<?php

namespace LWVendor\PhpOffice\PhpSpreadsheet\Shared\Trend;

class BestFit
{
    /**
     * Indicator flag for a calculation error.
     *
     * @var bool
     */
    protected $error = \false;
    /**
     * Algorithm type to use for best-fit.
     *
     * @var string
     */
    protected $bestFitType = 'undetermined';
    /**
     * Number of entries in the sets of x- and y-value arrays.
     *
     * @var int
     */
    protected $valueCount = 0;
    /**
     * X-value dataseries of values.
     *
     * @var float[]
     */
    protected $xValues = [];
    /**
     * Y-value dataseries of values.
     *
     * @var float[]
     */
    protected $yValues = [];
    /**
     * Flag indicating whether values should be adjusted to Y=0.
     *
     * @var bool
     */
    protected $adjustToZero = \false;
    /**
     * Y-value series of best-fit values.
     *
     * @var float[]
     */
    protected $yBestFitValues = [];
    protected $goodnessOfFit = 1;
    protected $stdevOfResiduals = 0;
    protected $covariance = 0;
    protected $correlation = 0;
    protected $SSRegression = 0;
    protected $SSResiduals = 0;
    protected $DFResiduals = 0;
    protected $f = 0;
    protected $slope = 0;
    protected $slopeSE = 0;
    protected $intersect = 0;
    protected $intersectSE = 0;
    protected $xOffset = 0;
    protected $yOffset = 0;
    public function getError()
    {
        return $this->error;
    }
    public function getBestFitType()
    {
        return $this->bestFitType;
    }
    /**
     * Return the Y-Value for a specified value of X.
     *
     * @param float $xValue X-Value
     *
     * @return bool Y-Value
     */
    public function getValueOfYForX($xValue)
    {
        return \false;
    }
    /**
     * Return the X-Value for a specified value of Y.
     *
     * @param float $yValue Y-Value
     *
     * @return bool X-Value
     */
    public function getValueOfXForY($yValue)
    {
        return \false;
    }
    /**
     * Return the original set of X-Values.
     *
     * @return float[] X-Values
     */
    public function getXValues()
    {
        return $this->xValues;
    }
    /**
     * Return the Equation of the best-fit line.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return bool
     */
    public function getEquation($dp = 0)
    {
        return \false;
    }
    /**
     * Return the Slope of the line.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getSlope($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->slope, $dp);
        }
        return $this->slope;
    }
    /**
     * Return the standard error of the Slope.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getSlopeSE($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->slopeSE, $dp);
        }
        return $this->slopeSE;
    }
    /**
     * Return the Value of X where it intersects Y = 0.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getIntersect($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->intersect, $dp);
        }
        return $this->intersect;
    }
    /**
     * Return the standard error of the Intersect.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getIntersectSE($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->intersectSE, $dp);
        }
        return $this->intersectSE;
    }
    /**
     * Return the goodness of fit for this regression.
     *
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getGoodnessOfFit($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->goodnessOfFit, $dp);
        }
        return $this->goodnessOfFit;
    }
    /**
     * Return the goodness of fit for this regression.
     *
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getGoodnessOfFitPercent($dp = 0)
    {
        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 $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getStdevOfResiduals($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->stdevOfResiduals, $dp);
        }
        return $this->stdevOfResiduals;
    }
    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getSSRegression($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->SSRegression, $dp);
        }
        return $this->SSRegression;
    }
    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getSSResiduals($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->SSResiduals, $dp);
        }
        return $this->SSResiduals;
    }
    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getDFResiduals($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->DFResiduals, $dp);
        }
        return $this->DFResiduals;
    }
    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getF($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->f, $dp);
        }
        return $this->f;
    }
    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getCovariance($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->covariance, $dp);
        }
        return $this->covariance;
    }
    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getCorrelation($dp = 0)
    {
        if ($dp != 0) {
            return \round($this->correlation, $dp);
        }
        return $this->correlation;
    }
    /**
     * @return float[]
     */
    public function getYBestFitValues()
    {
        return $this->yBestFitValues;
    }
    protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const) : void
    {
        $SSres = $SScov = $SScor = $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) {
                $SStot += ($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) {
                $SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
            } else {
                $SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
            }
        }
        $this->SSResiduals = $SSres;
        $this->DFResiduals = $this->valueCount - 1 - $const;
        if ($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 float[] $yValues
     * @param float[] $xValues
     * @param bool $const
     */
    protected function leastSquareFit(array $yValues, array $xValues, $const) : void
    {
        // calculate sums
        $x_sum = \array_sum($xValues);
        $y_sum = \array_sum($yValues);
        $meanX = $x_sum / $this->valueCount;
        $meanY = $y_sum / $this->valueCount;
        $mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
        for ($i = 0; $i < $this->valueCount; ++$i) {
            $xy_sum += $xValues[$i] * $yValues[$i];
            $xx_sum += $xValues[$i] * $xValues[$i];
            $yy_sum += $yValues[$i] * $yValues[$i];
            if ($const) {
                $mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
                $mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
            } else {
                $mBase += $xValues[$i] * $yValues[$i];
                $mDivisor += $xValues[$i] * $xValues[$i];
            }
        }
        // calculate slope
        $this->slope = $mBase / $mDivisor;
        // calculate intersect
        if ($const) {
            $this->intersect = $meanY - $this->slope * $meanX;
        } else {
            $this->intersect = 0;
        }
        $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $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
     * @param bool $const
     */
    public function __construct($yValues, $xValues = [], $const = \true)
    {
        //    Calculate number of points
        $nY = \count($yValues);
        $nX = \count($xValues);
        //    Define X Values if necessary
        if ($nX == 0) {
            $xValues = \range(1, $nY);
        } elseif ($nY != $nX) {
            //    Ensure both arrays of points are the same size
            $this->error = \true;
        }
        $this->valueCount = $nY;
        $this->xValues = $xValues;
        $this->yValues = $yValues;
    }
}
