def log_likelihood(self, data):
        x = data["x"]
        pi = self.pi(data)
        pi = np.clip(pi, 1e-16, 1-1e-16)

        # Compute the multinomial log likelihood given psi
        assert x.shape == pi.shape
        ll = 0
        ll += gammaln(x.sum(axis=1) + 1).sum() - gammaln(x+1).sum()
        ll += (x * np.log(pi)).sum()
        return ll
dataset/ETHPy150Open HIPS/pgmult/pgmult/distributions.py/PGMultinomialRegression.log_likelihood/original