def _objective(self, X, Y):
        constraints = Parallel(
            n_jobs=self.n_jobs,
            verbose=self.verbose - 1)(delayed(find_constraint_latent)(
                self.model, x, y, self.w)
                for x, y in zip(X, Y))
        slacks = list(zip(*constraints))[2]
        slacks = np.maximum(slacks, 0)

        objective = np.sum(slacks) * self.C + np.sum(self.w ** 2) / 2.
        return objective
dataset/ETHPy150Open pystruct/pystruct/pystruct/learners/subgradient_latent_ssvm.py/SubgradientLatentSSVM._objective/original