

def conditional_prec(self, augmented_data, flat=False):
    '\n        Compute the conditional mean \\psi given \\omega\n        :param augmented_data:\n        :return:\n        '
    O = augmented_data['omega']
    T = augmented_data['T']
    Km1 = (self.K - 1)
    if flat:
        prec = O
    else:
        prec = np.zeros((T, Km1, Km1))
        for t in xrange(T):
            O[t, :, :] = np.diag(O[t, :])
    return prec

dataset/ETHPy150Open HIPS/pgmult/pgmult/distributions.py PGMultinomialRegression.conditional_prec/VarMisuse@81/137 `prec`->`O`