Current File : //home/silvzytp/ccd-ind-code/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/PowerBestFit.php
<?php
namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
class PowerBestFit extends BestFit
{
/**
* 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 $this->getIntersect() * ($xValue - $this->xOffset) ** $this->getSlope();
}
/**
* 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->yOffset) / $this->getIntersect()) ** (1 / $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 'Y = ' . $intersect . ' * X^' . $slope;
}
/**
* Return the Value of X where it intersects Y = 0.
*
* @param int $dp Number of places of decimal precision to display
*
* @return float
*/
public function getIntersect($dp = 0)
{
if ($dp != 0) {
return round(exp($this->intersect), $dp);
}
return exp($this->intersect);
}
/**
* 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
*/
private function powerRegression(array $yValues, array $xValues, bool $const): void
{
$adjustedYValues = array_map(
function ($value) {
return ($value < 0.0) ? 0 - log(abs($value)) : log($value);
},
$yValues
);
$adjustedXValues = array_map(
function ($value) {
return ($value < 0.0) ? 0 - log(abs($value)) : log($value);
},
$xValues
);
$this->leastSquareFit($adjustedYValues, $adjustedXValues, $const);
}
/**
* 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 bool $const
*/
public function __construct($yValues, $xValues = [], $const = true)
{
parent::__construct($yValues, $xValues);
if (!$this->error) {
$this->powerRegression($yValues, $xValues, (bool) $const);
}
}
}
Mini Shell