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composer update

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This commit is contained in:
Manish Verma
2018-12-05 10:50:52 +05:30
parent 9eabcacfa7
commit 4addd1e9c6
3328 changed files with 156676 additions and 138988 deletions
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168
vendor/phpoffice/phpexcel/Classes/PHPExcel/Shared/trend/logarithmicBestFitClass.php vendored
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@@ -1,8 +1,11 @@
<?php
require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
/**
* PHPExcel
* PHPExcel_Logarithmic_Best_Fit
*
* Copyright (c) 2006 - 2014 PHPExcel
* 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
@@ -20,101 +23,88 @@
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2014 PHPExcel (http://www.codeplex.com/PHPExcel)
* @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
* @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##
*/
require_once(PHPEXCEL_ROOT . 'PHPExcel/Shared/trend/bestFitClass.php');
/**
* PHPExcel_Logarithmic_Best_Fit
*
* @category PHPExcel
* @package PHPExcel_Shared_Trend
* @copyright Copyright (c) 2006 - 2014 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';
/**
* 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);
}
/**
* 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());
}
/**
* 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 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()) / $this->getSlope());
} // function getValueOfXForY()
return 'Y = '.$intersect.' + '.$slope.' * log(X)';
}
/**
* 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 $const
*/
private function logarithmicRegression($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);
/**
* 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);
$this->leastSquareFit($yValues, $xValues, $const);
}
return 'Y = '.$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
/**
* 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 $const
*/
public function __construct($yValues, $xValues = array(), $const = true)
{
if (parent::__construct($yValues, $xValues) !== false) {
$this->logarithmicRegression($yValues, $xValues, $const);
}
}
}