"""
Finite Discrete Random Variables - Prebuilt variable types

Contains
========
FiniteRV
DiscreteUniform
Die
Bernoulli
Coin
Binomial
BetaBinomial
Hypergeometric
Rademacher
IdealSoliton
RobustSoliton
"""


from sympy import (S, sympify, Rational, binomial, cacheit, Integer,
                   Dummy, Eq, Intersection, Interval, log, Range,
                   Symbol, Lambda, Piecewise, Or, Gt, Lt, Ge, Le, Contains)
from sympy import beta as beta_fn
from sympy.stats.frv import (SingleFiniteDistribution,
                             SingleFinitePSpace)
from sympy.stats.rv import _value_check, Density, is_random


__all__ = ['FiniteRV',
'DiscreteUniform',
'Die',
'Bernoulli',
'Coin',
'Binomial',
'BetaBinomial',
'Hypergeometric',
'Rademacher',
'IdealSoliton',
'RobustSoliton',
]

def rv(name, cls, *args, **kwargs):
    args = list(map(sympify, args))
    dist = cls(*args)
    if kwargs.pop('check', True):
        dist.check(*args)
    pspace = SingleFinitePSpace(name, dist)
    if any(is_random(arg) for arg in args):
        from sympy.stats.compound_rv import CompoundPSpace, CompoundDistribution
        pspace = CompoundPSpace(name, CompoundDistribution(dist))
    return pspace.value

class FiniteDistributionHandmade(SingleFiniteDistribution):

    @property
    def dict(self):
        return self.args[0]

    def pmf(self, x):
        x = Symbol('x')
        return Lambda(x, Piecewise(*(
            [(v, Eq(k, x)) for k, v in self.dict.items()] + [(S.Zero, True)])))

    @property
    def set(self):
        return set(self.dict.keys())

    @staticmethod
    def check(density):
        for p in density.values():
            _value_check((p >= 0, p <= 1),
                        "Probability at a point must be between 0 and 1.")
        val = sum(density.values())
        _value_check(Eq(val, 1) != S.false, "Total Probability must be 1.")

def FiniteRV(name, density, **kwargs):
    r"""
    Create a Finite Random Variable given a dict representing the density.

    Parameters
    ==========

    name : Symbol
        Represents name of the random variable.
    density: A dict
        Dictionary conatining the pdf of finite distribution
    check : bool
        If True, it will check whether the given density
        integrates to 1 over the given set. If False, it
        will not perform this check. Default is False.

    Examples
    ========

    >>> from sympy.stats import FiniteRV, P, E

    >>> density = {0: .1, 1: .2, 2: .3, 3: .4}
    >>> X = FiniteRV('X', density)

    >>> E(X)
    2.00000000000000
    >>> P(X >= 2)
    0.700000000000000

    Returns
    =======

    RandomSymbol

    """
    # have a default of False while `rv` should have a default of True
    kwargs['check'] = kwargs.pop('check', False)
    return rv(name, FiniteDistributionHandmade, density, **kwargs)

class DiscreteUniformDistribution(SingleFiniteDistribution):

    @staticmethod
    def check(*args):
        # not using _value_check since there is a
        # suggestion for the user
        if len(set(args)) != len(args):
            from sympy.utilities.iterables import multiset
            from sympy.utilities.misc import filldedent
            weights = multiset(args)
            n = Integer(len(args))
            for k in weights:
                weights[k] /= n
            raise ValueError(filldedent("""
                Repeated args detected but set expected. For a
                distribution having different weights for each
                item use the following:""") + (
                '\nS("FiniteRV(%s, %s)")' % ("'X'", weights)))

    @property
    def p(self):
        return Rational(1, len(self.args))

    @property  # type: ignore
    @cacheit
    def dict(self):
        return {k: self.p for k in self.set}

    @property
    def set(self):
        return set(self.args)

    def pmf(self, x):
        if x in self.args:
            return self.p
        else:
            return S.Zero


def DiscreteUniform(name, items):
    r"""
    Create a Finite Random Variable representing a uniform distribution over
    the input set.

