Proyectos de Subversion Moodle

<?php

namespace PhpOffice\PhpSpreadsheet\Shared\Trend;

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 float Y-Value

     */

    abstract public function getValueOfYForX($xValue);

    /**

     * Return the X-Value for a specified value of Y.

     *

     * @param float $yValue Y-Value

     *

     * @return float X-Value

     */

    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 string

     */

    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;

    }

    /** @var mixed */

    private static $scrutinizerZeroPointZero = 0.0;

    /**

     * @param mixed $x

     * @param mixed $y

     */

    private static function scrutinizerLooseCompare($x, $y): bool

    {

        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 === 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);

        }

        // 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 - $sumX ** 2) * ($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(

                function ($value) {

                    return $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($yValues, $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;

    }

}