def uncollapsed_likelihood(self, X, parameters):
        """
        Calculates the score of the data X under this component model with mean
        mu and precision kappa.
        Inputs:
            X: A column of data (numpy)
            parameters: a dict with the following keys
                mu: the Von Mises mean
                kappa: the precision of the Von Mises
        """
        check_data_type_column_data(X)
        check_model_params_dict(parameters)

        mu = parameters['mu']
        kappa = parameters['kappa']

        N = float(len(X))

        hypers = self.get_hypers()
        a = hypers['a']
        b = hypers['b']
        kappa = hypers['kappa']

        sum_err = numpy.sum((mu%X)**2.0)

        log_likelihood = self.log_likelihood(X, {'mu':mu, 'kappa':rho})
        log_prior_mu = vonmises.logpdf(b, a)

        log_p = log_likelihood + log_prior_mu + log_prior_rho

        return log_p
dataset/ETHPy150Open probcomp/crosscat/src/tests/component_model_extensions/CyclicComponentModel.py/p_CyclicComponentModel.uncollapsed_likelihood/operator `-` -> `%`