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1441 ariadna 1
<?php
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namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
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class ExponentialBestFit extends BestFit
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{
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    /**
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     * Algorithm type to use for best-fit
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     * (Name of this Trend class).
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     */
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    protected string $bestFitType = 'exponential';
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    /**
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     * Return the Y-Value for a specified value of X.
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     *
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     * @param float $xValue X-Value
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     *
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     * @return float Y-Value
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     */
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    public function getValueOfYForX(float $xValue): float
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    {
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        return $this->getIntersect() * $this->getSlope() ** ($xValue - $this->xOffset);
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    }
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    /**
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     * Return the X-Value for a specified value of Y.
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     *
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     * @param float $yValue Y-Value
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     *
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     * @return float X-Value
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     */
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    public function getValueOfXForY(float $yValue): float
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    {
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        return log(($yValue + $this->yOffset) / $this->getIntersect()) / log($this->getSlope());
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    }
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    /**
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     * Return the Equation of the best-fit line.
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     *
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     * @param int $dp Number of places of decimal precision to display
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     */
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    public function getEquation(int $dp = 0): string
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    {
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        $slope = $this->getSlope($dp);
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        $intersect = $this->getIntersect($dp);
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        return 'Y = ' . $intersect . ' * ' . $slope . '^X';
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    }
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    /**
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     * Return the Slope of the line.
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     *
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     * @param int $dp Number of places of decimal precision to display
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     */
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    public function getSlope(int $dp = 0): float
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    {
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        if ($dp != 0) {
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            return round(exp($this->slope), $dp);
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        }
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        return exp($this->slope);
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    }
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    /**
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     * Return the Value of X where it intersects Y = 0.
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     *
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     * @param int $dp Number of places of decimal precision to display
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     */
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    public function getIntersect(int $dp = 0): float
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    {
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        if ($dp != 0) {
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            return round(exp($this->intersect), $dp);
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        }
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        return exp($this->intersect);
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    }
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    /**
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     * Execute the regression and calculate the goodness of fit for a set of X and Y data values.
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     *
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     * @param float[] $yValues The set of Y-values for this regression
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     * @param float[] $xValues The set of X-values for this regression
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     */
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    private function exponentialRegression(array $yValues, array $xValues, bool $const): void
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    {
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        $adjustedYValues = array_map(
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            fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value),
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            $yValues
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        );
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        $this->leastSquareFit($adjustedYValues, $xValues, $const);
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    }
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    /**
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     * Define the regression and calculate the goodness of fit for a set of X and Y data values.
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     *
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     * @param float[] $yValues The set of Y-values for this regression
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     * @param float[] $xValues The set of X-values for this regression
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     */
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    public function __construct(array $yValues, array $xValues = [], bool $const = true)
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    {
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        parent::__construct($yValues, $xValues);
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        if (!$this->error) {
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            $this->exponentialRegression($yValues, $xValues, (bool) $const);
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        }
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    }
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}