PLearn 0.1
RegressionTree.h
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00001 // -*- C++ -*-
00002 
00003 // RegressionTree.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: RegressionTree.h, v 1.0 2004/07/19 10:00:00 Bengio/Kegl/Godbout         *
00039  * This file is part of the PLearn library.                                     *
00040  ******************************************************************************** */
00041 
00044 #ifndef RegressionTree_INC
00045 #define RegressionTree_INC
00046 
00047 #include <plearn_learners/generic/PLearner.h>
00048 #include "RegressionTreeRegisters.h"
00049 #include "RegressionTreeLeave.h"
00050 namespace PLearn {
00051 using namespace std;
00052 class RegressionTreeQueue;
00053 class RegressionTreeNode;
00054 
00055 class RegressionTree: public PLearner
00056 {
00057     friend class RegressionTreeNode;
00058     typedef PLearner inherited;
00059   
00060 private:
00061 
00062 /*
00063   Build options: they have to be set before training
00064 */
00065 
00066     bool missing_is_valid;
00067     real loss_function_weight;
00068     int maximum_number_of_nodes;
00069     int compute_train_stats;   
00070     real complexity_penalty_factor;
00071     Vec multiclass_outputs;
00072     PP<RegressionTreeLeave> leave_template;    
00073     PP<RegressionTreeRegisters> sorted_train_set;
00074     static bool output_confidence_target; //to reload old computer
00075 /*
00076   Learnt options: they are sized and initialized if need be, at stage 0
00077 */
00078 
00079     PP<RegressionTreeNode> root;
00080     PP<RegressionTreeLeave> first_leave;
00081     PP<RegressionTreeQueue> priority_queue;
00082  
00083 /*
00084   Work fields: they are sized and initialized if need be, at buid time
00085 */  
00086  
00087     int length;
00088     real l2_loss_function_factor;
00089     real l1_loss_function_factor;
00090     TVec<int> split_cols;
00091     Vec       split_values;
00092     TVec<PP<RegressionTreeNode> > *nodes;
00093 
00094     mutable Vec tmp_vec;
00095     mutable Vec tmp_computeCostsFromOutput;
00096 
00097 public:
00098     RegressionTree();
00099     virtual              ~RegressionTree();
00100     
00101     PLEARN_DECLARE_OBJECT(RegressionTree);
00102 
00103     static  void         declareOptions(OptionList& ol);
00104     virtual void         makeDeepCopyFromShallowCopy(CopiesMap &copies);
00105     virtual void         build();
00106     virtual void         train();
00107     virtual void         finalize();
00108     virtual void         forget();
00109     virtual int          outputsize() const {return leave_template->outputsize();}
00110     virtual TVec<string> getTrainCostNames() const;
00111     virtual TVec<string> getTestCostNames() const;
00112     virtual TVec<string> getOutputNames() const;
00113     PP<RegressionTreeRegisters> getSortedTrainingSet() const;
00114     virtual void         computeOutput(const Vec& input, Vec& output) const;
00115     virtual void         computeOutputAndCosts(const Vec& input,
00116                                                const Vec& target,
00117                                                Vec& output, Vec& costs) const;
00118     virtual void         computeOutputAndNodes(const Vec& input, Vec& output,
00119                                                TVec<PP<RegressionTreeNode> >* nodes=0) const;
00120     virtual void         computeCostsFromOutputs(const Vec& input, const Vec& output, const Vec& target, Vec& costs) const;
00121     virtual void         computeCostsFromOutputsAndNodes(const Vec& input,
00122                                                          const Vec& output, 
00123                                                          const Vec& target,
00124                                                          const TVec<PP<RegressionTreeNode> >& nodes,
00125                                                          Vec& costs) const;
00126 private:
00127     void                   build_();
00128     void                   initialiseTree();
00129     PP<RegressionTreeNode> expandTree();
00130     void                   verbose(string msg, int level);
00131 };
00132 
00133 DECLARE_OBJECT_PTR(RegressionTree);
00134 
00135 } // end of namespace PLearn
00136 
00137 #endif
00138 
00139 
00140 /*
00141   Local Variables:
00142   mode:c++
00143   c-basic-offset:4
00144   c-file-style:"stroustrup"
00145   c-file-offsets:((innamespace . 0)(inline-open . 0))
00146   indent-tabs-mode:nil
00147   fill-column:79
00148   End:
00149 */
00150 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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