from . import densities
import tensorflow as tf
import numpy as np
from .param import Parameterized


class Prior(Parameterized):
    def logp(self, x):
        """
        The log density of the prior as x

        All priors (for the moment) are univariate, so if x is a vector or an
        array, this is the sum of the log densities.
        """
        raise NotImplementedError

    def __str__(self):
        """
        A short string to describe the prior at print time
        """
        raise NotImplementedError


class Gaussian(Prior):
    def __init__(self, mu, var):
        Prior.__init__(self)
        self.mu = np.atleast_1d(np.array(mu, np.float64))
        self.var = np.atleast_1d(np.array(var, np.float64))

    def logp(self, x):
        return tf.reduce_sum(densities.gaussian(x, self.mu, self.var))

    def __str__(self):
        return "N("+str(self.mu) + "," + str(self.var) + ")"


class Gamma(Prior):
    def __init__(self, shape, scale):
        Prior.__init__(self)
        self.shape = np.atleast_1d(np.array(shape, np.float64))
        self.scale = np.atleast_1d(np.array(scale, np.float64))

    def logp(self, x):
        return tf.reduce_sum(densities.gamma(self.shape, self.scale, x))

    def __str__(self):
        return "Ga("+str(self.shape) + "," + str(self.scale) + ")"


class Laplace(Prior):
    def __init__(self, mu, sigma):
        Prior.__init__(self)
        self.mu = np.atleast_1d(np.array(mu, np.float64))
        self.sigma = np.atleast_1d(np.array(sigma, np.float64))

    def logp(self, x):
        return tf.reduce_sum(densities.laplace(self.mu, self.sigma, x))

    def __str__(self):
        return "Lap.("+str(self.mu) + "," + str(self.sigma) + ")"