<?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

<      * @param bool $const


     */

<     private function powerRegression($yValues, $xValues, $const): void



>     private function powerRegression(array $yValues, array $xValues, bool $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);



>         $adjustedYValues = array_map(
>             function ($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
>         );




<         $this->leastSquareFit($yValues, $xValues, $const);



>         $this->leastSquareFit($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, $const);



>             $this->powerRegression($yValues, $xValues, (bool) $const);


        }
    }
}