from math import log
import numpy as np


class Metrics:
    @staticmethod
    def conditional_entropy(x, y, base=2):
        """quantifies the amount of information needed to describe the outcome
        of Y given that the value of X is known
        computes H(Y|X)

        Parameters
        ----------
        x : np.array
            values of the variable
        y : np.array
            array of labels
        base : int, optional
            base of the logarithm, by default 2

        Returns
        -------
        float
            conditional entropy of y given x
        """
        xy = np.c_[x, y]
        return Metrics.entropy(xy, base) - Metrics.entropy(x, base)

    @staticmethod
    def entropy(y, base=2):
        """measure of the uncertainty in predicting the value of y

        Parameters
        ----------
        y : np.array
            array of labels
        base : int, optional
            base of the logarithm, by default 2

        Returns
        -------
        float
            entropy of y
        """
        _, count = np.unique(y, return_counts=True, axis=0)
        proba = count.astype(float) / len(y)
        proba = proba[proba > 0.0]
        return np.sum(proba * np.log(1.0 / proba)) / log(base)

    @staticmethod
    def information_gain(x, y, base=2):
        """Measures the reduction in uncertainty about the value of y when the
        value of X is known (also called mutual information)
        (https://www.sciencedirect.com/science/article/pii/S0020025519303603)

        Parameters
        ----------
        x : np.array
            values of the variable
        y : np.array
            array of labels
        base : int, optional
            base of the logarithm, by default 2

        Returns
        -------
        float
            Information gained
        """
        return Metrics.entropy(y, base) - Metrics.conditional_entropy(
            x, y, base
        )

    @staticmethod
    def symmetrical_uncertainty(x, y):

        return (
            2.0
            * Metrics.information_gain(x, y)
            / (Metrics.entropy(x) + Metrics.entropy(y))
        )


class CFS:
    def __init__(self, a):
        self.a = a