vector_student_t_test.py 文件源码

def log_prob(self, x):
    def _compute(df, loc, scale_tril, x):
      k = scale_tril.shape[-1]
      ildj = np.sum(np.log(np.abs(np.diag(scale_tril))), axis=-1)
      logz = ildj + k * (0.5 * np.log(df) +
                         0.5 * np.log(np.pi) +
                         special.gammaln(0.5 * df) -
                         special.gammaln(0.5 * (df + 1.)))
      y = linalg.solve_triangular(scale_tril, np.matrix(x - loc).T,
                                  lower=True, overwrite_b=True)
      logs = -0.5 * (df + 1.) * np.sum(np.log1p(y**2. / df), axis=-2)
      return logs - logz
    if not self._df.shape:
      return _compute(self._df, self._loc, self._scale_tril, x)
    return np.concatenate([
        [_compute(self._df[i], self._loc[i], self._scale_tril[i], x[:, i, :])]
        for i in range(len(self._df))]).T