void MDDAGClassifier::saveConfusionMatrix(const string& dataFileName, const string& shypFileName,
const string& outFileName)
{
InputData* pData = loadInputData(dataFileName, shypFileName);
if (_verbose > 0)
cout << "Loading strong hypothesis..." << flush;
// The class that loads the weak hypotheses
UnSerialization us;
// Where to put the weak hypotheses
vector<BaseLearner*> weakHypotheses;
// loads them
us.loadHypotheses(shypFileName, weakHypotheses, pData);
// where the results go
vector< ExampleResults* > results;
if (_verbose > 0)
cout << "Classifying..." << flush;
// get the results
computeResults( pData, weakHypotheses, results, (int)weakHypotheses.size() );
const int numClasses = pData->getNumClasses();
const int numExamples = pData->getNumExamples();
ofstream outFile(outFileName.c_str());
//////////////////////////////////////////////////////////////////////////
for (int l = 0; l < numClasses; ++l)
outFile << '\t' << pData->getClassMap().getNameFromIdx(l);
outFile << endl;
for (int l = 0; l < numClasses; ++l)
{
vector<int> winnerCount(numClasses, 0);
for (int i = 0; i < numExamples; ++i)
{
if ( pData->hasPositiveLabel(i,l) )
++winnerCount[ results[i]->getWinner().first ];
}
// class name
outFile << pData->getClassMap().getNameFromIdx(l);
for (int j = 0; j < numClasses; ++j)
outFile << '\t' << winnerCount[j];
outFile << endl;
}
//////////////////////////////////////////////////////////////////////////
if (_verbose > 0)
cout << "Done!" << endl;
// delete the input data file
if (pData)
delete pData;
vector<ExampleResults*>::iterator it;
for (it = results.begin(); it != results.end(); ++it)
delete (*it);
}
void MDDAGClassifier::printConfusionMatrix(const string& dataFileName, const string& shypFileName)
{
InputData* pData = loadInputData(dataFileName, shypFileName);
if (_verbose > 0)
cout << "Loading strong hypothesis..." << flush;
// The class that loads the weak hypotheses
UnSerialization us;
// Where to put the weak hypotheses
vector<BaseLearner*> weakHypotheses;
// loads them
us.loadHypotheses(shypFileName, weakHypotheses, pData);
// where the results go
vector< ExampleResults* > results;
if (_verbose > 0)
cout << "Classifying..." << flush;
// get the results
computeResults( pData, weakHypotheses, results, (int)weakHypotheses.size());
const int numClasses = pData->getNumClasses();
const int numExamples = pData->getNumExamples();
if (_verbose > 0)
cout << "Done!" << endl;
const int colSize = 7;
if (_verbose > 0)
{
cout << "Raw Confusion Matrix:\n";
cout << setw(colSize) << "Truth ";
for (int l = 0; l < numClasses; ++l)
cout << setw(colSize) << nor_utils::getAlphanumeric(l);
cout << "\nClassification\n";
for (int l = 0; l < numClasses; ++l)
{
vector<int> winnerCount(numClasses, 0);
for (int i = 0; i < numExamples; ++i)
{
if ( pData->hasPositiveLabel(i, l) )
++winnerCount[ results[i]->getWinner().first ];
}
// class
cout << setw(colSize) << " " << nor_utils::getAlphanumeric(l);
for (int j = 0; j < numClasses; ++j)
cout << setw(colSize) << winnerCount[j];
cout << endl;
}
}
cout << "\nMatrix Key:\n";
// Print the legend
for (int l = 0; l < numClasses; ++l)
cout << setw(5) << nor_utils::getAlphanumeric(l) << ": " <<
pData->getClassMap().getNameFromIdx(l) << "\n";
// delete the input data file
if (pData)
delete pData;
vector<ExampleResults*>::iterator it;
for (it = results.begin(); it != results.end(); ++it)
delete (*it);
}
