from __future__ import division
# Copyright (c) 2012, 2013 Ricardo Andrade
# Licensed under the BSD 3-clause license (see LICENSE.txt)

import numpy as np
from scipy import stats,special
import scipy as sp
from GPy.util.univariate_Gaussian import std_norm_pdf,std_norm_cdf
import gp_transformations
from likelihood import Likelihood

class Poisson(Likelihood):
    """
    Poisson likelihood

    .. math::
        p(y_{i}|\\lambda(f_{i})) = \\frac{\\lambda(f_{i})^{y_{i}}}{y_{i}!}e^{-\\lambda(f_{i})}

    .. Note::
        Y is expected to take values in {0,1,2,...}
    """
    def __init__(self,gp_link=None,analytical_mean=False,analytical_variance=False):
        super(Poisson, self).__init__(gp_link,analytical_mean,analytical_variance)

    def _preprocess_values(self,Y):
        return Y

    def pdf_link(self, link_f, y, extra_data=None):
        """
        Likelihood function given link(f)

        .. math::
            p(y_{i}|\\lambda(f_{i})) = \\frac{\\lambda(f_{i})^{y_{i}}}{y_{i}!}e^{-\\lambda(f_{i})}

        :param link_f: latent variables link(f)
        :type link_f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param extra_data: extra_data which is not used in poisson distribution
        :returns: likelihood evaluated for this point
        :rtype: float
        """
        assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
        return np.prod(stats.poisson.pmf(y,link_f))

    def logpdf_link(self, link_f, y, extra_data=None):
        """
        Log Likelihood Function given link(f)

        .. math::
            \\ln p(y_{i}|\lambda(f_{i})) = -\\lambda(f_{i}) + y_{i}\\log \\lambda(f_{i}) - \\log y_{i}!

        :param link_f: latent variables (link(f))
        :type link_f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param extra_data: extra_data which is not used in poisson distribution
        :returns: likelihood evaluated for this point
        :rtype: float

        """
        assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
        return np.sum(-link_f + y*np.log(link_f) - special.gammaln(y+1))

    def dlogpdf_dlink(self, link_f, y, extra_data=None):
        """
        Gradient of the log likelihood function at y, given link(f) w.r.t link(f)

        .. math::
            \\frac{d \\ln p(y_{i}|\lambda(f_{i}))}{d\\lambda(f)} = \\frac{y_{i}}{\\lambda(f_{i})} - 1

        :param link_f: latent variables (f)
        :type link_f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param extra_data: extra_data which is not used in poisson distribution
        :returns: gradient of likelihood evaluated at points
        :rtype: Nx1 array

        """
        assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
        return y/link_f - 1

    def d2logpdf_dlink2(self, link_f, y, extra_data=None):
        """
        Hessian at y, given link(f), w.r.t link(f)
        i.e. second derivative logpdf at y given link(f_i) and link(f_j)  w.r.t link(f_i) and link(f_j)
        The hessian will be 0 unless i == j

        .. math::
            \\frac{d^{2} \\ln p(y_{i}|\lambda(f_{i}))}{d^{2}\\lambda(f)} = \\frac{-y_{i}}{\\lambda(f_{i})^{2}}

        :param link_f: latent variables link(f)
        :type link_f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param extra_data: extra_data which is not used in poisson distribution
        :returns: Diagonal of hessian matrix (second derivative of likelihood evaluated at points f)
        :rtype: Nx1 array

        .. Note::
            Will return diagonal of hessian, since every where else it is 0, as the likelihood factorizes over cases
            (the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
        """
        assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
        hess = -y/(link_f**2)
        return hess
        #d2_df = self.gp_link.d2transf_df2(gp)
        #transf = self.gp_link.transf(gp)
        #return obs * ((self.gp_link.dtransf_df(gp)/transf)**2 - d2_df/transf) + d2_df

    def d3logpdf_dlink3(self, link_f, y, extra_data=None):
        """
        Third order derivative log-likelihood function at y given link(f) w.r.t link(f)

        .. math::
            \\frac{d^{3} \\ln p(y_{i}|\lambda(f_{i}))}{d^{3}\\lambda(f)} = \\frac{2y_{i}}{\\lambda(f_{i})^{3}}

        :param link_f: latent variables link(f)
        :type link_f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param extra_data: extra_data which is not used in poisson distribution
        :returns: third derivative of likelihood evaluated at points f
        :rtype: Nx1 array
        """
        assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
        d3lik_dlink3 = 2*y/(link_f)**3
        return d3lik_dlink3

    def _mean(self,gp):
        """
        Mass (or density) function
        """
        return self.gp_link.transf(gp)

    def _variance(self,gp):
        """
        Mass (or density) function
        """
        return self.gp_link.transf(gp)

    def samples(self, gp):
        """
        Returns a set of samples of observations based on a given value of the latent variable.

        :param gp: latent variable
        """
        orig_shape = gp.shape
        gp = gp.flatten()
        Ysim = np.random.poisson(self.gp_link.transf(gp))
        return Ysim.reshape(orig_shape)