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📄 unscentedkalmanfiltertransitionadaptor.hpp

📁 dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical
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#ifndef INDII_ML_FILTER_UNSCENTEDKALMANFILTERTRANSITIONADAPTOR_HPP#define INDII_ML_FILTER_UNSCENTEDKALMANFILTERTRANSITIONADAPTOR_HPP#include "UnscentedTransformationModel.hpp"namespace indii {  namespace ml {    namespace filter {  template <class T> class UnscentedTransformation;  template <class T> class UnscentedKalmanFilter;  template <class T> class UnscentedKalmanSmoother;  template <class T> class UnscentedKalmanFilterModel;/** * Adaptor mapping UnscentedTransformationModel interface to method calls in * UnscentedKalmanFilterModel. *  * @author Lawrence Murray <lawrence@indii.org> * @version $Rev: 544 $ * @date $Date: 2008-09-01 15:04:39 +0100 (Mon, 01 Sep 2008) $ * * @param T The type of time. * * For internal use only. */template <class T>class UnscentedKalmanFilterTransitionAdaptor :    public UnscentedTransformationModel<T> {  friend class UnscentedTransformation<T>;  friend class UnscentedKalmanFilter<T>;  friend class UnscentedKalmanSmoother<T>;private:  /**   * Constructor.   *    * @param filter UnscentedKalmanFilter for which to map calls.   */  UnscentedKalmanFilterTransitionAdaptor(      UnscentedKalmanFilterModel<T>* model);  /**   * Maps call to UnscentedKalmanFilterModel::transition.   */  virtual indii::ml::aux::vector propagate(const indii::ml::aux::vector& x,      const T delta = 0);  /**   * Filter for which to act as adaptor.   */  UnscentedKalmanFilterModel<T>* model;};    }  }}#include "UnscentedKalmanFilterModel.hpp"template <class T>indii::ml::filter::UnscentedKalmanFilterTransitionAdaptor<T>::UnscentedKalmanFilterTransitionAdaptor(    UnscentedKalmanFilterModel<T>* model) : model(model) {  //}template <class T>indii::ml::aux::vector    indii::ml::filter::UnscentedKalmanFilterTransitionAdaptor<T>::propagate(    const indii::ml::aux::vector& X, const T delta) {  namespace aux = indii::ml::aux;  namespace ublas = boost::numeric::ublas;      const unsigned int N = model->getStateSize();  const unsigned int W = model->getSystemNoise().getDimensions();  aux::vector x(project(X, ublas::range(0,N)));  aux::vector w(project(X, ublas::range(N,N+W)));  return model->transition(x, w, delta);}#endif

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