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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##*//*** 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($xValues);// Define X Values if necessaryif ($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