<?php

namespace LWVendor\PhpOffice\PhpSpreadsheet\Calculation\Statistical\Distributions;

use LWVendor\PhpOffice\PhpSpreadsheet\Calculation\ArrayEnabled;
use LWVendor\PhpOffice\PhpSpreadsheet\Calculation\Exception;
use LWVendor\PhpOffice\PhpSpreadsheet\Calculation\Functions;
use LWVendor\PhpOffice\PhpSpreadsheet\Calculation\Information\ExcelError;
class Beta
{
    use ArrayEnabled;
    private const MAX_ITERATIONS = 256;
    private const LOG_GAMMA_X_MAX_VALUE = 2.55E+305;
    private const XMININ = 2.23E-308;
    /**
     * BETADIST.
     *
     * Returns the beta distribution.
     *
     * @param mixed $value Float value at which you want to evaluate the distribution
     *                      Or can be an array of values
     * @param mixed $alpha Parameter to the distribution as a float
     *                      Or can be an array of values
     * @param mixed $beta Parameter to the distribution as a float
     *                      Or can be an array of values
     * @param mixed $rMin as an float
     *                      Or can be an array of values
     * @param mixed $rMax as an float
     *                      Or can be an array of values
     *
     * @return array|float|string
     *         If an array of numbers is passed as an argument, then the returned result will also be an array
     *            with the same dimensions
     */
    public static function distribution($value, $alpha, $beta, $rMin = 0.0, $rMax = 1.0)
    {
        if (\is_array($value) || \is_array($alpha) || \is_array($beta) || \is_array($rMin) || \is_array($rMax)) {
            return self::evaluateArrayArguments([self::class, __FUNCTION__], $value, $alpha, $beta, $rMin, $rMax);
        }
        $rMin = $rMin ?? 0.0;
        $rMax = $rMax ?? 1.0;
        try {
            $value = DistributionValidations::validateFloat($value);
            $alpha = DistributionValidations::validateFloat($alpha);
            $beta = DistributionValidations::validateFloat($beta);
            $rMax = DistributionValidations::validateFloat($rMax);
            $rMin = DistributionValidations::validateFloat($rMin);
        } catch (Exception $e) {
            return $e->getMessage();
        }
        if ($rMin > $rMax) {
            $tmp = $rMin;
            $rMin = $rMax;
            $rMax = $tmp;
        }
        if ($value < $rMin || $value > $rMax || $alpha <= 0 || $beta <= 0 || $rMin == $rMax) {
            return ExcelError::NAN();
        }
        $value -= $rMin;
        $value /= $rMax - $rMin;
        return self::incompleteBeta($value, $alpha, $beta);
    }
    /**
     * BETAINV.
     *
     * Returns the inverse of the Beta distribution.
     *
     * @param mixed $probability Float probability at which you want to evaluate the distribution
     *                      Or can be an array of values
     * @param mixed $alpha Parameter to the distribution as a float
     *                      Or can be an array of values
     * @param mixed $beta Parameter to the distribution as a float
     *                      Or can be an array of values
     * @param mixed $rMin Minimum value as a float
     *                      Or can be an array of values
     * @param mixed $rMax Maximum value as a float
     *                      Or can be an array of values
     *
     * @return array|float|string
     *         If an array of numbers is passed as an argument, then the returned result will also be an array
     *            with the same dimensions
     */
    public static function inverse($probability, $alpha, $beta, $rMin = 0.0, $rMax = 1.0)
    {
        if (\is_array($probability) || \is_array($alpha) || \is_array($beta) || \is_array($rMin) || \is_array($rMax)) {
            return self::evaluateArrayArguments([self::class, __FUNCTION__], $probability, $alpha, $beta, $rMin, $rMax);
        }
        $rMin = $rMin ?? 0.0;
        $rMax = $rMax ?? 1.0;
        try {
            $probability = DistributionValidations::validateProbability($probability);
            $alpha = DistributionValidations::validateFloat($alpha);
            $beta = DistributionValidations::validateFloat($beta);
            $rMax = DistributionValidations::validateFloat($rMax);
            $rMin = DistributionValidations::validateFloat($rMin);
        } catch (Exception $e) {
            return $e->getMessage();
        }
        if ($rMin > $rMax) {
            $tmp = $rMin;
            $rMin = $rMax;
            $rMax = $tmp;
        }
        if ($alpha <= 0 || $beta <= 0 || $rMin == $rMax || $probability <= 0.0) {
            return ExcelError::NAN();
        }
        return self::calculateInverse($probability, $alpha, $beta, $rMin, $rMax);
    }
    /**
     * @return float|string
     */
    private static function calculateInverse(float $probability, float $alpha, float $beta, float $rMin, float $rMax)
