PLearn 0.1
RegressionTreeLeave.h
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00001 // -*- C++ -*-
00002 
00003 // RegressionTreeLeave.h
00004 // Copyright (c) 1998-2002 Pascal Vincent
00005 // Copyright (C) 1999-2002 Yoshua Bengio and University of Montreal
00006 // Copyright (c) 2002 Jean-Sebastien Senecal, Xavier Saint-Mleux, Rejean Ducharme
00007 //
00008 // Redistribution and use in source and binary forms, with or without
00009 // modification, are permitted provided that the following conditions are met:
00010 // 
00011 //  1. Redistributions of source code must retain the above copyright
00012 //     notice, this list of conditions and the following disclaimer.
00013 // 
00014 //  2. Redistributions in binary form must reproduce the above copyright
00015 //     notice, this list of conditions and the following disclaimer in the
00016 //     documentation and/or other materials provided with the distribution.
00017 // 
00018 //  3. The name of the authors may not be used to endorse or promote
00019 //     products derived from this software without specific prior written
00020 //     permission.
00021 // 
00022 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00023 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00024 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00025 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00026 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00027 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00028 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00029 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00030 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00031 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00032 // 
00033 // This file is part of the PLearn library. For more information on the PLearn
00034 // library, go to the PLearn Web site at www.plearn.org
00035 
00036 
00037 /* *********************************************************************************   
00038  * $Id: RegressionTreeLeave.h, v 1.0 2004/07/19 10:00:00 Bengio/Kegl/Godbout     *
00039  * This file is part of the PLearn library.                                      *
00040  ********************************************************************************* */
00041 
00042 #ifndef RegressionTreeLeave_INC
00043 #define RegressionTreeLeave_INC
00044 
00045 #include <plearn/base/Object.h>
00046 #include "RegressionTreeRegisters.h"
00047 namespace PLearn {
00048 using namespace std;
00049 
00050 class RegressionTreeLeave: public Object
00051 {
00052     typedef Object inherited;
00053     friend class RegressionTreeNode;
00054     friend class RegressionTreeRegisters;
00055     static Vec dummy_vec;
00056 
00057 public:
00058     bool missing_leave;
00059     real loss_function_weight;
00060     static int  verbosity;
00061 
00062 /*
00063   Build options: they have to be set before building
00064 */
00065     RTR_type_id  id;
00066     PP<RegressionTreeRegisters> train_set;
00067     static bool output_confidence_target;
00068 
00069 protected:
00070  
00071 /*
00072   Learnt options: they are sized and initialized if need be, in initLeave(...)
00073 */
00074     int  length_;
00075     real weights_sum;
00076     real targets_sum;
00077     real weighted_targets_sum;
00078     real weighted_squared_targets_sum;
00079     real loss_function_factor;
00080 
00081 public:
00082     RegressionTreeLeave();
00083     virtual              ~RegressionTreeLeave();
00084     PLEARN_DECLARE_OBJECT(RegressionTreeLeave);
00085 
00086     static  void         declareOptions(OptionList& ol);
00087     virtual void         makeDeepCopyFromShallowCopy(CopiesMap &copies);
00088     virtual void         build();
00089     virtual int          outputsize() const
00090     {return output_confidence_target?2:1;}
00091     void         initLeave(PP<RegressionTreeRegisters> the_train_set, RTR_type_id the_id, bool the_missing_leave = false);
00092     virtual void         initStats();
00093     virtual void         addRow(int row);
00094     virtual void         addRow(int row, real target, real weight);
00095     virtual void         addRow(int row, Vec outputv, Vec errorv);
00096     virtual void         addRow(int row, real target, real weight, Vec outputv, Vec errorv);
00097     virtual void         removeRow(int row, real target, real weight);
00098     virtual void         removeRow(int row, Vec outputv, Vec errorv);
00099     virtual void         removeRow(int row, real target, real weight, Vec outputv, Vec errorv);
00100     inline void          registerRow(int row)
00101     {train_set->registerLeave(id, row);}
00102     inline int           getId()const{return id;}
00103     inline int           length()const{return length_;}
00104     virtual void         getOutputAndError(Vec& output, Vec& error)const;
00105     virtual TVec<string> getOutputNames() const;
00106     virtual void         printStats();
00107     inline real          getWeightsSum(){return weights_sum;}
00108     inline real          getTargetsSum(){return targets_sum;}
00109     virtual bool         uniqTarget();
00110     virtual void         addLeave(PP<RegressionTreeLeave> leave);
00111     virtual void         removeLeave(PP<RegressionTreeLeave> leave);
00112 
00113 private:
00114     void         build_();
00115     void         verbose(string msg, int level);
00116 };
00117 
00118 DECLARE_OBJECT_PTR(RegressionTreeLeave);
00119 
00120 } // end of namespace PLearn
00121 
00122 #endif
00123 
00124 
00125 /*
00126   Local Variables:
00127   mode:c++
00128   c-basic-offset:4
00129   c-file-style:"stroustrup"
00130   c-file-offsets:((innamespace . 0)(inline-open . 0))
00131   indent-tabs-mode:nil
00132   fill-column:79
00133   End:
00134 */
00135 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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