diff --git a/include/crocoddyl/core/action-base.hpp b/include/crocoddyl/core/action-base.hpp
index 2909018454c6fae93a6c225ded2b620fb59e0a61..7ce25312a279eb568ba3e38931a205a7ab2d14cd 100644
--- a/include/crocoddyl/core/action-base.hpp
+++ b/include/crocoddyl/core/action-base.hpp
@@ -41,10 +41,10 @@ class ActionModelAbstract {
StateAbstract& get_state() const;
protected:
- unsigned int nu_; //!< Control dimension
- unsigned int nr_; //!< Dimension of the cost residual
- StateAbstract& state_; //!< Model of the state
- Eigen::VectorXd unone_; //!< Neutral state
+ unsigned int nu_; //!< Control dimension
+ unsigned int nr_; //!< Dimension of the cost residual
+ StateAbstract& state_; //!< Model of the state
+ Eigen::VectorXd unone_; //!< Neutral state
#ifdef PYTHON_BINDINGS
diff --git a/include/crocoddyl/core/numdiff/action.hpp b/include/crocoddyl/core/numdiff/action.hpp
index 0ae52a8975d2df9b428a01fafd47db5e6d675126..2253a9811e49298cb6336ec4a48e0267b45cddc2 100644
--- a/include/crocoddyl/core/numdiff/action.hpp
+++ b/include/crocoddyl/core/numdiff/action.hpp
@@ -17,13 +17,13 @@ namespace crocoddyl {
/**
* @brief This class computes the numerical differentiation of an ActionModel.
- *
+ *
* It computes the same quantity as a normal model would do but using numerical
* differentiation.
* The subtility is in the computation of the Hessian of the cost. Let us
* concider that the ActionModel owns a cost residual. This means that the cost
- * is the square of a residual \f$ l(x,u) = .5 r(x,u)**2 \f$, with
- * \f$ r(x,u) \f$ being a vector. Therefore the derivatives of the cost
+ * is the square of a residual \f$ l(x,u) = .5 r(x,u)**2 \f$, with
+ * \f$ r(x,u) \f$ being a vector. Therefore the derivatives of the cost
* \f$ l \f$ can be expressed in function of the derivatives of the residuals
* (jacobians), denoted by \f$ R_x \f$ and \f$ R_u \f$. Which would be:
* \f{eqnarray*}{
@@ -49,8 +49,8 @@ class ActionModelNumDiff : public ActionModelAbstract {
public:
/**
* @brief Construct a new ActionModelNumDiff object
- *
- * @param model
+ *
+ * @param model
* @param with_gauss_approx defines if we use the Gauss approximation of the
* cost hessian or not.
*/
@@ -60,12 +60,12 @@ class ActionModelNumDiff : public ActionModelAbstract {
void calc(const boost::shared_ptr<ActionDataAbstract>& data, const Eigen::Ref<const Eigen::VectorXd>& x,
const Eigen::Ref<const Eigen::VectorXd>& u);
/**
- * @brief calcDiff computes the
- *
- * @param data
- * @param x
- * @param u
- * @param recalc
+ * @brief calcDiff computes the
+ *
+ * @param data
+ * @param x
+ * @param u
+ * @param recalc
*/
void calcDiff(const boost::shared_ptr<ActionDataAbstract>& data, const Eigen::Ref<const Eigen::VectorXd>& x,
const Eigen::Ref<const Eigen::VectorXd>& u, const bool& recalc = true);
@@ -127,14 +127,16 @@ struct ActionDataNumDiff : public ActionDataAbstract {
}
}
- Eigen::MatrixXd Rx; //!< Cost residual jacobian: d r / dx
- Eigen::MatrixXd Ru; //!< Cost residual jacobian: d r / du