    Parameters
    ==========

    items: list/tuple
        Items over which Uniform distribution is to be made

    Examples
    ========

    >>> from sympy.stats import DiscreteUniform, density
    >>> from sympy import symbols

    >>> X = DiscreteUniform('X', symbols('a b c')) # equally likely over a, b, c
    >>> density(X).dict
    {a: 1/3, b: 1/3, c: 1/3}

    >>> Y = DiscreteUniform('Y', list(range(5))) # distribution over a range
    >>> density(Y).dict
    {0: 1/5, 1: 1/5, 2: 1/5, 3: 1/5, 4: 1/5}

    Returns
    =======

    RandomSymbol

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Discrete_uniform_distribution
    .. [2] http://mathworld.wolfram.com/DiscreteUniformDistribution.html

    """
    return rv(name, DiscreteUniformDistribution, *items)


class DieDistribution(SingleFiniteDistribution):
    _argnames = ('sides',)

    @staticmethod
    def check(sides):
        _value_check((sides.is_positive, sides.is_integer),
                    "number of sides must be a positive integer.")

    @property
    def is_symbolic(self):
        return not self.sides.is_number

    @property
    def high(self):
        return self.sides

    @property
    def low(self):
        return S.One

    @property
    def set(self):
        if self.is_symbolic:
            return Intersection(S.Naturals0, Interval(0, self.sides))
        return set(map(Integer, list(range(1, self.sides + 1))))

    def pmf(self, x):
        x = sympify(x)
        if not (x.is_number or x.is_Symbol or is_random(x)):
            raise ValueError("'x' expected as an argument of type 'number' or 'Symbol' or , "
                        "'RandomSymbol' not %s" % (type(x)))
        cond = Ge(x, 1) & Le(x, self.sides) & Contains(x, S.Integers)
        return Piecewise((S.One/self.sides, cond), (S.Zero, True))

def Die(name, sides=6):
    r"""
    Create a Finite Random Variable representing a fair die.

    Parameters
    ==========

    sides: Integer
        Represents the number of sides of the Die, by default is 6

    Examples
    ========

    >>> from sympy.stats import Die, density
    >>> from sympy import Symbol

    >>> D6 = Die('D6', 6) # Six sided Die
    >>> density(D6).dict
    {1: 1/6, 2: 1/6, 3: 1/6, 4: 1/6, 5: 1/6, 6: 1/6}

    >>> D4 = Die('D4', 4) # Four sided Die
    >>> density(D4).dict
    {1: 1/4, 2: 1/4, 3: 1/4, 4: 1/4}

    >>> n = Symbol('n', positive=True, integer=True)
    >>> Dn = Die('Dn', n) # n sided Die
    >>> density(Dn).dict
    Density(DieDistribution(n))
    >>> density(Dn).dict.subs(n, 4).doit()
    {1: 1/4, 2: 1/4, 3: 1/4, 4: 1/4}

    Returns
    =======

    RandomSymbol
    """

    return rv(name, DieDistribution, sides)


class BernoulliDistribution(SingleFiniteDistribution):
    _argnames = ('p', 'succ', 'fail')

    @staticmethod
    def check(p, succ, fail):
        _value_check((p >= 0, p <= 1),
                    "p should be in range [0, 1].")

    @property
    def set(self):
        return {self.succ, self.fail}

    def pmf(self, x):
        if isinstance(self.succ, Symbol) and isinstance(self.fail, Symbol):
            return Piecewise((self.p, x == self.succ),
                             (1 - self.p, x == self.fail),
                             (S.Zero, True))
        return Piecewise((self.p, Eq(x, self.succ)),
                         (1 - self.p, Eq(x, self.fail)),
                         (S.Zero, True))


def Bernoulli(name, p, succ=1, fail=0):
    r"""
    Create a Finite Random Variable representing a Bernoulli process.

