/// Solve pressure equation, by Newton iterations.
void CompressibleTpfa::solve(const double dt,
BlackoilState& state,
WellState& well_state)
{
const int nc = grid_.number_of_cells;
const int nw = (wells_ != 0) ? wells_->number_of_wells : 0;
// Set up dynamic data.
computePerSolveDynamicData(dt, state, well_state);
computePerIterationDynamicData(dt, state, well_state);
// Assemble J and F.
assemble(dt, state, well_state);
double inc_norm = 0.0;
int iter = 0;
double res_norm = residualNorm();
std::cout << "\nIteration Residual Change in p\n"
<< std::setw(9) << iter
<< std::setw(18) << res_norm
<< std::setw(18) << '*' << std::endl;
while ((iter < maxiter_) && (res_norm > residual_tol_)) {
// Solve for increment in Newton method:
// incr = x_{n+1} - x_{n} = -J^{-1}F
// (J is Jacobian matrix, F is residual)
solveIncrement();
++iter;
// Update pressure vars with increment.
for (int c = 0; c < nc; ++c) {
state.pressure()[c] += pressure_increment_[c];
}
for (int w = 0; w < nw; ++w) {
well_state.bhp()[w] += pressure_increment_[nc + w];
}
// Stop iterating if increment is small.
inc_norm = incrementNorm();
if (inc_norm <= change_tol_) {
std::cout << std::setw(9) << iter
<< std::setw(18) << '*'
<< std::setw(18) << inc_norm << std::endl;
break;
}
// Set up dynamic data.
computePerIterationDynamicData(dt, state, well_state);
// Assemble J and F.
assemble(dt, state, well_state);
// Update residual norm.
res_norm = residualNorm();
std::cout << std::setw(9) << iter
<< std::setw(18) << res_norm
<< std::setw(18) << inc_norm << std::endl;
}
if ((iter == maxiter_) && (res_norm > residual_tol_) && (inc_norm > change_tol_)) {
OPM_THROW(std::runtime_error, "CompressibleTpfa::solve() failed to converge in " << maxiter_ << " iterations.");
}
std::cout << "Solved pressure in " << iter << " iterations." << std::endl;
// Compute fluxes and face pressures.
computeResults(state, well_state);
}
void MDDAGClassifier::run(const string& dataFileName, const string& shypFileName,
int numIterations, const string& outResFileName, int numRanksEnclosed)
{
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, numIterations );
const int numClasses = pData->getNumClasses();
if (_verbose > 0)
{
// well.. if verbose = 0 no results are displayed! :)
cout << "Done!" << endl;
vector< vector<float> > rankedError(numRanksEnclosed);
// Get the per-class error for the numRanksEnclosed-th ranks
for (int i = 0; i < numRanksEnclosed; ++i)
getClassError( pData, results, rankedError[i], i );
// output it
cout << endl;
cout << "Error Summary" << endl;
cout << "=============" << endl;
for ( int l = 0; l < numClasses; ++l )
{
// first rank (winner): rankedError[0]
cout << "Class '" << pData->getClassMap().getNameFromIdx(l) << "': "
<< setprecision(4) << rankedError[0][l] * 100 << "%";
// output the others on its side
if (numRanksEnclosed > 1 && _verbose > 1)
{
cout << " (";
for (int i = 1; i < numRanksEnclosed; ++i)
cout << " " << i+1 << ":[" << setprecision(4) << rankedError[i][l] * 100 << "%]";
cout << " )";
}
cout << endl;
}
// the overall error
cout << "\n--> Overall Error: "
<< setprecision(4) << getOverallError(pData, results, 0) * 100 << "%";
// output the others on its side
if (numRanksEnclosed > 1 && _verbose > 1)
{
cout << " (";
for (int i = 1; i < numRanksEnclosed; ++i)
cout << " " << i+1 << ":[" << setprecision(4) << getOverallError(pData, results, i) * 100 << "%]";
cout << " )";
}
cout << endl;
} // verbose
// If asked output the results
if ( !outResFileName.empty() )
{
const int numExamples = pData->getNumExamples();
ofstream outRes(outResFileName.c_str());
outRes << "Instance" << '\t' << "Forecast" << '\t' << "Labels" << '\n';
string exampleName;
for (int i = 0; i < numExamples; ++i)
{
// output the name if it exists, otherwise the number
// of the example
exampleName = pData->getExampleName(i);
if ( exampleName.empty() )
outRes << i << '\t';
else
outRes << exampleName << '\t';
// output the predicted class
outRes << pData->getClassMap().getNameFromIdx( results[i]->getWinner().first ) << '\t';
outRes << '|';
vector<Label>& labels = pData->getLabels(i);
for (vector<Label>::iterator lIt=labels.begin(); lIt != labels.end(); ++lIt) {
if (lIt->y>0)
{
outRes << ' ' << pData->getClassMap().getNameFromIdx(lIt->idx);