- Eigen::VectorXd dx; //!< State disturbance
- Eigen::VectorXd du; //!< Control disturbance
- Eigen::VectorXd xp; //!< The integrated state from the disturbance on one DoF "\f$ \int x dx_i \f$"
- boost::shared_ptr<ActionDataAbstract> data_0; //!< The data that contains the final results
- std::vector<boost::shared_ptr<ActionDataAbstract> > data_x; //!< The temporary data associated with the state variation
- std::vector<boost::shared_ptr<ActionDataAbstract> > data_u; //!< The temporary data associated with the control variation
+ Eigen::MatrixXd Rx; //!< Cost residual jacobian: d r / dx
+ Eigen::MatrixXd Ru; //!< Cost residual jacobian: d r / du
+ Eigen::VectorXd dx; //!< State disturbance
+ Eigen::VectorXd du; //!< Control disturbance
+ Eigen::VectorXd xp; //!< The integrated state from the disturbance on one DoF "\f$ \int x dx_i \f$"
+ boost::shared_ptr<ActionDataAbstract> data_0; //!< The data that contains the final results
+ std::vector<boost::shared_ptr<ActionDataAbstract> >
+ data_x; //!< The temporary data associated with the state variation
+ std::vector<boost::shared_ptr<ActionDataAbstract> >
+ data_u; //!< The temporary data associated with the control variation
};
} // namespace crocoddyl
diff --git a/src/core/numdiff/action.cpp b/src/core/numdiff/action.cpp
index 82418e3bf815f15907bca5419fd89ad038870cb1..de077f566d39f902b16f5c1623ee8d6c6877e586 100644
--- a/src/core/numdiff/action.cpp
+++ b/src/core/numdiff/action.cpp
@@ -76,7 +76,7 @@ void ActionModelNumDiff::calcDiff(const boost::shared_ptr<ActionDataAbstract>& d
model_.get_state().diff(xn0, xn, data_nd->Fu.col(iu));
data->Lu(iu) = (c - c0) / disturbance_;
- if (with_gauss_approx_) {
+ if (with_gauss_approx_) {
data_nd->Ru.col(iu) = (data_nd->data_u[iu]->r - data_nd->data_0->r) / disturbance_;
}
data_nd->du(iu) = 0.0;
@@ -87,14 +87,11 @@ void ActionModelNumDiff::calcDiff(const boost::shared_ptr<ActionDataAbstract>& d
data->Lxx = data_nd->Rx.transpose() * data_nd->Rx;
data->Lxu = data_nd->Rx.transpose() * data_nd->Ru;
data->Luu = data_nd->Ru.transpose() * data_nd->Ru;
- }
- else
- {
+ } else {
data->Lxx.fill(0.0);
data->Lxu.fill(0.0);
data->Luu.fill(0.0);
}
-
}
ActionModelAbstract& ActionModelNumDiff::get_model() const { return model_; }
diff --git a/unittest/test_actions.cpp b/unittest/test_actions.cpp
index c3e44b68eac3fb20e97eb4e9af8338019e5f3ba6..367c9a8181d89eb847b35ae47e8803310d0f5640 100644
--- a/unittest/test_actions.cpp
+++ b/unittest/test_actions.cpp
@@ -79,7 +79,7 @@ void test_partial_derivatives_against_numdiff(crocoddyl::ActionModelAbstract& mo
BOOST_CHECK((data->Lxx - data_num_diff->Lxx).isMuchSmallerThan(1.0, tol));
BOOST_CHECK((data->Lxu - data_num_diff->Lxu).isMuchSmallerThan(1.0, tol));
BOOST_CHECK((data->Luu - data_num_diff->Luu).isMuchSmallerThan(1.0, tol));
- }else{
+ } else {
BOOST_CHECK((data_num_diff->Lxx).isMuchSmallerThan(1.0, tol));
BOOST_CHECK((data_num_diff->Lxu).isMuchSmallerThan(1.0, tol));
BOOST_CHECK((data_num_diff->Luu).isMuchSmallerThan(1.0, tol));