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See Release Notes

  • Bug fixes for general core bugs in 3.10.x will end 8 November 2021 (12 months).
  • Bug fixes for security issues in 3.10.x will end 9 May 2022 (18 months).
  • PHP version: minimum PHP 7.2.0 Note: minimum PHP version has increased since Moodle 3.8. PHP 7.3.x and 7.4.x are supported too.
<?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 $this->getIntersect() * pow(($xValue - $this->xOffset), $this->getSlope());
> 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 pow((($yValue + $this->yOffset) / $this->getIntersect()), (1 / $this->getSlope()));
> return (($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 * @param bool $const */
< private function powerRegression($yValues, $xValues, $const)
> private function powerRegression($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($value); foreach ($yValues 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) { $this->powerRegression($yValues, $xValues, $const); } } }