

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