def set_model(self, model=None, model_grads=None):
"""
Computes the coefficients of the polynomial.
:param Correlations self:
An instance of the Correlations class.
:param callable model:
The function that needs to be approximated. In the absence of a callable function, the input can be the function evaluated at the quadrature points.
:param callable model_grads:
The gradient of the function that needs to be approximated. In the absence of a callable gradient function, the input can be a matrix of gradient evaluations at the quadrature points.
"""
# Need to account for the nataf transform here?
model_values = None
model_grads_values = None
if callable(model):
model_values = evaluate_model(self._points, model)
else:
model_values = model
if model_grads is not None:
if callable(model_grads):
model_grads_values = evaluate_model_gradients(
self._points, model_grads)
else:
model_grads_values = model_grads
self.corrected_poly.set_model(model_values, model_grads_values)
def _iterative_quadrature_computation(self,
integrand,
quadrature_order_output=True):
# Keep increasing the order till we reach ORDER_LIMIT
quadrature_error = 500.0
quadrature_order = 0
integral_before = 10.0
while quadrature_error >= 1e-6:
quadrature_order += QUADRATURE_ORDER_INCREMENT
pts, wts = self._get_quadrature_points_and_weights(
quadrature_order)
integral = float(np.dot(wts, evaluate_model(pts, integrand)))
quadrature_error = np.abs(integral - integral_before)
integral_before = integral
if quadrature_order >= ORDER_LIMIT:
raise (RuntimeError, 'Even with ' + str(ORDER_LIMIT + 1) +
' points, an error in the mean of ' + str(1e-4) +
'cannot be obtained.')
if quadrature_order_output is True:
return integral, quadrature_order
else:
return integral
def set_model(self, model=None, model_grads=None):
""" Computes the coefficients of transformed polynomial (equivalent to calling ``self.get_transformed_poly().set_model(...)``)
Parameters
----------
model : ~collections.abc.Callable,numpy.ndarray, optional
The function that needs to be approximated. In the absence of a callable function, the input can be the function evaluated at the quadrature points.
model_grads : ~collections.abc.Callable,numpy.ndarray, optional
The gradient of the function that needs to be approximated. In the absence of a callable gradient function, the input can be a matrix of gradient evaluations at the quadrature points.
"""
# Need to account for the nataf transform here?
model_values = None
model_grads_values = None
if callable(model):
model_values = evaluate_model(self._points, model)
else:
model_values = model
if model_grads is not None:
if callable(model_grads):
model_grads_values = evaluate_model_gradients(self._points, model_grads)
else:
model_grads_values = model_grads
self.corrected_poly.set_model(model_values, model_grads_values)