treetime/gtr.py
def sequence_logLH(self,seq, pattern_multiplicity=None):
        """
        Returns the log-likelihood of sampling a sequence from equilibrium frequency.
        Expects a sequence as numpy array

        Parameters
        ----------

         seq : numpy array
            Compressed sequence as an array of chars

         pattern_multiplicity : numpy_array
            The number of times each position in sequence is observed in the
            initial alignment. If None, sequence is assumed to be not compressed

        """
        if pattern_multiplicity is None:
            pattern_multiplicity = np.ones_like(seq, dtype=float)
        return np.sum([np.sum((seq==state)*pattern_multiplicity*np.log(self.Pi[si]))
                      for si,state in enumerate(self.alphabet)])
def sequence_logLH(self,seq, pattern_multiplicity=None):
        if pattern_multiplicity is None:
            pattern_multiplicity = np.ones_like(seq, dtype=float)
        return np.sum([np.sum((seq==state)*pattern_multiplicity*np.log(self.Pi[si]))
                      for si,state in enumerate(self.alphabet)])
def sequence_logLH ( self , seq , pattern_multiplicity = None ) : if pattern_multiplicity is None : pattern_multiplicity = np . ones_like ( seq , dtype = float ) return np . sum ( [ np . sum ( ( seq == state ) * pattern_multiplicity * np . log ( self . Pi [ si ] ) ) for si , state in enumerate ( self . alphabet ) ] )
Returns the log-likelihood of sampling a sequence from equilibrium frequency.
        Expects a sequence as numpy array

        Parameters
        ----------

         seq : numpy array
            Compressed sequence as an array of chars

         pattern_multiplicity : numpy_array
            The number of times each position in sequence is observed in the
            initial alignment. If None, sequence is assumed to be not compressed
Returns the log - likelihood of sampling a sequence from equilibrium frequency . Expects a sequence as numpy array