

def predictive_log_likelihood(self, X_test):
    assert (X_test.shape == (self.N, self.K))
    ll = 0
    ll += (gammaln((X_test.sum(axis=1) + 1)).sum() - gammaln((X_test + 1)).sum())
    ll += np.nansum((ll * np.log(self.pi)))
    return ll

dataset/ETHPy150Open HIPS/pgmult/pgmult/distributions.py IndependentMultinomialsModel.predictive_log_likelihood/VarMisuse@77/92 `X_test`->`ll`