    {
        $a = 0;
        $b = 2;
        $guess = ($a + $b) / 2;
        $i = 0;
        while ($b - $a > Functions::PRECISION && ++$i <= self::MAX_ITERATIONS) {
            $guess = ($a + $b) / 2;
            $result = self::distribution($guess, $alpha, $beta);
            if ($result === $probability || $result === 0.0) {
                $b = $a;
            } elseif ($result > $probability) {
                $b = $guess;
            } else {
                $a = $guess;
            }
        }
        if ($i === self::MAX_ITERATIONS) {
            return ExcelError::NA();
        }
        return \round($rMin + $guess * ($rMax - $rMin), 12);
    }
    /**
     * Incomplete beta function.
     *
     * @author Jaco van Kooten
     * @author Paul Meagher
     *
     * The computation is based on formulas from Numerical Recipes, Chapter 6.4 (W.H. Press et al, 1992).
     *
     * @param float $x require 0<=x<=1
     * @param float $p require p>0
     * @param float $q require q>0
     *
     * @return float 0 if x<0, p<=0, q<=0 or p+q>2.55E305 and 1 if x>1 to avoid errors and over/underflow
     */
    public static function incompleteBeta(float $x, float $p, float $q) : float
    {
        if ($x <= 0.0) {
            return 0.0;
        } elseif ($x >= 1.0) {
            return 1.0;
        } elseif ($p <= 0.0 || $q <= 0.0 || $p + $q > self::LOG_GAMMA_X_MAX_VALUE) {
            return 0.0;
        }
        $beta_gam = \exp(0 - self::logBeta($p, $q) + $p * \log($x) + $q * \log(1.0 - $x));
        if ($x < ($p + 1.0) / ($p + $q + 2.0)) {
            return $beta_gam * self::betaFraction($x, $p, $q) / $p;
        }
        return 1.0 - $beta_gam * self::betaFraction(1 - $x, $q, $p) / $q;
    }
    // Function cache for logBeta function
    /** @var float */
    private static $logBetaCacheP = 0.0;
    /** @var float */
    private static $logBetaCacheQ = 0.0;
    /** @var float */
    private static $logBetaCacheResult = 0.0;
    /**
     * The natural logarithm of the beta function.
     *
     * @param float $p require p>0
     * @param float $q require q>0
     *
     * @return float 0 if p<=0, q<=0 or p+q>2.55E305 to avoid errors and over/underflow
     *
     * @author Jaco van Kooten
     */
    private static function logBeta(float $p, float $q) : float
    {
        if ($p != self::$logBetaCacheP || $q != self::$logBetaCacheQ) {
            self::$logBetaCacheP = $p;
            self::$logBetaCacheQ = $q;
            if ($p <= 0.0 || $q <= 0.0 || $p + $q > self::LOG_GAMMA_X_MAX_VALUE) {
                self::$logBetaCacheResult = 0.0;
            } else {
                self::$logBetaCacheResult = Gamma::logGamma($p) + Gamma::logGamma($q) - Gamma::logGamma($p + $q);
            }
        }
        return self::$logBetaCacheResult;
    }
    /**
     * Evaluates of continued fraction part of incomplete beta function.
     * Based on an idea from Numerical Recipes (W.H. Press et al, 1992).
     *
     * @author Jaco van Kooten
     */
    private static function betaFraction(float $x, float $p, float $q) : float
    {
        $c = 1.0;
        $sum_pq = $p + $q;
        $p_plus = $p + 1.0;
        $p_minus = $p - 1.0;
        $h = 1.0 - $sum_pq * $x / $p_plus;
        if (\abs($h) < self::XMININ) {
            $h = self::XMININ;
        }
        $h = 1.0 / $h;
        $frac = $h;
        $m = 1;
        $delta = 0.0;
        while ($m <= self::MAX_ITERATIONS && \abs($delta - 1.0) > Functions::PRECISION) {
            $m2 = 2 * $m;
            // even index for d
            $d = $m * ($q - $m) * $x / (($p_minus + $m2) * ($p + $m2));
            $h = 1.0 + $d * $h;
            if (\abs($h) < self::XMININ) {
                $h = self::XMININ;
            }
            $h = 1.0 / $h;
            $c = 1.0 + $d / $c;
            if (\abs($c) < self::XMININ) {
                $c = self::XMININ;
            }
            $frac *= $h * $c;
            // odd index for d
            $d = -($p + $m) * ($sum_pq + $m) * $x / (($p + $m2) * ($p_plus + $m2));
            $h = 1.0 + $d * $h;
            if (\abs($h) < self::XMININ) {
                $h = self::XMININ;
            }
            $h = 1.0 / $h;
            $c = 1.0 + $d / $c;
            if (\abs($c) < self::XMININ) {
                $c = self::XMININ;
            }
            $delta = $h * $c;
            $frac *= $delta;
            ++$m;
        }
        return $frac;
    }
    /*
    private static function betaValue(float $a, float $b): float
    {
        return (Gamma::gammaValue($a) * Gamma::gammaValue($b)) /
            Gamma::gammaValue($a + $b);
    }
    
    private static function regularizedIncompleteBeta(float $value, float $a, float $b): float
    {
        return self::incompleteBeta($value, $a, $b) / self::betaValue($a, $b);
    }
    */
}
