from __future__ import print_function, division
import thinkbayes2


class Coin(thinkbayes2.Suite):
    
    def Likelihood(self, data, hypo):
        x = hypo / 100
        if data == 'H':
            return x
        else:
            return 1-x

suite = Coin(range(0, 101))

import thinkplot
thinkplot.Pdf(suite)

posterior = suite.Copy()
posterior.Update('H')

thinkplot.Pdf(posterior)

results = 'HTHHTHHHTTHHHTH'
for data in results:
    posterior.Update(data)
    
thinkplot.Pdf(posterior)

class Pmf(object):

    def Normalize(self, fraction=1.0):
        """Normalizes this PMF so the sum of all probs is fraction.

        Args:
            fraction: what the total should be after normalization

        Returns: the total probability before normalizing
        """
        if self.log:
            raise ValueError("Normalize: Pmf is under a log transform")

        total = self.Total()
        if total == 0.0:
            raise ValueError('Normalize: total probability is zero.')
            #logging.warning('Normalize: total probability is zero.')
            #return total

        factor = fraction / total
        for x in self.d:
            self.d[x] *= factor

        return total

class Suite(Pmf):
    """Represents a suite of hypotheses and their probabilities."""

    def Update(self, data):
        """Updates each hypothesis based on the data.

        data: any representation of the data

        returns: the normalizing constant
        """
        for hypo in self.Values():
            like = self.Likelihood(data, hypo)
            self.Mult(hypo, like)
        return self.Normalize()