PLearn 0.1
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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 :