bpmf.py 文件源码

def _update_item_params(self):
        N = self.n_item
        X_bar = np.mean(self.item_features_, 0).reshape((self.n_feature, 1))
        # print 'X_bar', X_bar.shape
        S_bar = np.cov(self.item_features_.T)
        # print 'S_bar', S_bar.shape

        diff_X_bar = self.mu0_item - X_bar

        # W_{0}_star
        WI_post = inv(inv(self.WI_item) +
                      N * S_bar +
                      np.dot(diff_X_bar, diff_X_bar.T) *
                      (N * self.beta_item) / (self.beta_item + N))

        # Note: WI_post and WI_post.T should be the same.
        #       Just make sure it is symmertic here
        WI_post = (WI_post + WI_post.T) / 2.0

        # update alpha_item
        df_post = self.df_item + N
        self.alpha_item = wishart.rvs(df_post, WI_post, 1, self.rand_state)

        # update mu_item
        mu_mean = (self.beta_item * self.mu0_item + N * X_bar) / \
            (self.beta_item + N)
        mu_var = cholesky(inv(np.dot(self.beta_item + N, self.alpha_item)))
        # print 'lam', lam.shape
        self.mu_item = mu_mean + np.dot(
            mu_var, self.rand_state.randn(self.n_feature, 1))
        # print 'mu_item', self.mu_item.shape