PLearn 0.1
KNNRegressor.h
Go to the documentation of this file.
00001 // -*- C++ -*-
00002 
00003 // KNNRegressor.h
00004 //
00005 // Copyright (C) 2004 Nicolas Chapados 
00006 // 
00007 // Redistribution and use in source and binary forms, with or without
00008 // modification, are permitted provided that the following conditions are met:
00009 // 
00010 //  1. Redistributions of source code must retain the above copyright
00011 //     notice, this list of conditions and the following disclaimer.
00012 // 
00013 //  2. Redistributions in binary form must reproduce the above copyright
00014 //     notice, this list of conditions and the following disclaimer in the
00015 //     documentation and/or other materials provided with the distribution.
00016 // 
00017 //  3. The name of the authors may not be used to endorse or promote
00018 //     products derived from this software without specific prior written
00019 //     permission.
00020 // 
00021 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00022 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00023 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00024 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00025 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00026 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00027 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00028 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00029 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00030 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00031 // 
00032 // This file is part of the PLearn library. For more information on the PLearn
00033 // library, go to the PLearn Web site at www.plearn.org
00034 
00035 /* *******************************************************      
00036  * $Id: KNNRegressor.h 3994 2005-08-25 13:35:03Z chapados $ 
00037  ******************************************************* */
00038 
00039 // Authors: Nicolas Chapados
00040 
00044 #ifndef KNNRegressor_INC
00045 #define KNNRegressor_INC
00046 
00047 #include <plearn_learners/generic/PLearner.h>
00048 #include <plearn_learners/nearest_neighbors/GenericNearestNeighbors.h>
00049 
00050 namespace PLearn {
00051 
00086 class KNNRegressor: public PLearner
00087 {
00088     typedef PLearner inherited;
00089 
00090 protected:
00092     mutable Vec knn_output;
00093 
00095     mutable Vec knn_costs;
00096   
00097 public:
00098     //#####  Public Build Options  ############################################
00099 
00102     PP<GenericNearestNeighbors> knn;
00103 
00105     int kmin;
00106 
00109     real kmult;
00110 
00113     real kpow;
00114 
00117     bool use_knn_costs_as_weights;
00118 
00123     Ker kernel;
00124 
00127     PP<PLearner> local_model;
00128 
00129 public:
00130     //#####  Object Methods  ##################################################
00131 
00133     KNNRegressor();
00134 
00136     virtual void build();
00137 
00139     virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies);
00140 
00141     // Declares other standard object methods.
00142     // If your class is not instantiatable (it has pure virtual methods)
00143     // you should replace this by PLEARN_DECLARE_ABSTRACT_OBJECT.
00144     PLEARN_DECLARE_OBJECT(KNNRegressor);
00145 
00146 public:
00147     //#####  PLearner Methods  ####################################################
00148 
00150     virtual void setTrainingSet(VMat training_set, bool call_forget=true);
00151 
00154     virtual int outputsize() const;
00155 
00157     virtual void forget();
00158     
00160     virtual void train();
00161 
00163     virtual void computeOutput(const Vec& input, Vec& output) const;
00164 
00166     virtual void computeCostsFromOutputs(const Vec& input, const Vec& output, 
00167                                          const Vec& target, Vec& costs) const;
00168 
00172     virtual
00173     bool computeConfidenceFromOutput(const Vec& input, const Vec& output,
00174                                      real probability,
00175                                      TVec< pair<real,real> >& intervals) const;
00176   
00179     virtual TVec<std::string> getTestCostNames() const;
00180 
00183     virtual TVec<std::string> getTrainCostNames() const;
00184 
00185 private: 
00187     void build_();
00188 
00189 protected: 
00191     static void declareOptions(OptionList& ol);
00192 };
00193 
00194 // Declares a few other classes and functions related to this class.
00195 DECLARE_OBJECT_PTR(KNNRegressor);
00196   
00197 } // end of namespace PLearn
00198 
00199 #endif
00200 
00201 
00202 /*
00203   Local Variables:
00204   mode:c++
00205   c-basic-offset:4
00206   c-file-style:"stroustrup"
00207   c-file-offsets:((innamespace . 0)(inline-open . 0))
00208   indent-tabs-mode:nil
00209   fill-column:79
00210   End:
00211 */
00212 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Defines