コード例 #1
0
ファイル: doe.py プロジェクト: adbuerger/casiopeia
    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])
コード例 #2
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ファイル: doe.py プロジェクト: adbuerger/casiopeia
    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])
コード例 #3
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ファイル: doe.py プロジェクト: MannyKayy/casiopeia
    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)
コード例 #4
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ファイル: doe.py プロジェクト: MannyKayy/casiopeia
    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)
コード例 #5
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ファイル: doe.py プロジェクト: adbuerger/casiopeia
    def _setup_gauss_newton_lagrangian_hessian(self):

        gauss_newton_lagrangian_hessian_diag = ci.vertcat([ \
            ci.mx(self._cov_matrix_derivative_directions.shape[0] - \
                self._weightings_vectorized.shape[0], 1), \
            self._weightings_vectorized])

        self._gauss_newton_lagrangian_hessian = ci.diag( \
            gauss_newton_lagrangian_hessian_diag)
コード例 #6
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ファイル: doe.py プロジェクト: MannyKayy/casiopeia
    def _setup_gauss_newton_lagrangian_hessian(self):

        gauss_newton_lagrangian_hessian_diag = ci.vertcat([ \
            ci.mx(self._cov_matrix_derivative_directions.shape[0] - \
                self._weightings_vectorized.shape[0], 1), \
            self._weightings_vectorized])

        self._gauss_newton_lagrangian_hessian = ci.diag( \
            gauss_newton_lagrangian_hessian_diag)
コード例 #7
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ファイル: pe.py プロジェクト: MannyKayy/casiopeia
    def gauss_newton_lagrangian_hessian(self):

        gauss_newton_lagrangian_hessian_diag = ci.vertcat([ \
            ci.mx(self._optimization_variables.shape[0] - \
                self._weightings_vectorized.shape[0], 1), \
            self._weightings_vectorized])

        gauss_newton_lagrangian_hessian = ci.diag( \
            gauss_newton_lagrangian_hessian_diag)

        return gauss_newton_lagrangian_hessian
コード例 #8
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ファイル: pe.py プロジェクト: adbuerger/casiopeia
    def gauss_newton_lagrangian_hessian(self):

        gauss_newton_lagrangian_hessian_diag = ci.vertcat([ \
            ci.mx(self._optimization_variables.shape[0] - \
                self._weightings_vectorized.shape[0], 1), \
            self._weightings_vectorized])

        gauss_newton_lagrangian_hessian = ci.diag( \
            gauss_newton_lagrangian_hessian_diag)

        return gauss_newton_lagrangian_hessian
コード例 #9
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    def _setup_covariance_matrix(self, kkt_matrix, \
            number_of_unknown_parameters):

        I = ci.mx_eye(number_of_unknown_parameters)
        O = ci.mx(kkt_matrix.shape[0] - number_of_unknown_parameters, \
            number_of_unknown_parameters)

        Z_p = ci.vertcat([I, O])

        self._covariance_matrix = ci.solve(kkt_matrix, Z_p, "csparse")[ \
            :number_of_unknown_parameters, :number_of_unknown_parameters]
コード例 #10
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ファイル: matrices.py プロジェクト: adbuerger/casiopeia
    def _setup_covariance_matrix(self, kkt_matrix, \
            number_of_unknown_parameters):


        I = ci.mx_eye(number_of_unknown_parameters)
        O = ci.mx(kkt_matrix.shape[0] - number_of_unknown_parameters, \
            number_of_unknown_parameters)

        Z_p = ci.vertcat([I,O])

        self._covariance_matrix = ci.solve(kkt_matrix, Z_p, "csparse")[ \
            :number_of_unknown_parameters, :number_of_unknown_parameters]
コード例 #11
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ファイル: matrices.py プロジェクト: adbuerger/casiopeia
    def _setup_kkt_matrix(self, equality_constraints, optimization_variables):

        # Construct the KKT matrix from the Hessian of the Langrangian and the
        # Jacobian of the equality constraints

        kkt_matrix_A = self.hess_lag

        kkt_matrix_B = ci.jacobian(equality_constraints, \
            optimization_variables)

        kkt_matrix_C = ci.mx(equality_constraints.shape[0], \
            equality_constraints.shape[0])

        self._kkt_matrix = ci.blockcat( \
            kkt_matrix_A, kkt_matrix_B.T, \
            kkt_matrix_B, kkt_matrix_C)
コード例 #12
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    def _setup_kkt_matrix(self, equality_constraints, optimization_variables):

        # Construct the KKT matrix from the Hessian of the Langrangian and the
        # Jacobian of the equality constraints

        kkt_matrix_A = self.hess_lag

        kkt_matrix_B = ci.jacobian(equality_constraints, \
            optimization_variables)

        kkt_matrix_C = ci.mx(equality_constraints.shape[0], \
            equality_constraints.shape[0])

        self._kkt_matrix = ci.blockcat( \
            kkt_matrix_A, kkt_matrix_B.T, \
            kkt_matrix_B, kkt_matrix_C)