BPTF.py 文件源码

def _update_parameters(self, factors0, factors1, ratings, factors_mu, factors_variance):
        """
        :param factors0:
        :param factors1:
        :param ratings:
        :param factors_mu:
        :param factors_variance:
        :return:
        """
        index = ratings.keys()

        QQ = 0
        RQ = 0
        for dim0, dim1 in index:
            Q = factors0[dim0, :] * factors1[dim1, :]
            QQ += np.mat(Q).transpose() * np.mat(Q)
            RQ += (ratings[dim0, dim1] - self.mean_rating) * Q
        sigma_inv = np.linalg.inv(factors_variance + self.rating_sigma * QQ)
        mu = sigma_inv * (np.dot(factors_variance, np.reshape(factors_mu, newshape=(factors_mu.shape[0], 1))) + self.rating_sigma * RQ)
        return np.random.multivariate_normal(mu, sigma_inv)