    Parameters
    ==========

    p : Rational number between 0 and 1
       Represents probability of success
    succ : Integer/symbol/string
       Represents event of success
    fail : Integer/symbol/string
       Represents event of failure

    Examples
    ========

    >>> from sympy.stats import Bernoulli, density
    >>> from sympy import S

    >>> X = Bernoulli('X', S(3)/4) # 1-0 Bernoulli variable, probability = 3/4
    >>> density(X).dict
    {0: 1/4, 1: 3/4}

    >>> X = Bernoulli('X', S.Half, 'Heads', 'Tails') # A fair coin toss
    >>> density(X).dict
    {Heads: 1/2, Tails: 1/2}

    Returns
    =======

    RandomSymbol

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Bernoulli_distribution
    .. [2] http://mathworld.wolfram.com/BernoulliDistribution.html

    """

    return rv(name, BernoulliDistribution, p, succ, fail)


def Coin(name, p=S.Half):
    r"""
    Create a Finite Random Variable representing a Coin toss.

    Parameters
    ==========

    p : Rational Numeber between 0 and 1
      Represents probability of getting "Heads", by default is Half

    Examples
    ========

    >>> from sympy.stats import Coin, density
    >>> from sympy import Rational

    >>> C = Coin('C') # A fair coin toss
    >>> density(C).dict
    {H: 1/2, T: 1/2}

    >>> C2 = Coin('C2', Rational(3, 5)) # An unfair coin
    >>> density(C2).dict
    {H: 3/5, T: 2/5}

    Returns
    =======

    RandomSymbol

    See Also
    ========

    sympy.stats.Binomial

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Coin_flipping

    """
    return rv(name, BernoulliDistribution, p, 'H', 'T')


class BinomialDistribution(SingleFiniteDistribution):
    _argnames = ('n', 'p', 'succ', 'fail')

    @staticmethod
    def check(n, p, succ, fail):
        _value_check((n.is_integer, n.is_nonnegative),
                    "'n' must be nonnegative integer.")
        _value_check((p <= 1, p >= 0),
                    "p should be in range [0, 1].")

    @property
    def high(self):
        return self.n

    @property
    def low(self):
        return S.Zero

    @property
    def is_symbolic(self):
        return not self.n.is_number

    @property
    def set(self):
        if self.is_symbolic:
            return Intersection(S.Naturals0, Interval(0, self.n))
        return set(self.dict.keys())

    def pmf(self, x):
        n, p = self.n, self.p
        x = sympify(x)
        if not (x.is_number or x.is_Symbol or is_random(x)):
            raise ValueError("'x' expected as an argument of type 'number' or 'Symbol' or , "
                        "'RandomSymbol' not %s" % (type(x)))
        cond = Ge(x, 0) & Le(x, n) & Contains(x, S.Integers)
        return Piecewise((binomial(n, x) * p**x * (1 - p)**(n - x), cond), (S.Zero, True))

    @property  # type: ignore
    @cacheit
    def dict(self):
        if self.is_symbolic:
            return Density(self)
        return {k*self.succ + (self.n-k)*self.fail: self.pmf(k)
                    for k in range(0, self.n + 1)}


def Binomial(name, n, p, succ=1, fail=0):
    r"""
    Create a Finite Random Variable representing a binomial distribution.

    Parameters
    ==========

    n : Positive Integer
      Represents number of trials
    p : Rational Number between 0 and 1
      Represents probability of success
    succ : Integer/symbol/string
      Represents event of success, by default is 1
    fail : Integer/symbol/string
      Represents event of failure, by default is 0

    Examples
    ========

    >>> from sympy.stats import Binomial, density
    >>> from sympy import S, Symbol

    >>> X = Binomial('X', 4, S.Half) # Four "coin flips"
    >>> density(X).dict
    {0: 1/16, 1: 1/4, 2: 3/8, 3: 1/4, 4: 1/16}

    >>> n = Symbol('n', positive=True, integer=True)
    >>> p = Symbol('p', positive=True)
    >>> X = Binomial('X', n, S.Half) # n "coin flips"
    >>> density(X).dict
    Density(BinomialDistribution(n, 1/2, 1, 0))
    >>> density(X).dict.subs(n, 4).doit()
    {0: 1/16, 1: 1/4, 2: 3/8, 3: 1/4, 4: 1/16}

