Proyectos de Subversion Moodle

<?php

namespace 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 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;

    }

    protected function calculateGoodnessOfFit(float $sumX, float $sumY, float $sumX2, float $sumY2, float $sumXY, float $meanX, float $meanY, bool|int $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;

            }

        }

    }

    /** @return float|int */

    private function sumSquares(array $values)

    {

        return array_sum(

            array_map(

                fn ($value): float|int => $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, $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;

    }

}