code: def _invert_dummies(self, dummies):
          '''
          For the sake of computational efficiency (i.e., to avoid lots of
          large matrix multiplications in the backends), invert the dummy-coding
          process and represent full-rank dummies as a vector of indices into the
          coefficients.
          '''
          vec = np.zeros(len(dummies), dtype=int)
          for i in range(1, dummies.shape[1]):
              vec[dummies[:, i] == 1] = i
          return vef _invert_dummies(self, dummies):
          '''
          For the sake of computational efficiency (i.e., to avoid lots of
          large matrix multiplications in the backends), invert the dummy-coding
          process and represent full-rank dummies as a vector of indices into the
          coefficients.
          '''
          vec = np.zeros(len(dummies), dtype=int)
          for i in range(1, dummies.shape[1]):
              vec[dummies[:, i] == 1] = i
          return vec
docs: For the sake of computational efficiency (i.e., to avoid lots of
          large matrix multiplications in the backends), invert the dummy-coding
          process and represent full-rank dummies as a vector of indices into the
          coefficients.