    Returns
    =======

    RandomSymbol

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Binomial_distribution
    .. [2] http://mathworld.wolfram.com/BinomialDistribution.html

    """

    return rv(name, BinomialDistribution, n, p, succ, fail)

#-------------------------------------------------------------------------------
# Beta-binomial distribution ----------------------------------------------------------

class BetaBinomialDistribution(SingleFiniteDistribution):
    _argnames = ('n', 'alpha', 'beta')

    @staticmethod
    def check(n, alpha, beta):
        _value_check((n.is_integer, n.is_nonnegative),
        "'n' must be nonnegative integer. n = %s." % str(n))
        _value_check((alpha > 0),
        "'alpha' must be: alpha > 0 . alpha = %s" % str(alpha))
        _value_check((beta > 0),
        "'beta' must be: beta > 0 . beta = %s" % str(beta))

    @property
    def high(self):
        return self.n

    @property
    def low(self):
        return S.Zero

    @property
    def is_symbolic(self):
        return not self.n.is_number

    @property
    def set(self):
        if self.is_symbolic:
            return Intersection(S.Naturals0, Interval(0, self.n))
        return set(map(Integer, list(range(0, self.n + 1))))

    def pmf(self, k):
        n, a, b = self.n, self.alpha, self.beta
        return binomial(n, k) * beta_fn(k + a, n - k + b) / beta_fn(a, b)


def BetaBinomial(name, n, alpha, beta):
    r"""
    Create a Finite Random Variable representing a Beta-binomial distribution.

    Parameters
    ==========

    n : Positive Integer
      Represents number of trials
    alpha : Real positive number
    beta : Real positive number

    Examples
    ========

    >>> from sympy.stats import BetaBinomial, density

    >>> X = BetaBinomial('X', 2, 1, 1)
    >>> density(X).dict
    {0: 1/3, 1: 2*beta(2, 2), 2: 1/3}

    Returns
    =======

    RandomSymbol

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Beta-binomial_distribution
    .. [2] http://mathworld.wolfram.com/BetaBinomialDistribution.html

    """

    return rv(name, BetaBinomialDistribution, n, alpha, beta)


class HypergeometricDistribution(SingleFiniteDistribution):
    _argnames = ('N', 'm', 'n')

    @staticmethod
    def check(n, N, m):
        _value_check((N.is_integer, N.is_nonnegative),
                     "'N' must be nonnegative integer. N = %s." % str(n))
        _value_check((n.is_integer, n.is_nonnegative),
                     "'n' must be nonnegative integer. n = %s." % str(n))
        _value_check((m.is_integer, m.is_nonnegative),
                     "'m' must be nonnegative integer. m = %s." % str(n))

    @property
    def is_symbolic(self):
        return any(not x.is_number for x in (self.N, self.m, self.n))

    @property
    def high(self):
        return Piecewise((self.n, Lt(self.n, self.m) != False), (self.m, True))

    @property
    def low(self):
        return Piecewise((0, Gt(0, self.n + self.m - self.N) != False), (self.n + self.m - self.N, True))

    @property
    def set(self):
        N, m, n = self.N, self.m, self.n
        if self.is_symbolic:
            return Intersection(S.Naturals0, Interval(self.low, self.high))
        return {i for i in range(max(0, n + m - N), min(n, m) + 1)}

    def pmf(self, k):
        N, m, n = self.N, self.m, self.n
        return S(binomial(m, k) * binomial(N - m, n - k))/binomial(N, n)


def Hypergeometric(name, N, m, n):
    r"""
    Create a Finite Random Variable representing a hypergeometric distribution.

    Parameters
    ==========

    N : Positive Integer
      Represents finite population of size N.
    m : Positive Integer
      Represents number of trials with required feature.
    n : Positive Integer
      Represents numbers of draws.


