def _setup(self):
options = self.options
nx = self.training_points[None][0][0].shape[1]
for name in ['smoothness', 'num_elements']:
if isinstance(options[name], (int, float)):
options[name] = [options[name]] * nx
options[name] = np.atleast_1d(options[name])
self.printer.max_print_depth = options['max_print_depth']
num = {}
# number of inputs and outputs
num['x'] = self.training_points[None][0][0].shape[1]
num['y'] = self.training_points[None][0][1].shape[1]
# number of elements
num['elem_list'] = np.array(options['num_elements'], int)
num['elem'] = np.prod(num['elem_list'])
# number of terms/coefficients per element
num['term_list'] = 4 * np.ones(num['x'], int)
num['term'] = np.prod(num['term_list'])
# number of nodes
num['uniq_list'] = num['elem_list'] + 1
num['uniq'] = np.prod(num['uniq_list'])
# total number of training points (function values and derivatives)
num['t'] = 0
for kx in self.training_points[None]:
num['t'] += self.training_points[None][kx][0].shape[0]
# for RMT
num['coeff'] = num['term'] * num['elem']
num['support'] = num['term']
num['dof'] = num['uniq'] * 2**num['x']
self.num = num
self.rmtsc = PyRMTC()
self.rmtsc.setup(
num['x'],
np.array(self.options['xlimits'][:, 0]),
np.array(self.options['xlimits'][:, 1]),
np.array(num['elem_list'], np.int32),
np.array(num['term_list'], np.int32),
)
def _setup(self):
options = self.options
nx = self.training_points[None][0][0].shape[1]
for name in ["smoothness", "num_elements"]:
if isinstance(options[name], (int, float)):
options[name] = [options[name]] * nx
options[name] = np.atleast_1d(options[name])
self.printer.max_print_depth = options["max_print_depth"]
num = {}
# number of inputs and outputs
num["x"] = self.training_points[None][0][0].shape[1]
num["y"] = self.training_points[None][0][1].shape[1]
# number of elements
num["elem_list"] = np.array(options["num_elements"], int)
num["elem"] = np.prod(num["elem_list"])
# number of terms/coefficients per element
num["term_list"] = 4 * np.ones(num["x"], int)
num["term"] = np.prod(num["term_list"])
# number of nodes
num["uniq_list"] = num["elem_list"] + 1
num["uniq"] = np.prod(num["uniq_list"])
# total number of training points (function values and derivatives)
num["t"] = 0
for kx in self.training_points[None]:
num["t"] += self.training_points[None][kx][0].shape[0]
# for RMT
num["coeff"] = num["term"] * num["elem"]
num["support"] = num["term"]
num["dof"] = num["uniq"] * 2**num["x"]
self.num = num
self.rmtsc = PyRMTC()
self.rmtsc.setup(
num["x"],
np.array(self.options["xlimits"][:, 0]),
np.array(self.options["xlimits"][:, 1]),
np.array(num["elem_list"], np.int32),
np.array(num["term_list"], np.int32),
)