lik_layers.py 文件源码

def compute_log_lik_exp(self, m, v, y):
        if m.ndim == 2:
            gh_x, gh_w = self._gh_points(GH_DEGREE)
            gh_x = gh_x[:, np.newaxis, np.newaxis]
            gh_w = gh_w[:, np.newaxis, np.newaxis] / np.sqrt(np.pi)
            v_expand = v[np.newaxis, :, :]
            m_expand = m[np.newaxis, :, :]
            ts = gh_x * np.sqrt(2 * v_expand) + m_expand
            logcdfs = norm.logcdf(ts * y)
            prods = gh_w * logcdfs
            loglik = np.sum(prods)

            pdfs = norm.pdf(ts * y)
            cdfs = norm.cdf(ts * y)
            grad_cdfs = y * gh_w * pdfs / cdfs
            dts_dm = 1
            dts_dv = 0.5 * gh_x * np.sqrt(2 / v_expand)
            dm = np.sum(grad_cdfs * dts_dm, axis=0)
            dv = np.sum(grad_cdfs * dts_dv, axis=0)
        else:
            gh_x, gh_w = self._gh_points(GH_DEGREE)
            gh_x = gh_x[:, np.newaxis, np.newaxis, np.newaxis]
            gh_w = gh_w[:, np.newaxis, np.newaxis, np.newaxis] / np.sqrt(np.pi)
            v_expand = v[np.newaxis, :, :, :]
            m_expand = m[np.newaxis, :, :, :]
            ts = gh_x * np.sqrt(2 * v_expand) + m_expand
            logcdfs = norm.logcdf(ts * y)
            prods = gh_w * logcdfs
            prods_mean = np.mean(prods, axis=1)
            loglik = np.sum(prods_mean)

            pdfs = norm.pdf(ts * y)
            cdfs = norm.cdf(ts * y)
            grad_cdfs = y * gh_w * pdfs / cdfs
            dts_dm = 1
            dts_dv = 0.5 * gh_x * np.sqrt(2 / v_expand)
            dm = np.sum(grad_cdfs * dts_dm, axis=0) / m.shape[0]
            dv = np.sum(grad_cdfs * dts_dv, axis=0) / m.shape[0]

        return loglik, dm, dv