    Examples
    ========

    >>> from sympy.stats import Hypergeometric, density

    >>> X = Hypergeometric('X', 10, 5, 3) # 10 marbles, 5 white (success), 3 draws
    >>> density(X).dict
    {0: 1/12, 1: 5/12, 2: 5/12, 3: 1/12}

    Returns
    =======

    RandomSymbol

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Hypergeometric_distribution
    .. [2] http://mathworld.wolfram.com/HypergeometricDistribution.html

    """
    return rv(name, HypergeometricDistribution, N, m, n)


class RademacherDistribution(SingleFiniteDistribution):

    @property
    def set(self):
        return {-1, 1}

    @property
    def pmf(self):
        k = Dummy('k')
        return Lambda(k, Piecewise((S.Half, Or(Eq(k, -1), Eq(k, 1))), (S.Zero, True)))

def Rademacher(name):
    r"""
    Create a Finite Random Variable representing a Rademacher distribution.

    Examples
    ========

    >>> from sympy.stats import Rademacher, density

    >>> X = Rademacher('X')
    >>> density(X).dict
    {-1: 1/2, 1: 1/2}

    Returns
    =======

    RandomSymbol

    See Also
    ========

    sympy.stats.Bernoulli

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Rademacher_distribution

    """
    return rv(name, RademacherDistribution)

class IdealSolitonDistribution(SingleFiniteDistribution):
    _argnames = ('k',)

    @staticmethod
    def check(k):
         _value_check(k.is_integer and k.is_positive,
                    "'k' must be a positive integer.")

    @property
    def low(self):
        return S.One

    @property
    def high(self):
        return self.k

    @property
    def set(self):
        return set(list(Range(1, self.k+1)))

    @property
    @cacheit
    def dict(self):
        if self.k.is_Symbol:
            return Density(self)
        d = {1: Rational(1, self.k)}
        d.update(dict((i, Rational(1, i*(i - 1))) for i in range(2, self.k + 1)))
        return d

    def pmf(self, x):
        x = sympify(x)
        if not (x.is_number or x.is_Symbol or is_random(x)):
            raise ValueError("'x' expected as an argument of type 'number' or 'Symbol' or , "
                        "'RandomSymbol' not %s" % (type(x)))
        cond1 = Eq(x, 1) & x.is_integer
        cond2 = Ge(x, 1) & Le(x, self.k) & x.is_integer
        return Piecewise((1/self.k, cond1), (1/(x*(x - 1)), cond2), (S.Zero, True))

def IdealSoliton(name, k):
    r"""
    Create a Finite Random Variable of Ideal Soliton Distribution

    Parameters
    ==========

    k : Positive Integer
        Represents the number of input symbols in an LT (Luby Transform) code.

    Examples
    ========

    >>> from sympy.stats import IdealSoliton, density, P, E
    >>> sol = IdealSoliton('sol', 5)
    >>> density(sol).dict
    {1: 1/5, 2: 1/2, 3: 1/6, 4: 1/12, 5: 1/20}
    >>> density(sol).set
    {1, 2, 3, 4, 5}

    >>> from sympy import Symbol
    >>> k = Symbol('k', positive=True, integer=True)
    >>> sol = IdealSoliton('sol', k)
    >>> density(sol).dict
    Density(IdealSolitonDistribution(k))
    >>> density(sol).dict.subs(k, 10).doit()
    {1: 1/10, 2: 1/2, 3: 1/6, 4: 1/12, 5: 1/20, 6: 1/30, 7: 1/42, 8: 1/56, 9: 1/72, 10: 1/90}

    >>> E(sol.subs(k, 10))
    7381/2520

    >>> P(sol.subs(k, 4) > 2)
    1/4

    Returns
    =======

    RandomSymbol

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Soliton_distribution#Ideal_distribution
    .. [2] http://pages.cs.wisc.edu/~suman/courses/740/papers/luby02lt.pdf

    """
    return rv(name, IdealSolitonDistribution, k)

class RobustSolitonDistribution(SingleFiniteDistribution):
    _argnames= ('k', 'delta', 'c')

    @staticmethod
    def check(k, delta, c):
        _value_check(k.is_integer and k.is_positive,
                    "'k' must be a positive integer")
        _value_check(Gt(delta, 0) and Le(delta, 1),
                    "'delta' must be a real number in the interval (0,1)")
        _value_check(c.is_positive,
                    "'c' must be a positive real number.")

