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#  Copyright (c) 2003-2004 The Regents of the University of California.
#  Copyright (c) 2005 Gavin E. Crooks
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import random
from math import exp, log, sqrt
from typing import Tuple

import numpy as np
import scipy.optimize
from numpy import asarray, float64, shape, zeros
from scipy.special import digamma, gamma, gammaincc, polygamma


class Dirichlet(object):
    """The Dirichlet probability distribution. The Dirichlet is a continuous
    multivariate probability distribution across non-negative unit length
    vectors. In other words, the Dirichlet is a probability distribution of
    probability distributions. It is conjugate to the multinomial
    distribution and is widely used in Bayesian statistics.

    The Dirichlet probability distribution of order K-1 is

     p(theta_1,...,theta_K) d theta_1 ... d theta_K =
        (1/Z) prod_i=1,K theta_i^{alpha_i - 1} delta(1 -sum_i=1,K theta_i)

    The normalization factor Z can be expressed in terms of gamma functions:

      Z = {prod_i=1,K Gamma(alpha_i)} / {Gamma( sum_i=1,K alpha_i)}

    The K constants, alpha_1,...,alpha_K, must be positive. The K parameters,
    theta_1,...,theta_K are nonnegative and sum to 1.

    Status:
        Alpha
    """

    __slots__ = (
        "alpha",
        "_total",
        "_mean",
    )

    def __init__(self, alpha: np.ndarray) -> None:
        """
        Args:
            - alpha  -- The parameters of the Dirichlet prior distribution.
                        A vector of non-negative real numbers.
        """
        # TODO: Check that alphas are positive
        # TODO: what if alpha's not one dimensional?
        self.alpha = asarray(alpha, float64)

        self._total = sum(alpha)
        self._mean = self.alpha / self._total

    def sample(self) -> np.ndarray:
        """Return a randomly generated probability vector.

        Random samples are generated by sampling K values from gamma
        distributions with parameters a=\alpha_i, b=1, and renormalizing.

        Ref:
            A.M. Law, W.D. Kelton, Simulation Modeling and Analysis (1991).
        Authors:
            Gavin E. Crooks <gec@compbio.berkeley.edu> (2002)
        """
        alpha = self.alpha
        K = len(alpha)
        theta = zeros((K,), float64)

        for k in range(K):
            theta[k] = random.gammavariate(alpha[k], 1.0)
        theta /= sum(theta)

        return theta

    def mean(self) -> np.ndarray:
        return self._mean

    def covariance(self) -> float:
        alpha = self.alpha
        A = sum(alpha)
        # A2 = A * A
        K = len(alpha)
        cv = zeros((K, K), float64)

        for i in range(K):
            cv[i, i] = alpha[i] * (1.0 - alpha[i] / A) / (A * (A + 1.0))

        for i in range(K):
            for j in range(i + 1, K):
                v = -alpha[i] * alpha[j] / (A * A * (A + 1.0))
                cv[i, j] = v
                cv[j, i] = v
        return cv

    def mean_x(self, x: np.ndarray) -> float:
        x = asarray(x, float64)
        if shape(x) != shape(self.alpha):
            raise ValueError("Argument must be same dimension as Dirichlet")
        return sum(x * self.mean())

    def variance_x(self, x: np.ndarray) -> float:
        x = asarray(x, float64)
        if shape(x) != shape(self.alpha):
            raise ValueError("Argument must be same dimension as Dirichlet")

        cv = self.covariance()
        var = np.dot(np.dot(np.transpose(x), cv), x)
        return var

    def mean_entropy(self) -> float:
        """Calculate the average entropy of probabilities sampled
        from this Dirichlet distribution.

        Returns:
            The average entropy.

        Ref:
            Wolpert & Wolf, PRE 53:6841-6854 (1996) Theorem 7
            (Warning: this paper contains typos.)
        Status:
            Alpha
        Authors:
            GEC 2005

        """
        # TODO: Optimize
        alpha = self.alpha
        A = float(sum(alpha))
        ent = 0.0
        for a in alpha:
            if a > 0:
                ent += -1.0 * a * digamma(1.0 + a)  # FIXME: Check
        ent /= A
        ent += digamma(A + 1.0)
        return ent

    def variance_entropy(self) -> float:
        """Calculate the variance of the Dirichlet entropy.

