<?php

namespace TablePress\PhpOffice\PhpSpreadsheet\Shared\Trend;

abstract class BestFit
{
	/**
	 * Indicator flag for a calculation error.
	 */
	protected bool $error = false;

	/**
	 * Algorithm type to use for best-fit.
	 */
	protected string $bestFitType = 'undetermined';

	/**
	 * Number of entries in the sets of x- and y-value arrays.
	 */
	protected int $valueCount;

	/**
	 * X-value dataseries of values.
	 *
	 * @var float[]
	 */
	protected array $xValues = [];

	/**
	 * Y-value dataseries of values.
	 *
	 * @var float[]
	 */
	protected array $yValues = [];

	/**
	 * Flag indicating whether values should be adjusted to Y=0.
	 */
	protected bool $adjustToZero = false;

	/**
	 * Y-value series of best-fit values.
	 *
	 * @var float[]
	 */
	protected array $yBestFitValues = [];

	protected float $goodnessOfFit = 1;

	protected float $stdevOfResiduals = 0;

	protected float $covariance = 0;

	protected float $correlation = 0;

	protected float $SSRegression = 0;

	protected float $SSResiduals = 0;

	protected float $DFResiduals = 0;

	protected float $f = 0;

	protected float $slope = 0;

	protected float $slopeSE = 0;

	protected float $intersect = 0;

	protected float $intersectSE = 0;

	protected float $xOffset = 0;

	protected float $yOffset = 0;

	public function getError(): bool
	{
		return $this->error;
	}

	public function getBestFitType(): string
	{
		return $this->bestFitType;
	}

	/**
	 * Return the Y-Value for a specified value of X.
	 *
	 * @param float $xValue X-Value
	 *
	 * @return float Y-Value
	 */
	abstract public function getValueOfYForX(float $xValue): float;

	/**
	 * Return the X-Value for a specified value of Y.
	 *
	 * @param float $yValue Y-Value
	 *
	 * @return float X-Value
	 */
	abstract public function getValueOfXForY(float $yValue): float;

	/**
	 * Return the original set of X-Values.
	 *
	 * @return float[] X-Values
	 */
	public function getXValues(): array
	{
		return $this->xValues;
	}

	/**
	 * Return the original set of Y-Values.
	 *
	 * @return float[] Y-Values
	 */
	public function getYValues(): array
	{
		return $this->yValues;
	}

	/**
	 * Return the Equation of the best-fit line.
	 *
	 * @param int $dp Number of places of decimal precision to display
	 */
	abstract public function getEquation(int $dp = 0): string;

	/**
	 * Return the Slope of the line.
	 *
	 * @param int $dp Number of places of decimal precision to display
	 */
	public function getSlope(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->slope, $dp);
		}

		return $this->slope;
	}

	/**
	 * Return the standard error of the Slope.
	 *
	 * @param int $dp Number of places of decimal precision to display
	 */
	public function getSlopeSE(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->slopeSE, $dp);
		}

		return $this->slopeSE;
	}

	/**
	 * 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($this->intersect, $dp);
		}

		return $this->intersect;
	}

	/**
	 * Return the standard error of the Intersect.
	 *
	 * @param int $dp Number of places of decimal precision to display
	 */
	public function getIntersectSE(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->intersectSE, $dp);
		}

		return $this->intersectSE;
	}

	/**
	 * Return the goodness of fit for this regression.
	 *
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getGoodnessOfFit(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->goodnessOfFit, $dp);
		}

		return $this->goodnessOfFit;
	}

	/**
	 * Return the goodness of fit for this regression.
	 *
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getGoodnessOfFitPercent(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->goodnessOfFit * 100, $dp);
		}

		return $this->goodnessOfFit * 100;
	}

	/**
	 * Return the standard deviation of the residuals for this regression.
	 *
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getStdevOfResiduals(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->stdevOfResiduals, $dp);
		}

		return $this->stdevOfResiduals;
	}

	/**
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getSSRegression(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->SSRegression, $dp);
		}

		return $this->SSRegression;
	}

	/**
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getSSResiduals(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->SSResiduals, $dp);
		}

		return $this->SSResiduals;
	}

	/**
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getDFResiduals(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->DFResiduals, $dp);
		}

		return $this->DFResiduals;
	}

	/**
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getF(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->f, $dp);
		}

		return $this->f;
	}

	/**
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getCovariance(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->covariance, $dp);
		}

		return $this->covariance;
	}

	/**
	 * @param int $dp Number of places of decimal precision to return
	 */
	public function getCorrelation(int $dp = 0): float
	{
		if ($dp != 0) {
			return round($this->correlation, $dp);
		}

		return $this->correlation;
	}

	/**
	 * @return float[]
	 */
	public function getYBestFitValues(): array
	{
		return $this->yBestFitValues;
	}

