def _apply_controls_to_measurements(self, udata, qdata):
udata = inputchecks.check_controls_data(udata, \
self._discretization.system.nu, \
self._discretization.number_of_controls)
qdata = inputchecks.check_constant_controls_data(qdata, \
self._discretization.system.nq)
optimization_variables_for_measurements = ci.veccat([ \
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["P"],
])
optimization_variables_controls_applied = ci.veccat([ \
udata,
qdata,
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["P"],
])
measurements_fcn = ci.mx_function( \
"measurements_fcn", \
[optimization_variables_for_measurements], \
[self._discretization.measurements])
[self._measurements_controls_applied] = \
measurements_fcn([optimization_variables_controls_applied])
def _apply_controls_to_measurements(self, udata, qdata):
udata = inputchecks.check_controls_data(udata, \
self._discretization.system.nu, \
self._discretization.number_of_controls)
qdata = inputchecks.check_constant_controls_data(qdata, \
self._discretization.system.nq)
optimization_variables_for_measurements = ci.veccat([ \
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["P"],
])
optimization_variables_controls_applied = ci.veccat([ \
udata,
qdata,
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["P"],
])
measurements_fcn = ci.mx_function( \
"measurements_fcn", \
[optimization_variables_for_measurements], \
[self._discretization.measurements])
self._measurements_controls_applied = \
measurements_fcn(optimization_variables_controls_applied)
def _apply_parameters_to_objective(self):
# As mentioned above, the objective does not depend on the actual
# values of V, but on the values of P and EPS_U, while
# P is fed from pdata, and EPS_U is supposed to be 0
objective_free_variables = ci.veccat([ \
self._discretization.optimization_variables["P"],
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
])
objective_free_variables_parameters_applied = ci.veccat([ \
self._pdata,
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
ci.mx(*self._discretization.optimization_variables["EPS_U"].shape),
])
objective_fcn = ci.mx_function("objective_fcn", \
[objective_free_variables], [self._objective_parameters_free])
self._objective = objective_fcn( \
objective_free_variables_parameters_applied)
def _apply_parameters_to_equality_constraints(self):
optimization_variables_for_equality_constraints = ci.veccat([ \
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["P"],
])
optimization_variables_parameters_applied = ci.veccat([ \
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
ci.mx(*self._discretization.optimization_variables["EPS_U"].shape),
self._pdata,
])
equality_constraints_fcn = ci.mx_function( \
"equality_constraints_fcn", \
[optimization_variables_for_equality_constraints], \
[self._discretization.equality_constraints])
[self._equality_constraints_parameters_applied] = \
equality_constraints_fcn([optimization_variables_parameters_applied])
def _apply_parameters_to_objective(self):
objective_free_variables = []
objective_free_variables_parameters_applied = []
for doe_setup in self._doe_setups:
objective_free_variables.append( \
doe_setup._discretization.optimization_variables["P"])
objective_free_variables_parameters_applied.append( \
doe_setup._pdata)
for key in ["U", "Q", "X"]:
objective_free_variables.append( \
doe_setup._discretization.optimization_variables[key])
objective_free_variables_parameters_applied.append( \
doe_setup._discretization.optimization_variables[key])
objective_free_variables = ci.veccat(objective_free_variables)
objective_free_variables_parameters_applied = \
ci.veccat(objective_free_variables_parameters_applied)
objective_fcn = ci.mx_function("objective_fcn", \
[objective_free_variables], [self._objective_parameters_free])
[self._objective] = objective_fcn( \
[objective_free_variables_parameters_applied])
def _apply_parameters_to_equality_constraints(self):
optimization_variables_for_equality_constraints = ci.veccat([ \
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["P"],
])
optimization_variables_parameters_applied = ci.veccat([ \
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
ci.mx(*self._discretization.optimization_variables["EPS_U"].shape),
self._pdata,
])
equality_constraints_fcn = ci.mx_function( \
"equality_constraints_fcn", \
[optimization_variables_for_equality_constraints], \
