def update_training_derivatives(self, dyt_dxt, kx, name=None):
"""
Update the training data (values) at the previously set input values.
Parameters
----------
dyt_dxt : np.ndarray[nt, ny] or np.ndarray[nt]
The derivatives values for the nt training points.
kx : int
0-based index of the derivatives being set.
name : str or None
An optional label for the group of training points being set.
This is only used in special situations (e.g., multi-fidelity applications).
"""
check_support(self, "training_derivatives")
dyt_dxt = check_2d_array(dyt_dxt, "dyt_dxt")
if kx not in self.training_points[name]:
raise ValueError(
"The training points must be set first with set_training_values "
+ "before calling update_training_values.")
xt = self.training_points[name][kx][0]
if xt.shape[0] != dyt_dxt.shape[0]:
raise ValueError(
"The number of training points does not agree with the earlier call of "
+ "set_training_values.")
self.training_points[name][kx + 1][1] = np.array(dyt_dxt)
def set_training_derivatives(self, xt, dyt_dxt, kx, name=None):
"""
Set training data (derivatives).
Parameters
----------
xt : np.ndarray[nt, nx] or np.ndarray[nt]
The input values for the nt training points.
dyt_dxt : np.ndarray[nt, ny] or np.ndarray[nt]
The derivatives values for the nt training points.
kx : int
0-based index of the derivatives being set.
name : str or None
An optional label for the group of training points being set.
This is only used in special situations (e.g., multi-fidelity applications).
"""
check_support(self, "training_derivatives")
xt = check_2d_array(xt, "xt")
dyt_dxt = check_2d_array(dyt_dxt, "dyt_dxt")
if xt.shape[0] != dyt_dxt.shape[0]:
raise ValueError(
"the first dimension of xt and dyt_dxt must have the same length"
)
if not isinstance(kx, int):
raise ValueError("kx must be an int")
self.training_points[name][kx + 1] = [np.array(xt), np.array(dyt_dxt)]
def _predict_variance_derivatives(self, x):
"""
Implemented by surrogate models to predict the derivation of the variance at a point (optional).
Parameters:
-----------
x : np.ndarray
Input value for the prediction point.
Returns:
--------
derived_variance: np.ndarray
The jacobian of the variance
"""
check_support(self, "variance_derivatives", fail=True)
def predict_derivatives(self, x, kx):
"""
Predict the dy_dx derivatives at a set of points.
Parameters
----------
x : np.ndarray[n, nx] or np.ndarray[n]
Input values for the prediction points.
kx : int
The 0-based index of the input variable with respect to which derivatives are desired.
Returns
-------
dy_dx : np.ndarray[n, ny]
Derivatives.
"""
check_support(self, 'derivatives')
x = check_2d_array(x, 'x')
check_nx(self.nx, x)
n = x.shape[0]
self.printer.active = self.options['print_global'] and self.options[
'print_prediction']
if self.name == 'MixExp':
# Mixture of experts model
self.printer._title('Evaluation of the Mixture of experts')
else:
self.printer._title('Evaluation')
self.printer(' %-12s : %i' % ('# eval points.', n))
self.printer()
#Evaluate the unknown points using the specified model-method
with self.printer._timed_context('Predicting', key='prediction'):
y = self._predict_derivatives(x, kx)
time_pt = self.printer._time('prediction')[-1] / n
self.printer()
self.printer('Prediction time/pt. (sec) : %10.7f' % time_pt)
self.printer()
return y.reshape((n, self.ny))
def predict_variances(self, x):
"""
Predict the variances at a set of points.
Parameters
----------
x : np.ndarray[nt, nx] or np.ndarray[nt]
Input values for the prediction points.
Returns
-------
s2 : np.ndarray[nt, ny]
Variances.
"""
check_support(self, "variances")
check_nx(self.nx, x)
n = x.shape[0]
s2 = self._predict_variances(x)
return s2.reshape((n, self.ny))
def predict_derivatives(self, x: np.ndarray, kx: int) -> np.ndarray:
"""
Predict the dy_dx derivatives at a set of points.
Parameters
----------
x : np.ndarray[nt, nx] or np.ndarray[nt]
Input values for the prediction points.
kx : int
The 0-based index of the input variable with respect to which derivatives are desired.