}
}
outRes << endl;
}
if (_verbose > 0)
cout << "\nPredictions written on file <" << outResFileName << ">!" << 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::saveCalibratedPosteriors(const string& dataFileName, const string& shypFileName,
const string& outFileName, int numIterations)
{
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, numIterations );
const int numClasses = pData->getNumClasses();
const int numExamples = pData->getNumExamples();
ofstream outFile(outFileName.c_str());
string exampleName;
if (_verbose > 0)
cout << "Output posteriors..." << flush;
for (int i = 0; i < numExamples; ++i)
{
// output the name if it exists, otherwise the number
// of the example
exampleName = pData->getExampleName(i);
if ( !exampleName.empty() )
outFile << exampleName << ',';
// output the posteriors
outFile << results[i]->getVotesVector()[0];
for (int l = 1; l < numClasses; ++l)
outFile << ',' << results[i]->getVotesVector()[l];
outFile << '\n';
}
if (_verbose > 0)
cout << "Done!" << endl;
if (_verbose > 1)
{
cout << "\nClass order (You can change it in the header of the data file):" << endl;
for (int l = 0; l < numClasses; ++l)
cout << "- " << pData->getClassMap().getNameFromIdx(l) << 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::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);
}
void AdaBoostMHClassifier::saveROC(const string& dataFileName, const string& shypFileName,
const string& outFileName, int numIterations)
{
InputData* pData = loadInputData(dataFileName, shypFileName);
ofstream outFile(outFileName.c_str());
if ( ! outFile.is_open() )
{
cout << "Cannot open outfile" << endl;
exit( -1 );
}
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);
weakHypotheses.resize( numIterations );
// where the results go
vector< ExampleResults* > results;
if (_verbose > 0)
cout << "Classifying..." << flush;
// get the results
computeResults( pData, weakHypotheses, results, weakHypotheses.size());
const int numClasses = pData->getNumClasses();
const int numExamples = pData->getNumExamples();
if (_verbose > 0)
cout << "Done!" << endl;
vector< pair< int, double> > sortedExample( numExamples );
for( int i=0; i<numExamples; i++ )
{
sortedExample[i].first = i;
sortedExample[i].second = results[i]->getVotesVector()[0];
}
sort( sortedExample.begin(), sortedExample.end(), nor_utils::comparePair< 2, int, double, greater<double> >() );
vector<double> positiveWeights( numExamples );
double sumOfPositiveWeights = 0.0;
vector<double> negativeWeights( numExamples );
double sumOfNegativeWeights = 0.0;
fill( positiveWeights.begin(), positiveWeights.end(), 0.0 );
fill( negativeWeights.begin(), negativeWeights.end(), 0.0 );
string className = pData->getClassMap().getNameFromIdx( 0 );
vector<Label>& labels = pData->getLabels( sortedExample[0].first );
vector<Label>::iterator labIt = find( labels.begin(), labels.end(), 0);
if ( labIt != labels.end() )
{
if ( labIt->y > 0.0 )
{
positiveWeights[0] = labIt->initialWeight;
sumOfPositiveWeights += labIt->initialWeight;
} else
{
negativeWeights[0] = labIt->initialWeight;
sumOfNegativeWeights += labIt->initialWeight;
}
}
for( int i=1; i<numExamples; i++ )
{
labels = pData->getLabels( sortedExample[i].first );
labIt = find( labels.begin(), labels.end(), 0);
if ( labIt != labels.end() )
{
if ( labIt->y > 0.0 )
{
negativeWeights[i] = negativeWeights[i-1];
positiveWeights[i] = positiveWeights[i-1] + labIt->initialWeight;
sumOfPositiveWeights += labIt->initialWeight;
} else
{
positiveWeights[i] = positiveWeights[i-1];
negativeWeights[i] = negativeWeights[i-1] + labIt->initialWeight;
sumOfNegativeWeights += labIt->initialWeight;
}
} else {
positiveWeights[i] = positiveWeights[i-1];
negativeWeights[i] = negativeWeights[i-1];
}
}
outFile << "Class name: " << className << endl;
for( int i=0; i<numExamples; i++ )
{
outFile << sortedExample[i].first << " ";
// false positive rate
outFile << ( positiveWeights[i] / sumOfPositiveWeights ) << " ";
//true negative rate
outFile << ( negativeWeights[i] / sumOfNegativeWeights ) << endl;
}
outFile.close();
// delete the input data file
if (pData)
delete pData;
vector<ExampleResults*>::iterator it;
for (it = results.begin(); it != results.end(); ++it)
delete (*it);
}