STK_ProjectedVariance.h

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/*--------------------------------------------------------------------*/
/*     Copyright (C) 2004-2016  Serge Iovleff, Université Lille 1, Inria
 
    This program is free software; you can redistribute it and/or modify
    it under the terms of the GNU Lesser General Public License as
    published by the Free Software Foundation; either version 2 of the
    License, or (at your option) any later version.
 
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Lesser General Public License for more details.
 
    You should have received a copy of the GNU Lesser General Public
    License along with this program; if not, write to the
    Free Software Foundation, Inc.,
    59 Temple Place,
    Suite 330,
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    USA
 
    Contact : S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
*/
 
/*
 * Project:  stkpp::AAModels
 * Purpose:  Implementation of the ILinearReduct interface using the total variance.
 * Author:   iovleff, S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
 **/
 
 
#ifndef STK_PROJECTEDVARIANCE_H
#define STK_PROJECTEDVARIANCE_H
 
#include <STatistiK/include/STK_Stat_MultivariateReal.h>
#include <Algebra/include/STK_SymEigen.h>
#include "STK_ILinearReduct.h"
 
namespace STK
{
 
template<class Array>
class ProjectedVariance: public ILinearReduct<Array, VectorX>
{
  public:
    typedef ILinearReduct<Array, VectorX> Base;
    using Base::p_data_;
    using Base::p_reduced_;
    using Base::axis_;
    using Base::idx_values_;
    ProjectedVariance();
    ProjectedVariance(Array const* p_data);
    ProjectedVariance(Array const& data);
    ProjectedVariance(ProjectedVariance const& reducer);
    virtual ~ProjectedVariance();
    inline virtual ProjectedVariance* clone() const
    { return new ProjectedVariance(*this);}
 
  protected:
    ArraySquareX covariance_;
 
  private:
    virtual void maximizeStep();
 
    virtual void maximizeStep(VectorX const& weights);
    void computeAxis();
    virtual void update();
};
 
/* default constructor */
template<class Array>
ProjectedVariance<Array>::ProjectedVariance(): Base() {}
/* Constructor.
 *  @param p_data a pointer on the constant data set to reduce.
 **/
template<class Array>
ProjectedVariance<Array>::ProjectedVariance( Array const* p_data)
                                          : Base(p_data)
{}
/* Constructor.
 *  @param data a constatn reference on the data set to reduce.
 **/
template<class Array>
ProjectedVariance<Array>::ProjectedVariance( Array const& data)
                                          : Base(data)
{}
/* Copy constructor.
 * @param reducer the reducer to copy
 **/
template<class Array>
ProjectedVariance<Array>::ProjectedVariance( ProjectedVariance const& reducer)
                                          : Base(reducer)
{}
 
 /* Destructor */
template<class Array>
ProjectedVariance<Array>::~ProjectedVariance()
{}
 
/* Find the axis by maximizing the Index. */
template<class Array>
void ProjectedVariance<Array>::maximizeStep()
{
#ifdef STK_REDUCT_DEBUG
  if (!p_data_)
  { STKRUNTIME_ERROR_NO_ARG(ProjectedVariance::maximizeStep,data is not set);}
#endif
  covariance_.move(Stat::covariance(*p_data_, true));
  computeAxis();
}
 
/* Find the axis by maximizing the weighed Index.
 * @param weights the weights of the samples.
 **/
template<class Array>
void ProjectedVariance<Array>::maximizeStep(VectorX const& weights)
{
#ifdef STK_REDUCT_DEBUG
  if (!p_data_)
  { STKRUNTIME_ERROR_NO_ARG(ProjectedVariance::maximizeStep,data is not set);}
#endif
  covariance_.move(Stat::covariance(*p_data_, weights, true));
  computeAxis();
}
 
/* compute axis and index. */
template<class Array>
void ProjectedVariance<Array>::computeAxis()
{
  SymEigen<ArraySquareX> eigen(covariance_);
  eigen.run();
 
  // compute the range of the axis
  Range range(p_data_->beginCols(), std::min(this->dim_, p_data_->sizeCols()));
  // copy axis and index values
  axis_.resize(p_data_->cols(), range);
  idx_values_.resize(range);
  axis_       = eigen.rotation().col(range);
  idx_values_ = eigen.eigenValues().sub(range);
}
 
/* update the class if a new data set is set */
template<class Array>
void ProjectedVariance<Array>::update()
{
  covariance_.clear();
}
 
 
} // namespace STK
 
#endif /* STK_PROJECTEDVARIANCE_H_ */