    @property
    def R(self):
        return self.c * log(self.k/self.delta) * self.k**0.5

    @property
    def Z(self):
        z = 0
        for i in Range(1, round(self.k/self.R)):
            z += (1/i)
        z += log(self.R/self.delta)
        return 1 + z * self.R/self.k

    @property
    def low(self):
        return S.One

    @property
    def high(self):
        return self.k

    @property
    def set(self):
        return set(list(Range(1, self.k+1)))

    @property
    def is_symbolic(self):
        return not all([self.k.is_number, self.c.is_number, self.delta.is_number])

    def pmf(self, x):
        x = sympify(x)
        if not (x.is_number or x.is_Symbol or is_random(x)):
            raise ValueError("'x' expected as an argument of type 'number' or 'Symbol' or , "
                        "'RandomSymbol' not %s" % (type(x)))

        cond1 = Eq(x, 1) & x.is_integer
        cond2 = Ge(x, 1) & Le(x, self.k) & x.is_integer
        rho = Piecewise((Rational(1, self.k), cond1), (Rational(1, x*(x-1)), cond2), (S.Zero, True))

        cond1 = Ge(x, 1) & Le(x, round(self.k/self.R)-1)
        cond2 = Eq(x, round(self.k/self.R))
        tau = Piecewise((self.R/(self.k * x), cond1), (self.R * log(self.R/self.delta)/self.k, cond2), (S.Zero, True))

        return (rho + tau)/self.Z

def RobustSoliton(name, k, delta, c):
    r'''
    Create a Finite Random Variable of Robust Soliton Distribution

    Parameters
    ==========

    k : Positive Integer
        Represents the number of input symbols in an LT (Luby Transform) code.
    delta : Positive Rational Number
            Represents the failure probability. Must be in the interval (0,1).
    c : Positive Rational Number
        Constant of proportionality. Values close to 1 are recommended

    Examples
    ========

    >>> from sympy.stats import RobustSoliton, density, P, E
    >>> robSol = RobustSoliton('robSol', 5, 0.5, 0.01)
    >>> density(robSol).dict
    {1: 0.204253668152708, 2: 0.490631107897393, 3: 0.165210624506162, 4: 0.0834387731899302, 5: 0.0505633404760675}
    >>> density(robSol).set
    {1, 2, 3, 4, 5}

    >>> from sympy import Symbol
    >>> k = Symbol('k', positive=True, integer=True)
    >>> c = Symbol('c', positive=True)
    >>> robSol = RobustSoliton('robSol', k, 0.5, c)
    >>> density(robSol).dict
    Density(RobustSolitonDistribution(k, 0.5, c))
    >>> density(robSol).dict.subs(k, 10).subs(c, 0.03).doit()
    {1: 0.116641095387194, 2: 0.467045731687165, 3: 0.159984123349381, 4: 0.0821431680681869, 5: 0.0505765646770100,
    6: 0.0345781523420719, 7: 0.0253132820710503, 8: 0.0194459129233227, 9: 0.0154831166726115, 10: 0.0126733075238887}

    >>> E(robSol.subs(k, 10).subs(c, 0.05))
    2.91358846104106

    >>> P(robSol.subs(k, 4).subs(c, 0.1) > 2)
    0.243650614389834

    Returns
    =======

    RandomSymbol

    References
    ==========

    .. [1] https://en.wikipedia.org/wiki/Soliton_distribution#Robust_distribution
    .. [2] http://www.inference.org.uk/mackay/itprnn/ps/588.596.pdf
    .. [3] http://pages.cs.wisc.edu/~suman/courses/740/papers/luby02lt.pdf

    '''
    return rv(name, RobustSolitonDistribution, k, delta, c)
