PLearn 0.1
TransformOutputLearner.cc
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00001 // -*- C++ -*-
00002 
00003 // plearn_learners/generic/TransformOutputLearner.cc
00004 //
00005 // Copyright (C) 2007 Frederic Bastien
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 // Authors: Frederic Bastien
00036 
00040 #include "plearn_learners/generic/TransformOutputLearner.h"
00041 
00042 namespace PLearn {
00043 using namespace std;
00044 
00045 PLEARN_IMPLEMENT_OBJECT(
00046     TransformOutputLearner,
00047     "Transform a Learner who give the log probality as output to give the probability as output",
00048     "MULTI-LINE \nHELP");
00049 
00050 TransformOutputLearner::TransformOutputLearner()
00051     :inherited(),
00052      output_function(-1),
00053      warning0(true),
00054      warning1(true)
00055 {
00056     forward_test=true;
00057 }
00058 
00059 void TransformOutputLearner::declareOptions(OptionList& ol)
00060 {
00061     // ### Declare all of this object's options here.
00062     // ### For the "flags" of each option, you should typically specify
00063     // ### one of OptionBase::buildoption, OptionBase::learntoption or
00064     // ### OptionBase::tuningoption. If you don't provide one of these three,
00065     // ### this option will be ignored when loading values from a script.
00066     // ### You can also combine flags, for example with OptionBase::nosave:
00067     // ### (OptionBase::buildoption | OptionBase::nosave)
00068 
00069     // ### ex:
00070     // declareOption(ol, "myoption", &TransformOutputLearner::myoption,
00071     //               OptionBase::buildoption,
00072     //               "Help text describing this option");
00073     // ...
00074     declareOption(ol, "output_function", &TransformOutputLearner::output_function,
00075                   OptionBase::buildoption,
00076                   "The operation to do on the output\n"
00077                   "0: We transform the sublearner log probability output to probability output\n"
00078                   "1: We transform the sublearner probability output of class to class output\n"
00079                   "2: We transform the sublearner regression output to class output.");
00080 
00081     // Now call the parent class' declareOptions
00082     inherited::declareOptions(ol);
00083 }
00084 
00085 void TransformOutputLearner::build_()
00086 {
00087     tmp_output2.resize(learner_->outputsize());
00088 }
00089 
00090 // ### Nothing to add here, simply calls build_
00091 void TransformOutputLearner::build()
00092 {
00093     inherited::build();
00094     build_();
00095 }
00096 
00097 void TransformOutputLearner::computeOutput(const Vec& input, Vec& output) const
00098 {
00099     // Compute the output from the input.
00100     // int nout = outputsize();
00101     // output.resize(nout);
00102     // ...
00103     if(output_function==0){//logprob to prob
00104         learner_->computeOutput(input,output);
00105         exp(output,output);
00106     }else if(output_function==1){//logprob or prob to class
00107         learner_->computeOutput(input,tmp_output2);
00108         output[0]=argmax(tmp_output2);
00109     }else if(output_function==2){//Regression to class v1
00110         learner_->computeOutput(input,tmp_output2);
00111         output[0]=int(round(tmp_output2[0]));
00112 //    }else if(output_function==2){//Regression to class v2
00113 //         learner_->computeOutput(input, output);
00114 //         if (multiclass_outputs.length() <= 0) return;
00115 //         real closest_value=multiclass_outputs[0];
00116 //         real margin_to_closest_value=abs(output[0] - multiclass_outputs[0]);
00117 //         for (int value_ind = 1; value_ind < multiclass_outputs.length(); value_ind++)
00118 //         {
00119 //             real v=abs(output[0] - multiclass_outputs[value_ind]);
00120 //             if (v < margin_to_closest_value)
00121 //             {
00122 //                 closest_value = multiclass_outputs[value_ind];
00123 //                 margin_to_closest_value = v;
00124 //             }
00125 //         }
00126 //         output[0] = closest_value;
00127     }else
00128         PLERROR("In TransformOutputLearner::computeOutput - unknow output_function %d",output_function);
00129 }
00130 
00131 void TransformOutputLearner::computeCostsFromOutputs(const Vec& input, const Vec& output,
00132                                            const Vec& target, Vec& costs) const
00133 {
00134     PLASSERT( learner_ );
00135     if(output_function==0){
00136         if(warning0){
00137             PLWARNING("In TransformOutputLearner::computeCostsFromOutputs - you are loosing precision");
00138             warning0=false;
00139         }
00140         compute_log(output,tmp_output2);
00141         learner_->computeCostsFromOutputs(input,tmp_output2,target,costs);
00142     }else if(output_function==1 || output_function==2){
00143         if(warning1){
00144             PLWARNING("In TransformOutputLearner::computeCostsFromOutputs - we can't compute the costs from\n"
00145                       "outputs with output_function %d. We use TransformOutputLearner::computeOutputAndCosts.",output_function);
00146             warning1=false;
00147         }
00148         computeOutputAndCosts(input,target,tmp_output2,costs);
00149     }else
00150         PLERROR("In TransformOutputLearner::computeCostsFromOutputs - unknow output_function %d",output_function);
00151 }
00152 void TransformOutputLearner::computeOutputAndCosts(const Vec& input,  const Vec& target,
00153                                           Vec& output, Vec& costs) const
00154 {
00155     PLASSERT( learner_ );
00156     if(output_function==0){//logprob to prob
00157         learner_->computeOutputAndCosts(input,target,output,costs);
00158         exp(output,output);
00159     }else if(output_function==1){//logprob or prob to class
00160         learner_->computeOutputAndCosts(input,target,tmp_output2,costs);
00161         output[0]=argmax(output);
00162     }else if(output_function==2){//Regression to class v1
00163         learner_->computeOutputAndCosts(input,target,tmp_output2,costs);
00164         output[0]=int(round(tmp_output2[0]));
00165     }else
00166         PLERROR("In TransformOutputLearner::computeOutputAndCosts - unknow output_function %d",output_function);
00167 }
00168 
00169 void TransformOutputLearner::test(VMat testset, PP<VecStatsCollector> test_stats,
00170                     VMat testoutputs, VMat testcosts) const
00171 {
00172     PLASSERT( learner_ );
00173     if(output_function==0){
00174         learner_->test(testset, test_stats, testoutputs, testcosts);
00175         for(int i = 0;i<testoutputs.length();i++){
00176             Vec v = testoutputs(i);
00177             exp(v,v);
00178         }
00179     }
00180     else
00181         PLERROR("In TransformOutputLearner::test");
00182 }
00183 int TransformOutputLearner::outputsize() const
00184 {
00185     PLASSERT( learner_ );
00186     if(output_function==0)
00187         return learner_->outputsize();
00188     else if (output_function==1 || output_function==2)
00189         return 1;
00190     else
00191         PLERROR("In TransformOutputLearner::outputsize");
00192     return -1;
00193 }
00194 
00195 } // end of namespace PLearn
00196 
00197 
00198 /*
00199   Local Variables:
00200   mode:c++
00201   c-basic-offset:4
00202   c-file-style:"stroustrup"
00203   c-file-offsets:((innamespace . 0)(inline-open . 0))
00204   indent-tabs-mode:nil
00205   fill-column:79
00206   End:
00207 */
00208 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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