/branches/v2.25-scarificateur/jrest/lib/PHPExcel/Classes/PHPExcel/Shared/trend/trendClass.php |
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New file |
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<?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 |