[self._discretization.equality_constraints])
self._equality_constraints_parameters_applied = \
equality_constraints_fcn(optimization_variables_parameters_applied)
def _apply_parameters_to_objective(self):
# As mentioned above, the objective does not depend on the actual
# values of V, but on the values of P and EPS_E and EPS_U, while
# P is fed from pdata, and EPS_E, EPS_u are supposed to be 0
objective_free_variables = ci.veccat([ \
self._discretization.optimization_variables["P"],
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
])
objective_free_variables_parameters_applied = ci.veccat([ \
self._pdata,
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
ci.mx(*self._discretization.optimization_variables["EPS_U"].shape),
])
objective_fcn = ci.mx_function("objective_fcn", \
[objective_free_variables], [self._objective_parameters_free])
[self._objective] = objective_fcn( \
[objective_free_variables_parameters_applied])
def _apply_parameters_to_objective(self):
objective_free_variables = []
objective_free_variables_parameters_applied = []
for doe_setup in self._doe_setups:
objective_free_variables.append( \
doe_setup._discretization.optimization_variables["P"])
objective_free_variables_parameters_applied.append( \
doe_setup._pdata)
for key in ["U", "Q", "X"]:
objective_free_variables.append( \
doe_setup._discretization.optimization_variables[key])
objective_free_variables_parameters_applied.append( \
doe_setup._discretization.optimization_variables[key])
objective_free_variables = ci.veccat(objective_free_variables)
objective_free_variables_parameters_applied = \
ci.veccat(objective_free_variables_parameters_applied)
objective_fcn = ci.mx_function("objective_fcn", \
[objective_free_variables], [self._objective_parameters_free])
self._objective = objective_fcn( \
objective_free_variables_parameters_applied)
def _set_optimization_variables_initials(self, pinit, xinit):
xinit = inputchecks.check_states_data(xinit, \
self._discretization.system.nx, \
self._discretization.number_of_intervals)
repretitions_xinit = \
self._discretization.optimization_variables["X"][:,:-1].shape[1] / \
self._discretization.number_of_intervals
Xinit = ci.repmat(xinit[:, :-1], repretitions_xinit, 1)
Xinit = ci.horzcat([ \
Xinit.reshape((self._discretization.system.nx, \
Xinit.size() / self._discretization.system.nx)),
xinit[:, -1],
])
pinit = inputchecks.check_parameter_data(pinit, \
self._discretization.system.np)
Pinit = pinit
Vinit = np.zeros(self._discretization.optimization_variables["V"].shape)
EPS_Uinit = np.zeros( \
self._discretization.optimization_variables["EPS_U"].shape)
self._optimization_variables_initials = ci.veccat([ \
Pinit,
Xinit,
EPS_Uinit,
Vinit,
])
def _merge_equaltiy_constraints_parameters_applied(self):
equality_constraints_parameters_applied = [ \
doe_setup._equality_constraints_parameters_applied for \
doe_setup in self._doe_setups]
self._equality_constraints_parameters_applied = \
ci.veccat(equality_constraints_parameters_applied)
def _merge_equaltiy_constraints_parameters_applied(self):
equality_constraints_parameters_applied = [ \
doe_setup._equality_constraints_parameters_applied for \
doe_setup in self._doe_setups]
self._equality_constraints_parameters_applied = \
ci.veccat(equality_constraints_parameters_applied)
def _setup_residuals(self):
self._residuals = ci.sqrt(self._weightings_vectorized) * \
ci.veccat([ \
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["V"],
])
def _setup_residuals(self):
self._residuals = ci.sqrt(self._weightings_vectorized) * \
ci.veccat([ \
self._discretization.optimization_variables["V"],
self._discretization.optimization_variables["EPS_U"],
])
def _set_optimization_variables(self):
self._optimization_variables = ci.veccat([ \
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
])
def _set_optimization_variables(self):
self._optimization_variables = ci.veccat([ \
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
])
def _compute_optimized_covariance_matrix(self):
covariance_matrix_optimized_input = ci.veccat([ \
self._pdata,
self.design_results["x"],
np.zeros(self._discretization.optimization_variables["EPS_U"].shape)
])
self._covariance_matrix_optimized = self._covariance_matrix_fcn( \
covariance_matrix_optimized_input)
def _compute_optimized_covariance_matrix(self):
covariance_matrix_optimized_input = ci.veccat([ \
self._pdata,
