87bb296cf1614091658aad5d875eebfb07e8d47a,scipy/optimize/_trustregion_constr/canonical_constraint.py,CanonicalConstraint,from_PreparedConstraint,#Any#Any#,51

Before Change

        if np.all(lb == -np.inf) and np.all(ub == np.inf):
            return cls.empty(cfun.n)

        n_bounds = lb.shape[0]
        if np.all(lb == ub):
            fun, jac, hess = cls._equal_to_canonical(cfun, lb)
            n_eq = n_bounds
            n_ineq = 0
            keep_feasible = np.empty(0, dtype=bool)
        elif np.all(lb == -np.inf):
            fun, jac, hess, keep_feasible, n_ineq \
                = cls._less_to_canonical(cfun, ub, keep_feasible)
            n_eq = 0
        elif np.all(ub == np.inf):
            fun, jac, hess, keep_feasible, n_ineq \
                = cls._greater_to_canonical(cfun, lb, keep_feasible)
            n_eq = 0
        else:
            (fun, jac, hess, keep_feasible, n_eq, n_ineq) \
                = cls._interval_to_canonical(cfun, lb, ub, keep_feasible)

        return cls(n_eq, n_ineq, fun, jac, hess, keep_feasible)

    @classmethod
    def empty(cls, n):
        Create an "empty" instance.

After Change

        if np.all(lb == -np.inf) and np.all(ub == np.inf):
            return cls.empty(cfun.n)

        if np.all(lb == -np.inf) and np.all(ub == np.inf):
            return cls.empty(cfun.n)
        elif np.all(lb == ub):
            return cls._equal_to_canonical(cfun, lb)
        elif np.all(lb == -np.inf):
            return cls._less_to_canonical(cfun, ub, keep_feasible)
        elif np.all(ub == np.inf):
            return cls._greater_to_canonical(cfun, lb, keep_feasible)
        else:
            return cls._interval_to_canonical(cfun, lb, ub, keep_feasible)

    @classmethod
    def empty(cls, n):
        Create an "empty" instance.

Instances