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Directory >> /var/www/html/back/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Calculation/Statistical/

File / Folder	Size	Action
.	-
Averages	--	ren
Distributions	--	ren
AggregateBase.php	2.053KB	edt ren
Averages.php	7.463KB	edt ren
Conditional.php	9.916KB	edt ren
Confidence.php	1.753KB	edt ren
Counts.php	2.677KB	edt ren
Deviations.php	4.362KB	edt ren
MaxMinBase.php	0.399KB	edt ren
Maximum.php	2.263KB	edt ren
Minimum.php	2.264KB	edt ren
Percentiles.php	6.222KB	edt ren
Permutations.php	3.656KB	edt ren
Size.php	2.441KB	edt ren
StandardDeviations.php	2.187KB	edt ren
Standardize.php	1.609KB	edt ren
StatisticalValidations.php	0.95KB	edt ren
Trends.php	14.162KB	edt ren
VarianceBase.php	0.771KB	edt ren
Variances.php	4.981KB	edt ren

<?php

namespace PhpOffice\PhpSpreadsheet\Calculation\Statistical;

use PhpOffice\PhpSpreadsheet\Calculation\ArrayEnabled;
use PhpOffice\PhpSpreadsheet\Calculation\Exception;
use PhpOffice\PhpSpreadsheet\Calculation\Functions;
use PhpOffice\PhpSpreadsheet\Calculation\Information\ExcelError;
use PhpOffice\PhpSpreadsheet\Shared\Trend\Trend;

class Trends
{
    use ArrayEnabled;

private static function filterTrendValues(array &$array1, array &$array2): void
    {
        foreach ($array1 as $key => $value) {
            if ((is_bool($value)) || (is_string($value)) || ($value === null)) {
                unset($array1[$key], $array2[$key]);
            }
        }
    }

/**
     * @param mixed $array1 should be array, but scalar is made into one
     * @param mixed $array2 should be array, but scalar is made into one
     */
    private static function checkTrendArrays(&$array1, &$array2): void
    {
        if (!is_array($array1)) {
            $array1 = [$array1];
        }
        if (!is_array($array2)) {
            $array2 = [$array2];
        }

$array1 = Functions::flattenArray($array1);
        $array2 = Functions::flattenArray($array2);

self::filterTrendValues($array1, $array2);
        self::filterTrendValues($array2, $array1);

// Reset the array indexes
        $array1 = array_merge($array1);
        $array2 = array_merge($array2);
    }

protected static function validateTrendArrays(array $yValues, array $xValues): void
    {
        $yValueCount = count($yValues);
        $xValueCount = count($xValues);

if (($yValueCount === 0) || ($yValueCount !== $xValueCount)) {
            throw new Exception(ExcelError::NA());
        } elseif ($yValueCount === 1) {
            throw new Exception(ExcelError::DIV0());
        }
    }

/**
     * CORREL.
     *
     * Returns covariance, the average of the products of deviations for each data point pair.
     *
     * @param mixed $yValues array of mixed Data Series Y
     * @param null|mixed $xValues array of mixed Data Series X
     *
     * @return float|string
     */
    public static function CORREL($yValues, $xValues = null)
    {
        if (($xValues === null) || (!is_array($yValues)) || (!is_array($xValues))) {
            return ExcelError::VALUE();
        }

try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

return $bestFitLinear->getCorrelation();
    }

/**
     * COVAR.
     *
     * Returns covariance, the average of the products of deviations for each data point pair.
     *
     * @param mixed $yValues array of mixed Data Series Y
     * @param mixed $xValues array of mixed Data Series X
     *
     * @return float|string
     */
    public static function COVAR($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

return $bestFitLinear->getCovariance();
    }

/**
     * FORECAST.
     *
     * Calculates, or predicts, a future value by using existing values.
     * The predicted value is a y-value for a given x-value.
     *
     * @param mixed $xValue Float value of X for which we want to find Y
     *                      Or can be an array of values
     * @param mixed $yValues array of mixed Data Series Y
     * @param mixed $xValues of mixed Data Series X
     *
     * @return array|bool|float|string
     *         If an array of numbers is passed as an argument, then the returned result will also be an array
     *            with the same dimensions
     */
    public static function FORECAST($xValue, $yValues, $xValues)
    {
        if (is_array($xValue)) {
            return self::evaluateArrayArgumentsSubset([self::class, __FUNCTION__], 1, $xValue, $yValues, $xValues);
        }

try {
            $xValue = StatisticalValidations::validateFloat($xValue);
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

return $bestFitLinear->getValueOfYForX($xValue);
    }

/**
     * GROWTH.
     *
     * Returns values along a predicted exponential Trend
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     * @param mixed[] $newValues Values of X for which we want to find Y
     * @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
     *
     * @return float[]
     */
    public static function GROWTH($yValues, $xValues = [], $newValues = [], $const = true)
    {
        $yValues = Functions::flattenArray($yValues);
        $xValues = Functions::flattenArray($xValues);
        $newValues = Functions::flattenArray($newValues);
        $const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);

$bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);
        if (empty($newValues)) {
            $newValues = $bestFitExponential->getXValues();
        }

$returnArray = [];
        foreach ($newValues as $xValue) {
            $returnArray[0][] = [$bestFitExponential->getValueOfYForX($xValue)];
        }

return $returnArray; //* @phpstan-ignore-line
    }

/**
     * INTERCEPT.
     *
     * Calculates the point at which a line will intersect the y-axis by using existing x-values and y-values.
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     *
     * @return float|string
     */
    public static function INTERCEPT($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

return $bestFitLinear->getIntersect();
    }

/**
     * LINEST.
     *
     * Calculates the statistics for a line by using the "least squares" method to calculate a straight line
     *     that best fits your data, and then returns an array that describes the line.
     *
     * @param mixed[] $yValues Data Series Y
     * @param null|mixed[] $xValues Data Series X
     * @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
     * @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
     *
     * @return array|int|string The result, or a string containing an error
     */
    public static function LINEST($yValues, $xValues = null, $const = true, $stats = false)
    {
        $const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
        $stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
        if ($xValues === null) {
            $xValues = $yValues;
        }

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);

if ($stats === true) {
            return [
                [
                    $bestFitLinear->getSlope(),
                    $bestFitLinear->getIntersect(),
                ],
                [
                    $bestFitLinear->getSlopeSE(),
                    ($const === false) ? ExcelError::NA() : $bestFitLinear->getIntersectSE(),
                ],
                [
                    $bestFitLinear->getGoodnessOfFit(),
                    $bestFitLinear->getStdevOfResiduals(),
                ],
                [
                    $bestFitLinear->getF(),
                    $bestFitLinear->getDFResiduals(),
                ],
                [
                    $bestFitLinear->getSSRegression(),
                    $bestFitLinear->getSSResiduals(),
                ],
            ];
        }

return [
            $bestFitLinear->getSlope(),
            $bestFitLinear->getIntersect(),
        ];
    }

/**
     * LOGEST.
     *
     * Calculates an exponential curve that best fits the X and Y data series,
     *        and then returns an array that describes the line.
     *
     * @param mixed[] $yValues Data Series Y
     * @param null|mixed[] $xValues Data Series X
     * @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
     * @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
     *
     * @return array|int|string The result, or a string containing an error
     */
    public static function LOGEST($yValues, $xValues = null, $const = true, $stats = false)
    {
        $const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
        $stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
        if ($xValues === null) {
            $xValues = $yValues;
        }

foreach ($yValues as $value) {
            if ($value < 0.0) {
                return ExcelError::NAN();
            }
        }

$bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);

if ($stats === true) {
            return [
                [
                    $bestFitExponential->getSlope(),
                    $bestFitExponential->getIntersect(),
                ],
                [
                    $bestFitExponential->getSlopeSE(),
                    ($const === false) ? ExcelError::NA() : $bestFitExponential->getIntersectSE(),
                ],
                [
                    $bestFitExponential->getGoodnessOfFit(),
                    $bestFitExponential->getStdevOfResiduals(),
                ],
                [
                    $bestFitExponential->getF(),
                    $bestFitExponential->getDFResiduals(),
                ],
                [
                    $bestFitExponential->getSSRegression(),
                    $bestFitExponential->getSSResiduals(),
                ],
            ];
        }

return [
            $bestFitExponential->getSlope(),
            $bestFitExponential->getIntersect(),
        ];
    }

/**
     * RSQ.
     *
     * Returns the square of the Pearson product moment correlation coefficient through data points
     *     in known_y's and known_x's.
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     *
     * @return float|string The result, or a string containing an error
     */
    public static function RSQ($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

return $bestFitLinear->getGoodnessOfFit();
    }

/**
     * SLOPE.
     *
     * Returns the slope of the linear regression line through data points in known_y's and known_x's.
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     *
     * @return float|string The result, or a string containing an error
     */
    public static function SLOPE($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

return $bestFitLinear->getSlope();
    }

/**
     * STEYX.
     *
     * Returns the standard error of the predicted y-value for each x in the regression.
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     *
     * @return float|string
     */
    public static function STEYX($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

return $bestFitLinear->getStdevOfResiduals();
    }

/**
     * TREND.
     *
     * Returns values along a linear Trend
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     * @param mixed[] $newValues Values of X for which we want to find Y
     * @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
     *
     * @return float[]
     */
    public static function TREND($yValues, $xValues = [], $newValues = [], $const = true)
    {
        $yValues = Functions::flattenArray($yValues);
        $xValues = Functions::flattenArray($xValues);
        $newValues = Functions::flattenArray($newValues);
        $const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);

$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);
        if (empty($newValues)) {
            $newValues = $bestFitLinear->getXValues();
        }

$returnArray = [];
        foreach ($newValues as $xValue) {
            $returnArray[0][] = [$bestFitLinear->getValueOfYForX($xValue)];
        }

return $returnArray; //* @phpstan-ignore-line
    }
}

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