self.design_results["x"],
np.zeros(self._discretization.optimization_variables["EPS_U"].shape)
])
self._covariance_matrix_optimized = self._covariance_matrix_fcn( \
[covariance_matrix_optimized_input])[0]
def _compute_initial_covariance_matrix(self):
covariance_matrix_initial_input = ci.veccat([ \
self._pdata,
self._optimization_variables_initials,
np.zeros(self._discretization.optimization_variables["EPS_U"].shape)
])
self._covariance_matrix_initial = self._covariance_matrix_fcn( \
[covariance_matrix_initial_input])[0]
def _compute_initial_covariance_matrix(self):
covariance_matrix_initial_input = ci.veccat([ \
self._pdata,
self._optimization_variables_initials,
np.zeros(self._discretization.optimization_variables["EPS_U"].shape)
])
self._covariance_matrix_initial = self._covariance_matrix_fcn( \
covariance_matrix_initial_input)
def _set_optimization_variables_lower_bounds(self, umin, qmin, xmin, x0):
umin_user_provided = umin
umin = inputchecks.check_controls_data(umin, \
self._discretization.system.nu, 1)
if umin_user_provided is None:
umin = -np.inf * np.ones(umin.shape)
Umin = ci.repmat(umin, 1, \
self._discretization.optimization_variables["U"].shape[1])
qmin_user_provided = qmin
qmin = inputchecks.check_constant_controls_data(qmin, \
self._discretization.system.nq)
if qmin_user_provided is None:
qmin = -np.inf * np.ones(qmin.shape)
Qmin = qmin
xmin_user_provided = xmin
xmin = inputchecks.check_states_data(xmin, \
self._discretization.system.nx, 0)
if xmin_user_provided is None:
xmin = -np.inf * np.ones(xmin.shape)
Xmin = ci.repmat(xmin, 1, \
self._discretization.optimization_variables["X"].shape[1])
Xmin[:,0] = x0
self._optimization_variables_lower_bounds = ci.veccat([ \
Umin,
Qmin,
Xmin,
])
def _set_optimization_variables_lower_bounds(self, umin, qmin, xmin, x0):
umin_user_provided = umin
umin = inputchecks.check_controls_data(umin, \
self._discretization.system.nu, 1)
if umin_user_provided is None:
umin = -np.inf * np.ones(umin.shape)
Umin = ci.repmat(umin, 1, \
self._discretization.optimization_variables["U"].shape[1])
qmin_user_provided = qmin
qmin = inputchecks.check_constant_controls_data(qmin, \
self._discretization.system.nq)
if qmin_user_provided is None:
qmin = -np.inf * np.ones(qmin.shape)
Qmin = qmin
xmin_user_provided = xmin
xmin = inputchecks.check_states_data(xmin, \
self._discretization.system.nx, 0)
if xmin_user_provided is None:
xmin = -np.inf * np.ones(xmin.shape)
Xmin = ci.repmat(xmin, 1, \
self._discretization.optimization_variables["X"].shape[1])
Xmin[:,0] = x0
self._optimization_variables_lower_bounds = ci.veccat([ \
Umin,
Qmin,
Xmin,
])
def _set_cov_matrix_derivative_directions(self):
# These correspond to the optimization variables of the parameter
# estimation problem; the evaluation of the covariance matrix, though,
# does not depend on the actual values of V, EPS_E and EPS_U, and with
# this, the DoE problem does not
self._cov_matrix_derivative_directions = ci.veccat([ \
self._discretization.optimization_variables["P"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["V"],
self._discretization.optimization_variables["EPS_U"],
])
def _set_cov_matrix_derivative_directions(self):
# These correspond to the optimization variables of the parameter
# estimation problem; the evaluation of the covariance matrix, though,
# does not depend on the actual values of V, EPS_E and EPS_U, and with
# this, the DoE problem does not
self._cov_matrix_derivative_directions = ci.veccat([ \
self._discretization.optimization_variables["P"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
self._discretization.optimization_variables["V"],
])
def _setup_covariance_matrix_for_evaluation(self):
covariance_matrix_free_variables = ci.veccat([ \
self._discretization.optimization_variables["P"],
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
])
self._covariance_matrix_fcn = ci.mx_function("covariance_matrix_fcn", \
[covariance_matrix_free_variables], \
[self._covariance_matrix.covariance_matrix])
def _setup_covariance_matrix_for_evaluation(self):
covariance_matrix_free_variables = ci.veccat([ \
self._discretization.optimization_variables["P"],
self._discretization.optimization_variables["U"],
self._discretization.optimization_variables["Q"],
self._discretization.optimization_variables["X"],
self._discretization.optimization_variables["EPS_U"],
])
