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)
