<?php

namespace MathPHP\Probability\Distribution\Continuous;

use MathPHP\Exception\MathException;
use MathPHP\Functions\Special;
use MathPHP\Functions\Support;

/**
 * Beta distribution
 * https://en.wikipedia.org/wiki/Beta_distribution
 */
class Beta extends Continuous
{
    /**
     * Distribution parameter bounds limits
     * α ∈ (0,∞)
     * β ∈ (0,∞)
     * @var array{"α": string, "β": string}
     */
    public const PARAMETER_LIMITS = [
        'α' => '(0,∞)',
        'β' => '(0,∞)',
    ];

    /**
     * Distribution support bounds limits
     * x ∈ [0,1]
     * @var array{x: string}
     */
    public const SUPPORT_LIMITS = [
        'x' => '[0,1]',
    ];

    /** @var int|float Shape Parameter */
    protected $α;

    /** @var int|float Shape Parameter */
    protected $β;

    /**
     * Constructor
     *
     * @param float $α shape parameter α > 0
     * @param float $β shape parameter β > 0
     */
    public function __construct(float $α, float $β)
    {
        parent::__construct($α, $β);
    }

    /**
     * Probability density function
     *
     *       xᵃ⁻¹(1 - x)ᵝ⁻¹
     * pdf = --------------
     *           B(α,β)
     *
     * @param float $x x ∈ (0,1)
     *
     * @return float
     */
    public function pdf(float $x): float
    {
        Support::checkLimits(self::SUPPORT_LIMITS, ['x' => $x]);

        $α = $this->α;
        $β = $this->β;

        $xᵃ⁻¹ = \pow($x, $α - 1);
        $⟮1 − x⟯ᵝ⁻¹ = \pow(1 - $x, $β - 1);
        $B⟮α、β⟯ = Special::beta($α, $β);
        return ($xᵃ⁻¹ * $⟮1 − x⟯ᵝ⁻¹) / $B⟮α、β⟯;
    }

    /**
     * Cumulative distribution function
     *
     * cdf = Iₓ(α,β)
     *
     * @param float $x x ∈ (0,1)
     *
     * @return float
     */
    public function cdf(float $x): float
    {
        Support::checkLimits(self::SUPPORT_LIMITS, ['x' => $x]);

        $α = $this->α;
        $β = $this->β;

        return Special::regularizedIncompleteBeta($x, $α, $β);
    }

    /**
     * Inverse cumulative distribution function (quantile function)
     * Iterative method
     *
     * @param float $x
     * @param float $tolerance (optional)
     * @param int   $max_iterations (optional)
     *
     * @return float
     *
     * @throws MathException if it fails to converge on a guess within the tolerance
     */
    public function inverse(float $x, float $tolerance = 1.0e-15, int $max_iterations = 200): float
    {
        [$a, $b] = [0, 2];

        for ($i = 0; $i < $max_iterations; $i++) {
            $guess = ($a + $b) / 2;
            $cdf   = $this->cdf($guess);

            if ($cdf == $x || $cdf == 0) {
                $b = $a;
            } elseif ($cdf > $x) {
                $b = $guess;
            } else {
                $a = $guess;
            }

            if (($b - $a) <= $tolerance) {
                return $guess;
            }
        }

        throw new MathException("Failed to converge on a Beta inverse within a tolerance of $tolerance after {$max_iterations} iterations");
    }

    /**
     * Mean of the distribution
     *
     *       α
     * μ = -----
     *     α + β
     *
     * @return float
     */
    public function mean(): float
    {
        $α = $this->α;
        $β = $this->β;

        return $α / ($α + $β);
    }

    /**
     * Median of the distribution
     *
     * Closed forms
     *  - For symmetric cases α = β, median = 1/2
     *  - For α = 1 and β > 0, median = 1 - 2^(-1/β)
     *  - For α > 0 and β = 1, median = 2^(-1/α)
     *  - For α = 3 and β = 2, median = 0.6142724318676105
     *  - For α = 2 and β = 3, median = 0.38572756813238945
     *
     * Approximation
     *             α  - ⅓
     *  median =  ---------
     *            α + β - ⅔
     *
     * @see https://en.wikipedia.org/wiki/Beta_distribution#Median
     *
     * @return float
     */
    public function median(): float
    {
        $α = $this->α;
        $β = $this->β;

        if ($α == $β) {
            return 0.5;
        }

        if ($α == 1 && $β > 0) {
            return 1 - 2 ** (-1 / $β);
        }

        if ($β == 1 && $α > 0) {
            return 2 ** (-1 / $α);
        }

        if ($α == 3 && $β == 2) {
            return 0.6142724318676105;
        }

        if ($α == 2 && $β == 3) {
            return 0.38572756813238945;
        }

        return ($α - 1 / 3) / ($α + $β - 2 / 3);
    }

    /**
     * Mode of the distribution
     *
     *          α - 1
     * mode = ---------    α, β > 1
     *        α + β - 2
     *
     * mode = 0            α = 1, β > 1
     * mode = 1            α > 1, β = 1
     *
     * @return float
     */
    public function mode(): float
    {
        $α = $this->α;
        $β = $this->β;

        if ($α == 1 && $β > 1) {
            return 0;
        }
        if ($α > 1 && $β == 1) {
            return 1;
        }

        return ($α - 1) / ($α + $β - 2);
    }

    /**
     * Variance of the distribution
     *
     *                  αβ
     * var[X] = -------------------
     *          ⟮α ＋ β⟯²⟮α ＋ β ＋ 1⟯
     *
     * @return float
     */
    public function variance(): float
    {
        $α = $this->α;
        $β = $this->β;

        $αβ          = $α * $β;
        $⟮α ＋ β⟯²     = ($α + $β) ** 2;
        $⟮α ＋ β ＋ 1⟯ = $α + $β + 1;

        return $αβ / ($⟮α ＋ β⟯² * $⟮α ＋ β ＋ 1⟯);
    }
}