self._covariance_matrix_fcn = ci.mx_function("covariance_matrix_fcn", \
[covariance_matrix_free_variables], \
[self._covariance_matrix.covariance_matrix])
def _set_optimization_variables_upper_bounds(self, umax, qmax, xmax, x0):
umax_user_provided = umax
umax = inputchecks.check_controls_data(umax, \
self._discretization.system.nu, 1)
if umax_user_provided is None:
umax = np.inf * np.ones(umax.shape)
Umax = ci.repmat(umax, 1, \
self._discretization.optimization_variables["U"].shape[1])
qmax_user_provided = qmax
qmax = inputchecks.check_constant_controls_data(qmax, \
self._discretization.system.nq)
if qmax_user_provided is None:
qmax = -np.inf * np.ones(qmax.shape)
Qmax = qmax
xmax_user_provided = xmax
xmax = inputchecks.check_states_data(xmax, \
self._discretization.system.nx, 0)
if xmax_user_provided is None:
xmax = np.inf * np.ones(xmax.shape)
Xmax = ci.repmat(xmax, 1, \
self._discretization.optimization_variables["X"].shape[1])
Xmax[:,0] = x0
self._optimization_variables_upper_bounds = ci.veccat([ \
Umax,
Xmax,
])
def _set_optimization_variables_upper_bounds(self, umax, qmax, xmax, x0):
umax_user_provided = umax
umax = inputchecks.check_controls_data(umax, \
self._discretization.system.nu, 1)
if umax_user_provided is None:
umax = np.inf * np.ones(umax.shape)
Umax = ci.repmat(umax, 1, \
self._discretization.optimization_variables["U"].shape[1])
qmax_user_provided = qmax
qmax = inputchecks.check_constant_controls_data(qmax, \
self._discretization.system.nq)
if qmax_user_provided is None:
qmax = -np.inf * np.ones(qmax.shape)
Qmax = qmax
xmax_user_provided = xmax
xmax = inputchecks.check_states_data(xmax, \
self._discretization.system.nx, 0)
if xmax_user_provided is None:
xmax = np.inf * np.ones(xmax.shape)
Xmax = ci.repmat(xmax, 1, \
self._discretization.optimization_variables["X"].shape[1])
Xmax[:,0] = x0
self._optimization_variables_upper_bounds = ci.veccat([ \
Umax,
Xmax,
])
def _compute_initial_covariance_matrix(self):
covariance_matrix_initial_input = []
for doe_setup in self._doe_setups:
covariance_matrix_initial_input.append(doe_setup._pdata)
covariance_matrix_initial_input.append( \
doe_setup._optimization_variables_initials)
covariance_matrix_initial_input = ci.veccat( \
covariance_matrix_initial_input)
self._covariance_matrix_initial = self._covariance_matrix_fcn( \
[covariance_matrix_initial_input])[0]
def _compute_initial_covariance_matrix(self):
covariance_matrix_initial_input = []
for doe_setup in self._doe_setups:
covariance_matrix_initial_input.append(doe_setup._pdata)
covariance_matrix_initial_input.append( \
doe_setup._optimization_variables_initials)
covariance_matrix_initial_input = ci.veccat( \
covariance_matrix_initial_input)
self._covariance_matrix_initial = self._covariance_matrix_fcn( \
covariance_matrix_initial_input)
def _setup_covariance_matrix_for_evaluation(self):
covariance_matrix_free_variables = []
for doe_setup in self._doe_setups:
for key in ["P", "U", "Q", "X"]:
covariance_matrix_free_variables.append( \
doe_setup._discretization.optimization_variables[key])
covariance_matrix_free_variables = ci.veccat( \
covariance_matrix_free_variables)
self._covariance_matrix_fcn = ci.mx_function("covariance_matrix_fcn", \
[covariance_matrix_free_variables], \
[self._covariance_matrix.covariance_matrix])
def _setup_covariance_matrix_for_evaluation(self):
covariance_matrix_free_variables = []
for doe_setup in self._doe_setups:
for key in ["P", "U", "Q", "X"]:
covariance_matrix_free_variables.append( \
doe_setup._discretization.optimization_variables[key])
covariance_matrix_free_variables = ci.veccat( \
covariance_matrix_free_variables)
self._covariance_matrix_fcn = ci.mx_function("covariance_matrix_fcn", \
[covariance_matrix_free_variables], \
[self._covariance_matrix.covariance_matrix])
def _set_weightings(self, wv, weps_u):
input_error_weightings = \
inputchecks.check_input_error_weightings(weps_u, \
self._discretization.system.neps_u,
self._discretization.number_of_intervals)
measurement_weightings = \
inputchecks.check_measurement_weightings(wv, \
self._discretization.system.nphi, \
self._discretization.number_of_intervals + 1)
self._weightings_vectorized = ci.veccat([ \
input_error_weightings,
measurement_weightings,
])
def _set_weightings(self, wv, weps_u):
input_error_weightings = \
inputchecks.check_input_error_weightings(weps_u, \
self._discretization.system.neps_u, \
self._discretization.number_of_intervals)
