<?php

namespace PhpOffice\PhpSpreadsheet\Shared\Trend;


< class BestFit



> abstract 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 = [];


>     /** @var float */


    protected $goodnessOfFit = 1;


>     /** @var float */


    protected $stdevOfResiduals = 0;


>     /** @var float */


    protected $covariance = 0;


>     /** @var float */


    protected $correlation = 0;


>     /** @var float */


    protected $SSRegression = 0;


>     /** @var float */


    protected $SSResiduals = 0;


>     /** @var float */


    protected $DFResiduals = 0;


>     /** @var float */


    protected $f = 0;


>     /** @var float */


    protected $slope = 0;


>     /** @var float */


    protected $slopeSE = 0;


>     /** @var float */


    protected $intersect = 0;


>     /** @var float */


    protected $intersectSE = 0;


>     /** @var float */


    protected $xOffset = 0;


>     /** @var float */


    protected $yOffset = 0;


>     /** @return bool */


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


>     /** @return string */


    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



>      * @return float Y-Value


     */

<     public function getValueOfYForX($xValue)
<     {
<         return false;
<     }



>     abstract public function getValueOfYForX($xValue);



    /**
     * Return the X-Value for a specified value of Y.
     *
     * @param float $yValue Y-Value
     *

<      * @return bool X-Value



>      * @return float X-Value


     */

<     public function getValueOfXForY($yValue)
<     {
<         return false;
<     }



>     abstract public function getValueOfXForY($yValue);



    /**
     * 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



>      * @return string


     */

<     public function getEquation($dp = 0)
<     {
<         return false;
<     }



>     abstract public function getEquation($dp = 0);



    /**
     * 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)



>     /** @var mixed */
>     private static $scrutinizerZeroPointZero = 0.0;
> 
>     /**
>      * @param mixed $x
>      * @param mixed $y
>      */
>     private static function scrutinizerLooseCompare($x, $y): bool


    {

<         $SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;



>         return $x == $y;
>     }
> 
>     /**
>      * @param float $sumX
>      * @param float $sumY
>      * @param float $sumX2
>      * @param float $sumY2
>      * @param float $sumXY
>      * @param float $meanX
>      * @param float $meanY
>      * @param bool|int $const
>      */
>     protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $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) {



>             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) {



>             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;



>         $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)) {



>         // Scrutinizer thinks $SSres == $SStot is always true. It is wrong.
>         if ($SStot == self::$scrutinizerZeroPointZero || self::scrutinizerLooseCompare($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 * $sumX2 - pow($sumX, 2)) * ($this->valueCount * $sumY2 - pow($sumY, 2)));



>         $this->correlation = ($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;
            }
        }
    }


>     /** @return float|int */
    /**
>     private function sumSquares(array $values)
     * @param float[] $yValues
>     {
     * @param float[] $xValues
>         return array_sum(
     * @param bool $const
>             array_map(
     */
>                 function ($value) {
    protected function leastSquareFit(array $yValues, array $xValues, $const)
>                     return $value ** 2;
    {
>                 },
        // calculate sums
>                 $values
        $x_sum = array_sum($xValues);
>             )
        $y_sum = array_sum($yValues);
>         );
        $meanX = $x_sum / $this->valueCount;
>     }
        $meanY = $y_sum / $this->valueCount;
> 



<      * @param bool $const



<     protected function leastSquareFit(array $yValues, array $xValues, $const)



>     protected function leastSquareFit(array $yValues, array $xValues, bool $const): void



<         $x_sum = array_sum($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;



>         $sumValuesX = array_sum($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;



<             $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);



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


            }
        }

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

        // calculate intersect

<         if ($const) {
<             $this->intersect = $meanY - ($this->slope * $meanX);
<         } else {
<             $this->intersect = 0;
<         }



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




<         $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $const);



>         $this->calculateGoodnessOfFit($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

<      * @param bool $const


     */

<     public function __construct($yValues, $xValues = [], $const = true)



>     public function __construct($yValues, $xValues = [])


    {
        //    Calculate number of points

<         $nY = count($yValues);
<         $nX = count($xValues);



>         $yValueCount = count($yValues);
>         $xValueCount = count($xValues);



        //    Define X Values if necessary

<         if ($nX == 0) {
<             $xValues = range(1, $nY);
<         } elseif ($nY != $nX) {



>         if ($xValueCount === 0) {
>             $xValues = range(1, $yValueCount);
>         } elseif ($yValueCount !== $xValueCount) {


            //    Ensure both arrays of points are the same size
            $this->error = true;
        }


<         $this->valueCount = $nY;



>         $this->valueCount = $yValueCount;


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