aima/probability.py
def normalize(self):
        """Make sure the probabilities of all values sum to 1.
        Returns the normalized distribution.
        Raises a ZeroDivisionError if the sum of the values is 0.
        >>> P = ProbDist('Flip'); P['H'], P['T'] = 35, 65
        >>> P = P.normalize()
        >>> print '%5.3f %5.3f' % (P.prob['H'], P.prob['T'])
        0.350 0.650
        """
        total = float(sum(self.prob.values()))
        if not (1.0-epsilon < total < 1.0+epsilon):
            for val in self.prob:
                self.prob[val] /= total
        return self
def normalize(self):
        """Make sure the probabilities of all values sum to 1.
        Returns the normalized distribution.
        Raises a ZeroDivisionError if the sum of the values is 0.
        >>> P = ProbDist('Flip'); P['H'], P['T'] = 35, 65
        >>> P = P.normalize()
        >>> print '%5.3f %5.3f' % (P.prob['H'], P.prob['T'])
        0.350 0.650
        """
        total = float(sum(self.prob.values()))
        if not (1.0-epsilon < total < 1.0+epsilon):
            for val in self.prob:
                self.prob[val] /= total
        return self
def normalize ( self ) : total = float ( sum ( self . prob . values ( ) ) ) if not ( 1.0 - epsilon < total < 1.0 + epsilon ) : for val in self . prob : self . prob [ val ] /= total return self
Make sure the probabilities of all values sum to 1.
        Returns the normalized distribution.
        Raises a ZeroDivisionError if the sum of the values is 0.
        >>> P = ProbDist('Flip'); P['H'], P['T'] = 35, 65
        >>> P = P.normalize()
        >>> print '%5.3f %5.3f' % (P.prob['H'], P.prob['T'])
        0.350 0.650
Make sure the probabilities of all values sum to 1 . Returns the normalized distribution . Raises a ZeroDivisionError if the sum of the values is 0 . >>> P = ProbDist ( Flip ) ; P [ H ] P [ T ] = 35 65 >>> P = P . normalize () >>> print %5 . 3f %5 . 3f % ( P . prob [ H ] P . prob [ T ] ) 0 . 350 0 . 650