

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, self)))
    slacks = list(zip(*constraints))[2]
    slacks = np.maximum(slacks, 0)
    objective = ((np.sum(slacks) * self.C) + (np.sum((self.w ** 2)) / 2.0))
    return objective

dataset/ETHPy150Open pystruct/pystruct/pystruct/learners/subgradient_latent_ssvm.py SubgradientLatentSSVM._objective/VarMisuse@64/74 `Y`->`self`