<?php

namespace TablePress\PhpOffice\PhpSpreadsheet\Shared\Trend;

class PowerBestFit extends BestFit
{
	/**
	 * Algorithm type to use for best-fit
	 * (Name of this Trend class).
	 */
	protected string $bestFitType = 'power';

	/**
	 * Return the Y-Value for a specified value of X.
	 *
	 * @param float $xValue X-Value
	 *
	 * @return float Y-Value
	 */
	public function getValueOfYForX(float $xValue): float
	{
		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(float $yValue): float
	{
		return (($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
	 */
	public function getEquation(int $dp = 0): string
	{
		$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
	 */
	public function getIntersect(int $dp = 0): float
	{
		if ($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
	 */
	private function powerRegression(array $yValues, array $xValues, bool $const): void
	{
		$adjustedYValues = array_map(
			fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value),
			$yValues
		);
		$adjustedXValues = array_map(
			fn ($value): float => ($value < 0.0) ? 0 - log(abs($value)) : log($value),
			$xValues
		);

		$this->leastSquareFit($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
	 */
	public function __construct(array $yValues, array $xValues = [], bool $const = true)
	{
		parent::__construct($yValues, $xValues);

		if (!$this->error) {
			$this->powerRegression($yValues, $xValues, (bool) $const);
		}
	}
}
