ensemble_hic/error_models.py
def _evaluate_log_prob(self, mock_data):
        """
        Evaluates the log-probability of the data given the mock data
        
        :param mock_data: back-calculated count / frequency data
        :type mock_data: :class:`numpy.ndarray`

        :returns: log-probablity of the data
        :rtype: float
        """
        d_counts = self.data

        return -mock_data.sum() + numpy.sum(d_counts * numpy.log(mock_data))

        Evaluates the log-probability of the data given the mock data
        
        :param mock_data: back-calculated count / frequency data
        :type mock_data: :class:`numpy.ndarray`

        :returns: log-probablity of the data
        :rtype: float
        
Evaluates the log-probability of the data given the mock data
Evaluates the log - probability of the data given the mock data
def _evaluate_log_prob(self, mock_data):
        d_counts = self.data
        return -mock_data.sum() + numpy.sum(d_counts * numpy.log(mock_data))
def _evaluate_log_prob ( self , mock_data ) : d_counts = self . data return - mock_data . sum ( ) + numpy . sum ( d_counts * numpy . log ( mock_data ) )
Evaluates the log-probability of the data given the mock data
Evaluates the log - probability of the data given the mock data
"""
        Evaluates the log-probability of the data given the mock data
        
        :param mock_data: back-calculated count / frequency data
        :type mock_data: :class:`numpy.ndarray`

        :returns: log-probablity of the data
        :rtype: float
        """
log-probablity of the data
log - probablity of the data
float
self
mock_data
back-calculated count / frequency data
back - calculated count / frequency data