Returns
-------
dy_dx : np.ndarray[nt, ny]
Derivatives.
"""
check_support(self, "derivatives")
x = ensure_2d_array(x, "x")
check_nx(self.nx, x)
n = x.shape[0]
self.printer.active = (
self.options["print_global"] and self.options["print_prediction"]
)
if self.name == "MixExp":
# Mixture of experts model
self.printer._title("Evaluation of the Mixture of experts")
else:
self.printer._title("Evaluation")
self.printer(" %-12s : %i" % ("# eval points.", n))
self.printer()
# Evaluate the unknown points using the specified model-method
with self.printer._timed_context("Predicting", key="prediction"):
y = self._predict_derivatives(x, kx)
time_pt = self.printer._time("prediction")[-1] / n
self.printer()
self.printer("Prediction time/pt. (sec) : %10.7f" % time_pt)
self.printer()
return y.reshape((n, self.ny))
def predict_output_derivatives(self, x):
"""
Predict the derivatives dy_dyt at a set of points.
Parameters
----------
x : np.ndarray[nt, nx] or np.ndarray[nt]
Input values for the prediction points.
Returns
-------
dy_dyt : dict of np.ndarray[nt, nt]
Dictionary of output derivatives.
Key is None for derivatives wrt yt and kx for derivatives wrt dyt_dxt.
"""
check_support(self, "output_derivatives")
check_nx(self.nx, x)
dy_dyt = self._predict_output_derivatives(x)
return dy_dyt
def predict_variance_derivatives(self, x):
"""
Predict the derivation of the variance at a point
Parameters:
-----------
x : np.ndarray
Input value for the prediction point.
Returns:
--------
derived_variance: np.ndarray
The jacobian of the variance
"""
x = ensure_2d_array(x, "x")
check_support(self, "variance_derivatives")
check_nx(self.nx, x)
n = x.shape[0]
self.printer.active = (
self.options["print_global"] and self.options["print_prediction"]
)
if self.name == "MixExp":
# Mixture of experts model
self.printer._title("Evaluation of the Mixture of experts")
else:
self.printer._title("Evaluation")
self.printer(" %-12s : %i" % ("# eval points.", n))
self.printer()
# Evaluate the unknown points using the specified model-method
with self.printer._timed_context("Predicting", key="prediction"):
y = self._predict_variance_derivatives(x)
time_pt = self.printer._time("prediction")[-1] / n
self.printer()
self.printer("Prediction time/pt. (sec) : %10.7f" % time_pt)
self.printer()
return y
def _predict_variances(self, x):
"""
Implemented by surrogate models to predict the variances at a set of points (optional).
If this method is implemented, the surrogate model should have
::
self.supports['variances'] = True
in the _initialize() implementation.
Parameters
----------
x : np.ndarray[nt, nx]
Input values for the prediction points.
Returns
-------
s2 : np.ndarray[nt, ny]
Variances.
"""
check_support(self, "variances", fail=True)
def _predict_output_derivatives(self, x):
"""
Implemented by surrogate models to predict the dy_dyt derivatives (optional).
If this method is implemented, the surrogate model should have
::
self.supports['output_derivatives'] = True
in the _initialize() implementation.
Parameters
----------
x : np.ndarray[nt, nx]
Input values for the prediction points.
Returns
-------
dy_dyt : dict of np.ndarray[nt, nt]
Dictionary of output derivatives.
Key is None for derivatives wrt yt and kx for derivatives wrt dyt_dxt.
"""
check_support(self, "output_derivatives", fail=True)
def _predict_derivatives(self, x, kx):
"""
Implemented by surrogate models to predict the dy_dx derivatives (optional).
If this method is implemented, the surrogate model should have
::
self.supports['derivatives'] = True
in the _initialize() implementation.
Parameters
----------
x : np.ndarray[nt, nx]
Input values for the prediction points.
kx : int
The 0-based index of the input variable with respect to which derivatives are desired.
Returns
-------
dy_dx : np.ndarray[nt, ny]
Derivatives.
"""
check_support(self, "derivatives", fail=True)