measurement_weightings = \
inputchecks.check_measurement_weightings(wv, \
self._discretization.system.nphi, \
self._discretization.number_of_intervals + 1)
self._weightings_vectorized = ci.veccat([ \
input_error_weightings,
measurement_weightings,
])
def _set_optimization_variables_initials(self, qinit, x0, uinit):
self.simulation = Simulation(self._discretization.system, \
self._pdata, qinit)
self.simulation.run_system_simulation(x0, \
self._discretization.time_points, uinit, print_status = False)
xinit = self.simulation.simulation_results
repretitions_xinit = \
self._discretization.optimization_variables["X"][:,:-1].shape[1] / \
self._discretization.number_of_intervals
Xinit = ci.repmat(xinit[:, :-1], repretitions_xinit, 1)
Xinit = ci.horzcat([ \
Xinit.reshape((self._discretization.system.nx, \
Xinit.size() / self._discretization.system.nx)),
xinit[:, -1],
])
uinit = inputchecks.check_controls_data(uinit, \
self._discretization.system.nu, \
self._discretization.number_of_intervals)
Uinit = uinit
qinit = inputchecks.check_constant_controls_data(qinit, \
self._discretization.system.nq)
Qinit = qinit
self._optimization_variables_initials = ci.veccat([ \
Uinit,
Qinit,
Xinit,
])
def _set_optimization_variables_initials(self, qinit, x0, uinit):
self.simulation = Simulation(self._discretization.system, \
self._pdata, qinit)
self.simulation.run_system_simulation(x0, \
self._discretization.time_points, uinit, print_status = False)
xinit = self.simulation.simulation_results
repretitions_xinit = \
self._discretization.optimization_variables["X"][:,:-1].shape[1] / \
self._discretization.number_of_intervals
Xinit = ci.repmat(xinit[:, :-1], repretitions_xinit, 1)
Xinit = ci.horzcat([ \
Xinit.reshape((self._discretization.system.nx, \
Xinit.numel() / self._discretization.system.nx)),
xinit[:, -1],
])
uinit = inputchecks.check_controls_data(uinit, \
self._discretization.system.nu, \
self._discretization.number_of_intervals)
Uinit = uinit
qinit = inputchecks.check_constant_controls_data(qinit, \
self._discretization.system.nq)
Qinit = qinit
self._optimization_variables_initials = ci.veccat([ \
Uinit,
Qinit,
Xinit,
])
def _compute_optimized_covariance_matrix(self):
starting_position_design_results = 0
covariance_matrix_optimized_input = []
for doe_setup in self._doe_setups:
covariance_matrix_optimized_input.append(doe_setup._pdata)
covariance_matrix_optimized_input.append(self.design_results["x"][ \
starting_position_design_results : \
starting_position_design_results + \
doe_setup._optimization_variables.shape[0]])
starting_position_design_results += \
doe_setup._optimization_variables.shape[0]
covariance_matrix_optimized_input = ci.veccat( \
covariance_matrix_optimized_input)
self._covariance_matrix_optimized = self._covariance_matrix_fcn( \
[covariance_matrix_optimized_input])[0]
def _compute_optimized_covariance_matrix(self):
starting_position_design_results = 0
covariance_matrix_optimized_input = []
for doe_setup in self._doe_setups:
covariance_matrix_optimized_input.append(doe_setup._pdata)
covariance_matrix_optimized_input.append(self.design_results["x"][ \
starting_position_design_results : \
starting_position_design_results + \
doe_setup._optimization_variables.shape[0]])
starting_position_design_results += \
doe_setup._optimization_variables.shape[0]
covariance_matrix_optimized_input = ci.veccat( \
covariance_matrix_optimized_input)
self._covariance_matrix_optimized = self._covariance_matrix_fcn( \
covariance_matrix_optimized_input)
def _set_optimization_variables_initials(self, pinit, xinit):
xinit = inputchecks.check_states_data(xinit, \
self._discretization.system.nx, \
self._discretization.number_of_intervals)
repretitions_xinit = \
self._discretization.optimization_variables["X"][:,:-1].shape[1] / \
self._discretization.number_of_intervals
Xinit = ci.repmat(xinit[:, :-1], repretitions_xinit, 1)
Xinit = ci.horzcat([ \
Xinit.reshape((self._discretization.system.nx, \
Xinit.numel() / self._discretization.system.nx)),
xinit[:, -1],
])
pinit = inputchecks.check_parameter_data(pinit, \
self._discretization.system.np)
Pinit = pinit
Vinit = np.zeros(
self._discretization.optimization_variables["V"].shape)
EPS_Uinit = np.zeros( \
self._discretization.optimization_variables["EPS_U"].shape)
self._optimization_variables_initials = ci.veccat([ \
Pinit,
Xinit,
Vinit,
EPS_Uinit,
])