LinearBestFit.php
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<?php
namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
class LinearBestFit extends BestFit
{
/**
* 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;
}
/**
* 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();
}
/**
* 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';
}
/**
* 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 linearRegression(array $yValues, array $xValues, bool $const): void
{
$this->leastSquareFit($yValues, $xValues, $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) {
$this->linearRegression($yValues, $xValues, (bool) $const);
}
}
}