path: root/meowpp/math/Transformation.h

#ifndef   math_Transformation_H__
#define   math_Transformation_H__

#include "Matrix.h"
#include "../Self.h"

#include <list>
#include <cstdlib>

namespace meow {

/*!
 * @brief A base class for implementing kinds of transformations.
 *
 * We define that the input and output form of our transformations all be
 * \b matrix . Some advance methods such as calculating jacobian matrix
 * will require that the input form must be a vector.
 * @author cat_leopard
 */
template<class Scalar>
class Transformation {
private:
  struct Myself {
    size_t  inputRows_;
    size_t  inputCols_;
    size_t outputRows_;
    size_t outputCols_;
    size_t      psize_;

    Myself(Myself const& b):
    inputRows_(b.inputRows_), inputCols_(b.inputCols_),
    outputRows_(b.outputRows_), outputCols_(b.outputCols_),
    psize_(b.psize_) {
    }

    Myself(size_t ir, size_t ic, size_t or_, size_t oc, size_t ps):
    inputRows_(ir), inputCols_(ic), outputRows_(or_), outputCols_(oc),
    psize_(ps) {
    }

    ~Myself() {
    }
  };

  Self<Myself> const self;
protected:
  /*!
   * Construct and setup
   * @param [in] inputRows  number of rows    of the input  matrix.
   * @param [in] inputCols  number of columns of the input  matrix.
   * @param [in] outputRows number of rows    of the output matrix.
   * @param [in] outputCols number of columns of the output matrix.
   * @param [in] psize      number of parameters
   */
  Transformation(size_t  inputRows, size_t  inputCols,
                 size_t outputRows, size_t outputCols,
                 size_t psize):
  self(Myself(inputRows, inputCols, outputRows, outputCols, psize)) {
  }

  /*!
   * Construct and copy setings from another transformation class.
   * @param [in] b Specify where to copy the informations.
   */
  Transformation(Transformation const& b):
  self(b.self, Self<Myself>::COPY_FROM) {
  }

  /*!
   * @brief Copy from the specified one
   *
   * @param [in] b The specified one
   * @return \c *this
   */
  Transformation& copyFrom(Transformation const& b) {
    self().copyFrom(b.self);
    return *this;
  }

  /*!
   * @brief reference from the specified one
   *
   * @param [in] b The specified one
   * @return \c *this
   */
  Transformation& referenceFrom(Transformation const& b) {
    self().referenceFrom(b.self);
    return *this;
  }
public:
  /*!
   * Destructor
   */
  virtual ~Transformation() {
  }

  /*!
   * @brief Return the number of rows of the input matrix.
   *
   * @return Number of rows.
   */
  size_t inputRows() const {
    return self->inputRows_;
  }

  /*!
   * @brief Return the number of columns of the input matrix.
   *
   * @return Number of columns.
   */
  size_t inputCols() const {
    return self->inputCols_;
  }

  /*!
   * @brief Return the number of rows of the output matrix.
   *
   * @return Number of rows.
   */
  size_t outputRows() const {
    return self->outputRows_;
  }

  /*!
   * @brief Return the number of columns of the output matrix.
   *
   * @return Number of columns.
   */
  size_t outputCols() const {
    return self->outputCols_;
  }

  /*!
   * @brief Return the number of parameters.
   *
   * @return Number of parameters.
   */
  size_t parameterSize() const {
    return self->psize_;
  }

  /*!
   * @brief Get the \a i -th parameter.
   *
   * @param [in] i The index of the specified parameter.
   * @note It's a pure virtual method.
   */
  virtual Scalar parameter(size_t i) const = 0;

  /*!
   * @brief Setup the \a i -th parameter.
   *
   * @param [in] i The index of the specified parameter.
   * @param [in] s The new value to the specified parameter.
   * @note It's a pure virtual method.
   */
  virtual Scalar parameter(size_t i, Scalar const& s) = 0;

  /*!
   * @brief Do transformate.
   *
   * @param [in] x The input matrix.
   * @note It's a pure virtual method.
   */
  virtual Matrix<Scalar> transformate(Matrix<Scalar> const& x) const = 0;

  /*!
   * @brief Calculate the jacobian matrix (derivate by the input matrix)
   *        of the transformation.
   *
   * Consider the case of a non-differentiable
   * transformation might be implemented, we return an empty matrix
   * now instead of making it be a pure virtual method.
   * @param [in] x The input matrix.
   * @return       An empty matrix.
   */
  virtual Matrix<Scalar> jacobian(Matrix<Scalar> const& x) const {
    return Matrix<Scalar>();
  }

  /*!
   * @brief Calculate the jacobian matrix (derivate by the \a i -th parameter)
   *        of the transformation.
   *
   * Consider the case of a non-differentiable transformation might be
   * implemented, we return an empty matrix now instead of making it be
   * a pure virtual method.
   * @param [in] x The input matrix.
   * @param [in] i The index of the specified parameter.
   * @return       An empty matrix.
   */
  virtual Matrix<Scalar> jacobian(Matrix<Scalar> const& x, size_t i) const {
    return Matrix<Scalar>();
  }

  /*!
   * @brief Return whether this transformation is inversable or not
   *
   * @return \c false
   */
  virtual bool inversable() const { return false; }

  /*!
   * @brief Do the inverse transformation
   *
   * @param [in] x The input matirx
   * @return       An empty matrix
   */
  virtual Matrix<Scalar> transformateInv(Matrix<Scalar> const& x) const {
    return Matrix<Scalar>();
  }

  /*!
   * @brief Return the jacobian matrix of the inverse transformation
   *
   * @param [in] x The input matirx
   * @return       An empty matrix
   */
  virtual Matrix<Scalar> jacobianInv(Matrix<Scalar> const& x) const {
    return Matrix<Scalar>();
  }

  /*!
   * @brief Return the jacobian matrix of the inverse transformation
   *
   * @param [in] x The input matirx
   * @param [in] i The index of the specified parameter.
   * @return       An empty matrix
   */
  virtual Matrix<Scalar> jacobianInv(Matrix<Scalar> const& x, size_t i) const {
    return Matrix<Scalar>();
  }
};

} // meow

#endif // math_Transformation_H__