def _train(self):
"""
Train the model
"""
self._setup()
tmp = self.rmtsc
self.rmtsc = None
inputs = {"self": self}
with cached_operation(inputs, self.options["data_dir"]) as outputs:
self.rmtsc = tmp
if outputs:
self.sol_coeff = outputs["sol_coeff"]
self.sol = outputs["sol"]
self.mtx = outputs["mtx"]
self.full_dof2coeff = outputs["full_dof2coeff"]
self.full_hess = outputs["full_hess"]
self.full_jac_dict = outputs["full_jac_dict"]
else:
self._new_train()
outputs["sol_coeff"] = self.sol_coeff
outputs["sol"] = self.sol
outputs["mtx"] = self.mtx
outputs["full_dof2coeff"] = self.full_dof2coeff
outputs["full_hess"] = self.full_hess
outputs["full_jac_dict"] = self.full_jac_dict
def _train(self):
"""
Train the model
"""
self._setup()
tmp = self.rmtsc
self.rmtsc = None
inputs = {'self': self}
with cached_operation(inputs, self.options['data_dir']) as outputs:
self.rmtsc = tmp
if outputs:
self.sol_coeff = outputs['sol_coeff']
self.sol = outputs['sol']
self.mtx = outputs['mtx']
self.full_dof2coeff = outputs['full_dof2coeff']
self.full_hess = outputs['full_hess']
self.full_jac_dict = outputs['full_jac_dict']
else:
self._new_train()
outputs['sol_coeff'] = self.sol_coeff
outputs['sol'] = self.sol
outputs['mtx'] = self.mtx
outputs['full_dof2coeff'] = self.full_dof2coeff
outputs['full_hess'] = self.full_hess
outputs['full_jac_dict'] = self.full_jac_dict
def _train(self):
"""
Train the model
"""
inputs = {"self": self}
with cached_operation(inputs, self.options["data_dir"]) as outputs:
if outputs:
self.sol = outputs["sol"]
else:
self._new_train()
def _train(self):
"""
Train the model
"""
inputs = {'self': self}
with cached_operation(inputs, self.options['data_dir']) as outputs:
if outputs:
self.sol = outputs['sol']
else:
self._new_train()
def _compute_energy_terms(self):
# This computes the energy terms that are to be minimized.
# The quadrature points are the centroids of the multi-dimensional elements.
num = self.num
xlimits = self.options["xlimits"]
inputs = {}
inputs["nx"] = xlimits.shape[0]
inputs["elem_list"] = num["elem_list"]
if self.__class__.__name__ == "RMTB":
inputs["num_ctrl_list"] = num["ctrl_list"]
inputs["order_list"] = num["order_list"]
if self.options["save_energy_terms"]:
cache_dir = self.options["data_dir"]
else:
cache_dir = None
with cached_operation(inputs, cache_dir) as outputs:
if outputs:
sq_mtx = outputs["sq_mtx"]
else:
n = np.prod(2 * num["elem_list"])
x = np.empty(n * num["x"])
self.rmtsc.compute_quadrature_points(
n, np.array(2 * num["elem_list"], dtype=np.int32), x
)
x = x.reshape((n, num["x"]))
sq_mtx = [None] * num["x"]
for kx in range(num["x"]):
mtx = self._compute_jac(kx + 1, kx + 1, x)
sq_mtx[kx] = (
mtx.T.tocsc() * mtx * (xlimits[kx, 1] - xlimits[kx, 0]) ** 4
)
outputs["sq_mtx"] = sq_mtx
elem_vol = np.prod((xlimits[:, 1] - xlimits[:, 0]) / (2 * num["elem_list"]))
total_vol = np.prod(xlimits[:, 1] - xlimits[:, 0])
full_hess = scipy.sparse.csc_matrix((num["dof"], num["dof"]))
for kx in range(num["x"]):
full_hess += sq_mtx[kx] * (
elem_vol
/ total_vol
* self.options["smoothness"][kx]
/ (xlimits[kx, 1] - xlimits[kx, 0]) ** 4
)
return full_hess
def _train(self):
"""
Train the model
"""
self._setup()
tmp = self.idwc
self.idwc = None
inputs = {'self': self}
with cached_operation(inputs, self.options['data_dir']) as outputs:
self.idwc = tmp
if outputs:
self.sol = outputs['sol']
else:
self._new_train()
def _train(self):
"""
Train the model
"""
self._setup()
tmp = self.rbfc
self.rbfc = None
inputs = {"self": self}
with cached_operation(inputs, self.options["data_dir"]) as outputs:
self.rbfc = tmp
if outputs:
self.sol = outputs["sol"]
else:
self._new_train()
outputs["sol"] = self.sol
