PLearn 0.1
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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 :