def get_sparsity_in(self, i): n = nlpsol_out(i) if n == "f": return Sparsity.scalar() elif n in ("x", "lam_x"): return Sparsity.dense(self.nx) elif n in ("g", "lam_g"): return Sparsity.dense(self.ng) else: return Sparsity(0, 0)
def get_sparsity_in(self, i): n = ca.nlpsol_out(i) if n == 'f': return ca.Sparsity.scalar() elif n in ('x', 'lam_x'): return ca.Sparsity.dense(self.nx) elif n in ('g', 'lam_g'): return ca.Sparsity.dense(self.ng) else: return ca.Sparsity(0, 0)
def get_name_in(i: int) -> int: """ Get the name of a variable Parameters ---------- i: int The index of the variable Returns ------- The name of the variable """ return nlpsol_out(i)
def eval(self, arg): darg = {} for (i, s) in enumerate(ca.nlpsol_out()): darg[s] = arg[i] solution = np.array( (darg['x'][:len(self.param_scale)] * self.param_scale).T) loss = darg['f'] logger.record_tabular('loss', loss) logger.record_tabular('solution', solution) self.algo.solution = solution params, torch_params = self.algo.get_itr_snapshot(self.itr) logger.save_itr_params(self.itr, params, torch_params) logger.dump_tabular(with_prefix=False) self.itr += 1 return [0]
def get_sparsity_in(self, i: int) -> tuple: """ Get the sparsity of a specific variable Parameters ---------- i: int The index of the variable Returns ------- The sparsity of the variable """ n = nlpsol_out(i) if n == "f": return Sparsity.scalar() elif n in ("x", "lam_x"): return Sparsity.dense(self.nx) elif n in ("g", "lam_g"): return Sparsity.dense(self.ng) else: return Sparsity(0, 0)
def get_name_in(i): return nlpsol_out(i)
def get_name_in(self, i): return ca.nlpsol_out(i)