TestInstanceResult.h

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/*
 GRT MIT License
 Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software
 and associated documentation files (the "Software"), to deal in the Software without restriction,
 including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
 and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
 subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial
 portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
 LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
 WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
 SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */

#ifndef GRT_TEST_INSTANCE_RESULT_HEADER
#define GRT_TEST_INSTANCE_RESULT_HEADER

#include "GRTTypedefs.h"

GRT_BEGIN_NAMESPACE

class TestInstanceResult{
public:
    TestInstanceResult(){
  testMode = CLASSIFICATION_MODE;
  testIteration = 0;
  classLabel = 0;
  predictedClassLabel = 0;
  unProcessedPredictedClassLabel = 0;
    }
    
    TestInstanceResult(const TestInstanceResult &rhs){
        *this = rhs;
    }
    
    ~TestInstanceResult(){
        
    }

    TestInstanceResult& operator=(const TestInstanceResult &rhs){
        if( this != &rhs ){
   this->testMode = rhs.testMode;
   this->testIteration = rhs.testIteration;
   this->classLabel = rhs.classLabel;
   this->predictedClassLabel = rhs.predictedClassLabel;
   this->unProcessedPredictedClassLabel = rhs.unProcessedPredictedClassLabel;
   this->classLikelihoods = rhs.classLikelihoods;
   this->classDistances = rhs.classDistances;
   this->regressionData = rhs.regressionData;
   this->targetData = rhs.targetData;
        }
        return *this;
    }
    
    bool setClassificationResult(const unsigned int testIteration,const unsigned int classLabel,const unsigned int predictedClassLabel,const unsigned int unProcessedPredictedClassLabel,const VectorFloat &classLikelihoods,const VectorFloat &classDistances){
  this->testMode = CLASSIFICATION_MODE;
  this->testIteration = testIteration;
  this->classLabel = classLabel;
  this->predictedClassLabel = predictedClassLabel;
  this->unProcessedPredictedClassLabel = unProcessedPredictedClassLabel;
  this->classLikelihoods = classLikelihoods;
  this->classDistances = classDistances;
        return true;
    }
    
    bool setRegressionResult(const unsigned int testIteration,const VectorFloat &regressionData,const VectorFloat &targetData){
        this->testMode = REGRESSION_MODE;
        this->testIteration = testIteration;
        this->regressionData = regressionData;
        this->targetData = targetData;
        return true;
    }

    unsigned int getTestMode() const{
        return testMode;
    }

    unsigned int getTestIteration() const{
        return testIteration;
    }

    unsigned int getClassLabel() const{
        return classLabel;
    }

    unsigned int getPredictedClassLabel() const{
        return predictedClassLabel;
    }

    Float getMaximumLikelihood() const{
        Float maxLikelihood = 0;
        for(size_t i=0; i<classLikelihoods.size(); i++){
            if( classLikelihoods[i] > maxLikelihood ){
                maxLikelihood = classLikelihoods[i];
            }
        }
        return maxLikelihood;
    }

    Float getSquaredError() const{
        Float sum = 0;
        if( regressionData.size() != targetData.size() ) return 0;
        for(size_t i=0; i<regressionData.size(); i++){
            sum += (regressionData[i]-targetData[i])*(regressionData[i]-targetData[i]);
        }
        return sum;
    }

    VectorFloat getClassLikelihoods() const {
        return classLikelihoods;
    }

    VectorFloat getDistances() const {
        return classDistances;
    }

protected:
 unsigned int testMode;
 unsigned int testIteration;
 unsigned int classLabel;
 unsigned int predictedClassLabel;
 unsigned int unProcessedPredictedClassLabel;
 VectorFloat classLikelihoods;
 VectorFloat classDistances;
 VectorFloat regressionData;
 VectorFloat targetData;
    
public:
    
    enum TestMode{CLASSIFICATION_MODE=0,REGRESSION_MODE};

};

GRT_END_NAMESPACE

#endif //GRT_TEST_INSTANCE_RESULT_HEADER