($yValues, $xValues, $const);
}
} // function __construct()
 
} // class exponentialBestFit
/branches/v2.25-scarificateur/jrest/lib/PHPExcel/Classes/PHPExcel/Shared/trend/linearBestFitClass.php
New file
0,0 → 1,111
<?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');
 
 
/**
* PHPExcel_Linear_Best_Fit
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2013 PHPExcel (http://www.codeplex.com/PHPExcel)
*/
class PHPExcel_Linear_Best_Fit extends PHPExcel_Best_Fit
{
/**
* Algorithm type to use for best-fit
* (Name of this trend class)
*
* @var string
**/
protected $_bestFitType = 'linear';
 
 
/**
* Return the Y-Value for a specified value of X
*
* @param float $xValue X-Value
* @return float Y-Value
**/
public function getValueOfYForX($xValue) {
return $this->getIntersect() + $this->getSlope() * $xValue;
} // 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);
 
return 'Y = '.$intersect.' + '.$slope.' * X';
} // function getEquation()
 
 
/**
* Execute the regression and calculate the goodness of fit for a set of X and Y data values
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param boolean $const
*/
private function _linear_regression($yValues, $xValues, $const) {
$this->_leastSquareFit($yValues, $xValues,$const);
} // function _linear_regression()
 
 
/**
* Define the regression and calculate the goodness of fit for a set of X and Y data values
*
* @param float[] $yValues The set of Y-values for this regression
* @param float[] $xValues The set of X-values for this regression
* @param boolean $const
*/
function __construct($yValues, $xValues=array(), $const=True) {
if (parent::__construct($yValues, $xValues) !== False) {
$this->_linear_regression($yValues, $xValues, $const);
}
} // function __construct()
 
} // class linearBestFit