PLearn 0.1
NearestNeighborPredictionCost.cc
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00001 // -*- C++ -*-
00002 
00003 // NearestNeighborPredictionCost.cc
00004 //
00005 // Copyright (C) 2004 Martin Monperrus 
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: NearestNeighborPredictionCost.cc 4055 2005-09-07 17:34:43Z plearner $ 
00037  ******************************************************* */
00038 
00039 // Authors: Martin Monperrus
00040 
00044 #include "NearestNeighborPredictionCost.h"
00045 #include <plearn/var/ProjectionErrorVariable.h>
00046 #include <plearn/vmat/LocalNeighborsDifferencesVMatrix.h>
00047 #include <plearn/var/Func.h>
00048 #include <plearn/vmat/AutoVMatrix.h>
00049 #include <plearn/vmat/MemoryVMatrix.h>
00050 #include <plearn/io/load_and_save.h>
00051 
00052 namespace PLearn {
00053 //using namespace std;
00054 using namespace PLearn;
00055 
00056 
00057 NearestNeighborPredictionCost::NearestNeighborPredictionCost() : test_set(AutoVMatrix("/u/monperrm/data/amat/gauss2D_200_0p001_1.amat"))
00058     /* ### Initialize all fields to their default value */
00059 {
00060     // ...
00061 
00062     // ### You may or may not want to call build_() to finish building the object
00063     // build_();
00064 }
00065 
00066 PLEARN_IMPLEMENT_OBJECT(NearestNeighborPredictionCost, "ONE LINE DESCRIPTION", "MULTI LINE\nHELP");
00067 
00068 void NearestNeighborPredictionCost::declareOptions(OptionList& ol)
00069 {
00070     // ### Declare all of this object's options here
00071     // ### For the "flags" of each option, you should typically specify  
00072     // ### one of OptionBase::buildoption, OptionBase::learntoption or 
00073     // ### OptionBase::tuningoption. Another possible flag to be combined with
00074     // ### is OptionBase::nosave
00075 
00076     declareOption(ol, "knn", &NearestNeighborPredictionCost::knn, OptionBase::buildoption,
00077                   "Help text describing this option");
00078     declareOption(ol, "test_set", &NearestNeighborPredictionCost::test_set, OptionBase::buildoption,
00079                   "Help text describing this option");
00080     declareOption(ol, "learner_spec", &NearestNeighborPredictionCost::learner_spec, OptionBase::buildoption,
00081                   "Help text describing this option");
00082   
00083 // ### ex:
00084     // declareOption(ol, "myoption", &NearestNeighborPredictionCost::myoption, OptionBase::buildoption,
00085     //               "Help text describing this option");
00086     // ...
00087 
00088     // Now call the parent class' declareOptions
00089     inherited::declareOptions(ol);
00090 }
00091 
00092 void NearestNeighborPredictionCost::build_()
00093 {
00094     // ### This method should do the real building of the object,
00095     // ### according to set 'options', in *any* situation. 
00096     // ### Typical situations include:
00097     // ###  - Initial building of an object from a few user-specified options
00098     // ###  - Building of a "reloaded" object: i.e. from the complete set of all serialised options.
00099     // ###  - Updating or "re-building" of an object after a few "tuning" options have been modified.
00100     // ### You should assume that the parent class' build_() has already been called.
00101   
00102     PLearn::load(learner_spec,learner);
00103     learner->report_progress = false;
00104     cost.resize(knn);
00105     cost<<0;
00106     computed_outputs = new MemoryVMatrix(test_set->length(),learner->outputsize());
00107     learner->use(test_set,computed_outputs);
00108 
00109 }
00110 
00111 void NearestNeighborPredictionCost::run()
00112 {
00113 
00114     VMat targets_vmat;
00115     int l = test_set->length();
00116     int n = test_set->width();
00117     int n_dim = learner->outputsize() / test_set->width();
00118     Var targets = Var(1,n);
00119     Var prediction = Var(n_dim,n);
00120     Var proj_err = projection_error(prediction, targets, 0, n);
00121     Func projection_error_f =  Func(prediction & targets, proj_err);
00122     Vec temp(n_dim*n);
00123     Vec temp2(n);
00124 
00125     for (int j=0; j<knn; ++j)
00126     {
00127         targets_vmat = local_neighbors_differences(test_set, j+1, true);
00128         for (int i=0;i<l;++i)
00129         {
00130             computed_outputs->getRow(i,temp);
00131             targets_vmat->getRow(i,temp2);
00132             //cout<<temp2<<"/ "<<temp<<" "<<dot(temp,temp2)<<endl;
00133             //cout<<projection_error_f(temp,temp2)<<endl;
00134             cost[j]+=projection_error_f(temp,temp2);
00135         }
00136     }
00137     cost/=l;
00138     cout<<"Cost = "<<cost<<endl;
00139     cout<<min(cost)<<endl;
00140 //   printf("%.12f",cost);
00141 
00142 }
00143 
00144 
00145 // ### Nothing to add here, simply calls build_
00146 void NearestNeighborPredictionCost::build()
00147 {
00148     inherited::build();
00149     build_();
00150 }
00151 
00152 void NearestNeighborPredictionCost::makeDeepCopyFromShallowCopy(CopiesMap& copies)
00153 {
00154     inherited::makeDeepCopyFromShallowCopy(copies);
00155 
00156     // ### Call deepCopyField on all "pointer-like" fields 
00157     // ### that you wish to be deepCopied rather than 
00158     // ### shallow-copied.
00159     // ### ex:
00160     // deepCopyField(trainvec, copies);
00161 
00162     // ### Remove this line when you have fully implemented this method.
00163     PLERROR("NearestNeighborPredictionCost::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!");
00164 }
00165 
00166 } // end of namespace PLearn
00167 
00168 
00169 /*
00170   Local Variables:
00171   mode:c++
00172   c-basic-offset:4
00173   c-file-style:"stroustrup"
00174   c-file-offsets:((innamespace . 0)(inline-open . 0))
00175   indent-tabs-mode:nil
00176   fill-column:79
00177   End:
00178 */
00179 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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