	/**
				 * @param bool|int $const
				 */
				protected function calculateGoodnessOfFit(float $sumX, float $sumY, float $sumX2, float $sumY2, float $sumXY, float $meanX, float $meanY, $const): void
	{
		$SSres = $SScov = $SStot = $SSsex = 0.0;
		foreach ($this->xValues as $xKey => $xValue) {
			$bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);

			$SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
			if ($const === true) {
				$SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY);
			} else {
				$SStot += $this->yValues[$xKey] * $this->yValues[$xKey];
			}
			$SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);
			if ($const === true) {
				$SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
			} else {
				$SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
			}
		}

		$this->SSResiduals = $SSres;
		$this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);

		if ($this->DFResiduals == 0.0) {
			$this->stdevOfResiduals = 0.0;
		} else {
			$this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
		}

		if ($SStot == 0.0 || $SSres == $SStot) {
			$this->goodnessOfFit = 1;
		} else {
			$this->goodnessOfFit = 1 - ($SSres / $SStot);
		}

		$this->SSRegression = $this->goodnessOfFit * $SStot;
		$this->covariance = $SScov / $this->valueCount;
		$this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - $sumX ** 2) * ($this->valueCount * $sumY2 - $sumY ** 2));
		$this->slopeSE = $this->stdevOfResiduals / sqrt($SSsex);
		$this->intersectSE = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2));
		if ($this->SSResiduals != 0.0) {
			if ($this->DFResiduals == 0.0) {
				$this->f = 0.0;
			} else {
				$this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals);
			}
		} else {
			if ($this->DFResiduals == 0.0) {
				$this->f = 0.0;
			} else {
				$this->f = $this->SSRegression / $this->DFResiduals;
			}
		}
	}

	/**
	 * @param array<float|int> $values
	 *
	 * @return float|int
	 */
	private function sumSquares(array $values)
	{
		return array_sum(
			array_map(
				fn ($value) => $value ** 2,
				$values
			)
		);
	}

	/**
	 * @param float[] $yValues
	 * @param float[] $xValues
	 */
	protected function leastSquareFit(array $yValues, array $xValues, bool $const): void
	{
		// calculate sums
		$sumValuesX = array_sum($xValues);
		$sumValuesY = array_sum($yValues);
		$meanValueX = $sumValuesX / $this->valueCount;
		$meanValueY = $sumValuesY / $this->valueCount;
		$sumSquaresX = $this->sumSquares($xValues);
		$sumSquaresY = $this->sumSquares($yValues);
		$mBase = $mDivisor = 0.0;
		$xy_sum = 0.0;
		for ($i = 0; $i < $this->valueCount; ++$i) {
			$xy_sum += $xValues[$i] * $yValues[$i];

			if ($const === true) {
				$mBase += ($xValues[$i] - $meanValueX) * ($yValues[$i] - $meanValueY);
				$mDivisor += ($xValues[$i] - $meanValueX) * ($xValues[$i] - $meanValueX);
			} else {
				$mBase += $xValues[$i] * $yValues[$i];
				$mDivisor += $xValues[$i] * $xValues[$i];
			}
		}

		// calculate slope
		$this->slope = $mBase / $mDivisor;

		// calculate intersect
		$this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;

		$this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);
	}

	/**
	 * Define the regression.
	 *
	 * @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 = [])
	{
		//    Calculate number of points
		$yValueCount = count($yValues);
		$xValueCount = count($xValues);

		//    Define X Values if necessary
		if ($xValueCount === 0) {
			$xValues = range(1.0, $yValueCount);
		} elseif ($yValueCount !== $xValueCount) {
			//    Ensure both arrays of points are the same size
			$this->error = true;
		}

		$this->valueCount = $yValueCount;
		$this->xValues = $xValues;
		$this->yValues = $yValues;
	}
}