        Ref:
            Wolpert & Wolf, PRE 53:6841-6854 (1996) Theorem 8
            (Warning: this paper contains typos.)
        """
        alpha = self.alpha
        A = float(sum(alpha))
        A2 = A * (A + 1)
        L = len(alpha)

        dg1 = zeros((L), float64)
        dg2 = zeros((L), float64)
        tg2 = zeros((L), float64)

        for i in range(L):
            dg1[i] = digamma(alpha[i] + 1.0)
            dg2[i] = digamma(alpha[i] + 2.0)
            tg2[i] = polygamma(1, alpha[i] + 2.0)

        dg_Ap2 = digamma(A + 2.0)
        tg_Ap2 = polygamma(1, A + 2.0)

        mean = self.mean_entropy()
        var = 0.0

        for i in range(L):
            for j in range(L):
                if i != j:
                    var += (
                        ((dg1[i] - dg_Ap2) * (dg1[j] - dg_Ap2) - tg_Ap2)
                        * (alpha[i] * alpha[j])
                        / A2
                    )
                else:
                    var += (
                        ((dg2[i] - dg_Ap2) ** 2 + (tg2[i] - tg_Ap2))
                        * (alpha[i] * (alpha[i] + 1.0))
                        / A2
                    )

        var -= mean ** 2
        return var

    def mean_relative_entropy(self, pvec: np.ndarray) -> float:
        ln_p = np.log(pvec)
        return -self.mean_x(ln_p) - self.mean_entropy()

    def variance_relative_entropy(self, pvec: np.ndarray) -> float:
        ln_p = np.log(pvec)
        return self.variance_x(ln_p) + self.variance_entropy()

    def interval_relative_entropy(
        self, pvec: np.ndarray, frac: float
    ) -> Tuple[float, float]:
        mean = self.mean_relative_entropy(pvec)
        variance = self.variance_relative_entropy(pvec)
        sd = sqrt(variance)

        # If the variance is small, use the standard 95%
        # confidence interval: mean +/- 1.96 * sd
        if mean / sd > 3.0:
            return max(0.0, mean - sd * 1.96), mean + sd * 1.96

        g = Gamma.from_mean_variance(mean, variance)
        low_limit = g.inverse_cdf((1.0 - frac) / 2.0)
        high_limit = g.inverse_cdf(1.0 - (1.0 - frac) / 2.0)

        return low_limit, high_limit


class Gamma(object):
    """The gamma probability distribution. (Not to be confused with the
    gamma function.)


    """

    __slots__ = "alpha", "beta"

    def __init__(self, alpha: float, beta: float) -> None:
        if alpha <= 0.0:
            raise ValueError("alpha must be positive")
        if beta <= 0.0:
            raise ValueError("beta must be positive")
        self.alpha = alpha
        self.beta = beta

    @classmethod
    def from_shape_scale(cls, shape: float, scale: float) -> "Gamma":
        return cls(shape, 1.0 / scale)

    @classmethod
    def from_mean_variance(cls, mean: float, variance: float) -> "Gamma":
        alpha = mean ** 2 / variance
        beta = alpha / mean
        return cls(alpha, beta)

    def mean(self) -> float:
        return self.alpha / self.beta

    def variance(self) -> float:
        return self.alpha / (self.beta ** 2)

    def sample(self) -> float:
        return random.gammavariate(self.alpha, 1.0 / self.beta)

    def pdf(self, x: float) -> float:
        if x == 0.0:
            return 0.0
        a = self.alpha
        b = self.beta
        return (x ** (a - 1.0)) * exp(-b * x) * (b ** a) / gamma(a)

    def cdf(self, x: float) -> float:
        return 1.0 - gammaincc(self.alpha, self.beta * x)

    def inverse_cdf(self, p: float) -> float:
        def rootof(x: float) -> float:
            return self.cdf(exp(x)) - p

        root = scipy.optimize.newton(rootof, log(self.mean()))
        return exp(root)
