KernelProjection.h

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// -*- C++ -*-

// KernelProjection.h
//
// Copyright (C) 2004 Olivier Delalleau 
// 
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// 
//  1. Redistributions of source code must retain the above copyright
//     notice, this list of conditions and the following disclaimer.
// 
//  2. Redistributions in binary form must reproduce the above copyright
//     notice, this list of conditions and the following disclaimer in the
//     documentation and/or other materials provided with the distribution.
// 
//  3. The name of the authors may not be used to endorse or promote
//     products derived from this software without specific prior written
//     permission.
// 
// THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
// OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
// NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
// 
// This file is part of the PLearn library. For more information on the PLearn
// library, go to the PLearn Web site at www.plearn.org

/* *******************************************************      
   * $Id: KernelProjection.h,v 1.10 2004/07/21 20:28:13 tihocan Exp $ 
   ******************************************************* */

// Authors: Olivier Delalleau

#ifndef KernelProjection_INC
#define KernelProjection_INC

#include <plearn_learners/generic/PLearner.h>
#include <plearn/ker/Kernel.h>

namespace PLearn {
using namespace std;

class KernelProjection: public PLearner
{

private:

  typedef PLearner inherited;

  mutable Vec k_x_xi;
  mutable Mat result;
  mutable Mat used_eigenvectors;
  
protected:

  // *********************
  // * protected options *
  // *********************

  int n_comp_kept;
  int n_examples;

  // Fields below are not options.

  mutable bool first_output;

  mutable Vec last_input;   
  mutable Vec last_output;  
    
public:

  // ************************
  // * public build options *
  // ************************

  bool compute_costs;
  bool free_extra_components;
  int ignore_n_first;
  Ker kernel;
  real min_eigenvalue;
  int n_comp;
  int n_comp_for_cost;
  int normalize;
  
  // ************************
  // * public learnt options *
  // ************************

  Vec eigenvalues;
  Mat eigenvectors;
  
  // ****************
  // * Constructors *
  // ****************

  KernelProjection();

  // ********************
  // * PLearner methods *
  // ********************

private: 

  void build_();

protected: 
  
  static void declareOptions(OptionList& ol);

public:

  virtual Vec getEigenvalues() {return eigenvalues;}

  // ************************
  // **** Object methods ****
  // ************************

  virtual void build();

  virtual void makeDeepCopyFromShallowCopy(map<const void*, void*>& copies);

  // Declares other standard object methods.
  PLEARN_DECLARE_OBJECT(KernelProjection);

  // **************************
  // **** PLearner methods ****
  // **************************

  virtual int outputsize() const;

  virtual void forget();

  virtual void train();

  virtual void computeOutput(const Vec& input, Vec& output) const;

  virtual void computeCostsFromOutputs(const Vec& input, const Vec& output, 
                                       const Vec& target, Vec& costs) const;
                                
  virtual TVec<string> getTestCostNames() const;

  virtual TVec<string> getTrainCostNames() const;

  virtual void setTrainingSet(VMat training_set, bool call_forget=true);

  // *** SUBCLASS WRITING: ***
  // While in general not necessary, in case of particular needs 
  // (efficiency concerns for ex) you may also want to overload
  // some of the following methods:
  // virtual void computeOutputAndCosts(const Vec& input, const Vec& target, Vec& output, Vec& costs) const;
  // virtual void computeCostsOnly(const Vec& input, const Vec& target, Vec& costs) const;
  // virtual void test(VMat testset, PP<VecStatsCollector> test_stats, VMat testoutputs=0, VMat testcosts=0) const;
  // virtual int nTestCosts() const;
  // virtual int nTrainCosts() const;

};

// Declares a few other classes and functions related to this class.
DECLARE_OBJECT_PTR(KernelProjection);
  
} // end of namespace PLearn

#endif

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