forked from SimplesIP/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.
425 lines
12 KiB
425 lines
12 KiB
<?php |
|
|
|
/** |
|
* PHPExcel_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_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; |
|
} |
|
|
|
|
|
public function getBestFitType() |
|
{ |
|
return $this->bestFitType; |
|
} |
|
|
|
/** |
|
* 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; |
|
} |
|
|
|
/** |
|
* 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; |
|
} |
|
|
|
/** |
|
* Return the original set of X-Values |
|
* |
|
* @return float[] X-Values |
|
*/ |
|
public function getXValues() |
|
{ |
|
return $this->xValues; |
|
} |
|
|
|
/** |
|
* 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; |
|
} |
|
|
|
/** |
|
* 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; |
|
} |
|
|
|
/** |
|
* 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; |
|
} |
|
|
|
/** |
|
* 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; |
|
} |
|
|
|
/** |
|
* 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; |
|
} |
|
|
|
/** |
|
* 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 $this->goodnessOfFit; |
|
} |
|
|
|
public function getGoodnessOfFitPercent($dp = 0) |
|
{ |
|
if ($dp != 0) { |
|
return round($this->goodnessOfFit * 100, $dp); |
|
} |
|
return $this->goodnessOfFit * 100; |
|
} |
|
|
|
/** |
|
* 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 $this->stdevOfResiduals; |
|
} |
|
|
|
public function getSSRegression($dp = 0) |
|
{ |
|
if ($dp != 0) { |
|
return round($this->SSRegression, $dp); |
|
} |
|
return $this->SSRegression; |
|
} |
|
|
|
public function getSSResiduals($dp = 0) |
|
{ |
|
if ($dp != 0) { |
|
return round($this->SSResiduals, $dp); |
|
} |
|
return $this->SSResiduals; |
|
} |
|
|
|
public function getDFResiduals($dp = 0) |
|
{ |
|
if ($dp != 0) { |
|
return round($this->DFResiduals, $dp); |
|
} |
|
return $this->DFResiduals; |
|
} |
|
|
|
public function getF($dp = 0) |
|
{ |
|
if ($dp != 0) { |
|
return round($this->f, $dp); |
|
} |
|
return $this->f; |
|
} |
|
|
|
public function getCovariance($dp = 0) |
|
{ |
|
if ($dp != 0) { |
|
return round($this->covariance, $dp); |
|
} |
|
return $this->covariance; |
|
} |
|
|
|
public function getCorrelation($dp = 0) |
|
{ |
|
if ($dp != 0) { |
|
return round($this->correlation, $dp); |
|
} |
|
return $this->correlation; |
|
} |
|
|
|
public function getYBestFitValues() |
|
{ |
|
return $this->yBestFitValues; |
|
} |
|
|
|
protected function calculateGoodnessOfFit($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 / $this->DFResiduals; |
|
} |
|
} |
|
} |
|
|
|
protected function leastSquareFit($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); |
|
} |
|
|
|
/** |
|
* 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 $const |
|
*/ |
|
public function __construct($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; |
|
} |
|
}
|
|
|