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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2019-2025, LAAS-CNRS, University of Edinburgh, |
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// Heriot-Watt University |
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// Copyright note valid unless otherwise stated in individual files. |
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// All rights reserved. |
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/////////////////////////////////////////////////////////////////////////////// |
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// Auto-generated file for double |
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#include "crocoddyl/core/numdiff/action.hpp" |
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#include "python/crocoddyl/core/action-base.hpp" |
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#include "python/crocoddyl/core/core.hpp" |
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#include "python/crocoddyl/utils/copyable.hpp" |
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namespace crocoddyl { |
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namespace python { |
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template <typename Model> |
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struct ActionModelNumDiffVisitor |
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: public bp::def_visitor<ActionModelNumDiffVisitor<Model>> { |
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typedef typename Model::ActionDataAbstract Data; |
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typedef typename Model::VectorXs VectorXs; |
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template <class PyClass> |
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void visit(PyClass& cl) const { |
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cl.def("calc", |
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static_cast<void (Model::*)(const std::shared_ptr<Data>&, |
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const Eigen::Ref<const VectorXs>&)>( |
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&Model::calc), |
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bp::args("self", "data", "x")) |
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.def( |
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"calc", |
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static_cast<void (Model::*)( |
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const std::shared_ptr<Data>&, const Eigen::Ref<const VectorXs>&, |
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const Eigen::Ref<const VectorXs>&)>(&Model::calc), |
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bp::args("self", "data", "x", "u"), |
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"Compute the next state and cost value.\n\n" |
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"The system evolution is described in model.\n" |
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":param data: NumDiff action data\n" |
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":param x: state point (dim. state.nx)\n" |
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":param u: control input (dim. nu)") |
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.def("calcDiff", |
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static_cast<void (Model::*)(const std::shared_ptr<Data>&, |
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const Eigen::Ref<const VectorXs>&)>( |
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&Model::calcDiff), |
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bp::args("self", "data", "x")) |
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.def( |
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"calcDiff", |
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static_cast<void (Model::*)( |
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const std::shared_ptr<Data>&, const Eigen::Ref<const VectorXs>&, |
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const Eigen::Ref<const VectorXs>&)>(&Model::calcDiff), |
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bp::args("self", "data", "x", "u"), |
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"Compute the derivatives of the dynamics and cost functions.\n\n" |
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"It computes the Jacobian and Hessian using numerical " |
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"differentiation. It assumes that calc has been run first.\n" |
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":param data: NumDiff action data\n" |
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":param x: state point (dim. state.nx)\n" |
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":param u: control input (dim. nu)") |
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.def("createData", &Model::createData, bp::args("self"), |
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"Create the action data.\n\n" |
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"Each action model (AM) has its own data that needs to be " |
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"allocated.\n" |
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"This function returns the allocated data for a predefined AM.\n" |
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":return AM data.") |
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.add_property( |
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"model", |
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bp::make_function(&Model::get_model, |
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bp::return_value_policy<bp::return_by_value>()), |
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"action model") |
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.add_property( |
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"disturbance", bp::make_function(&Model::get_disturbance), |
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&Model::set_disturbance, |
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"disturbance constant used in the numerical differentiation") |
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.add_property( |
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"withGaussApprox", |
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bp::make_function(&Model::get_with_gauss_approx, |
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bp::return_value_policy<bp::return_by_value>()), |
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"Gauss approximation for computing the Hessians"); |
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} |
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}; |
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template <typename Data> |
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struct ActionDataNumDiffVisitor |
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: public bp::def_visitor<ActionDataNumDiffVisitor<Data>> { |
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template <class PyClass> |
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void visit(PyClass& cl) const { |
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cl.add_property( |
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"Rx", bp::make_getter(&Data::Rx, bp::return_internal_reference<>()), |
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"Jacobian of the cost residual.") |
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.add_property( |
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"Ru", bp::make_getter(&Data::Ru, bp::return_internal_reference<>()), |
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"Jacobian of the cost residual.") |
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.add_property( |
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"dx", bp::make_getter(&Data::dx, bp::return_internal_reference<>()), |
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"state disturbance.") |
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.add_property( |
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"du", bp::make_getter(&Data::du, bp::return_internal_reference<>()), |
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"control disturbance.") |
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.add_property( |
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"data_0", |
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bp::make_getter(&Data::data_0, |
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bp::return_value_policy<bp::return_by_value>()), |
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"data that contains the final results") |
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.add_property( |
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"data_x", |
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bp::make_getter(&Data::data_x, |
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bp::return_value_policy<bp::return_by_value>()), |
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"temporary data associated with the state variation") |
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.add_property( |
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"data_u", |
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bp::make_getter(&Data::data_u, |
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bp::return_value_policy<bp::return_by_value>()), |
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"temporary data associated with the control variation"); |
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} |
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}; |
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#define CROCODDYL_ACTION_MODEL_NUMDIFF_PYTHON_BINDINGS(Scalar) \ |
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typedef ActionModelNumDiffTpl<Scalar> Model; \ |
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typedef ActionModelAbstractTpl<Scalar> ModelBase; \ |
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bp::register_ptr_to_python<std::shared_ptr<Model>>(); \ |
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bp::class_<Model, bp::bases<ModelBase>>( \ |
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"ActionModelNumDiff", \ |
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"Abstract class for computing calcDiff by using numerical " \ |
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"differentiation.\n\n", \ |
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bp::init<std::shared_ptr<ModelBase>, bp::optional<bool>>( \ |
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bp::args("self", "model", "gaussApprox"), \ |
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"Initialize the action model NumDiff.\n\n" \ |
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":param model: action model where we compute the derivatives " \ |
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"through NumDiff\n" \ |
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":param gaussApprox: compute the Hessian using Gauss approximation " \ |
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"(default False)")) \ |
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.def(ActionModelNumDiffVisitor<Model>()) \ |
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.def(CastVisitor<Model>()) \ |
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.def(PrintableVisitor<Model>()) \ |
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.def(CopyableVisitor<Model>()); |
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#define CROCODDYL_ACTION_DATA_NUMDIFF_PYTHON_BINDINGS(Scalar) \ |
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typedef ActionDataNumDiffTpl<Scalar> Data; \ |
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typedef ActionDataAbstractTpl<Scalar> DataBase; \ |
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typedef ActionModelNumDiffTpl<Scalar> Model; \ |
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bp::register_ptr_to_python<std::shared_ptr<Data>>(); \ |
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bp::class_<Data, bp::bases<DataBase>>( \ |
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"ActionDataNumDiff", "Numerical differentiation action data.", \ |
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bp::init<Model*>(bp::args("self", "model"), \ |
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"Create numerical differentiation action data.\n\n" \ |
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":param model: numdiff action model")) \ |
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.def(ActionDataNumDiffVisitor<Data>()) \ |
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.def(CopyableVisitor<Data>()); |
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void exposeActionNumDiff() { |
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CROCODDYL_ACTION_MODEL_NUMDIFF_PYTHON_BINDINGS(double) |
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CROCODDYL_ACTION_DATA_NUMDIFF_PYTHON_BINDINGS(double) |
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} |
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} // namespace python |
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} // namespace crocoddyl |
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