91 lines
2.4 KiB
PHP
91 lines
2.4 KiB
PHP
<?php
|
|
|
|
namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
|
|
|
|
class LogarithmicBestFit extends BestFit
|
|
{
|
|
/**
|
|
* 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 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 '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 bool $const
|
|
*/
|
|
private function logarithmicRegression($yValues, $xValues, $const): void
|
|
{
|
|
foreach ($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);
|
|
}
|
|
|
|
/**
|
|
* 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) {
|
|
$this->logarithmicRegression($yValues, $xValues, $const);
|
|
}
|
|
}
|
|
}
|