本文整理汇总了C++中UnSerialization类的典型用法代码示例。如果您正苦于以下问题:C++ UnSerialization类的具体用法?C++ UnSerialization怎么用?C++ UnSerialization使用的例子?那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了UnSerialization类的9个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: _verbose
// -----------------------------------------------------------------------
// -----------------------------------------------------------------------
DataReader::DataReader(const nor_utils::Args& args, int verbose) : _verbose(verbose), _args(args)
{
string mdpTrainFileName = _args.getValue<string>("traintestmdp", 0);
string testFileName = _args.getValue<string>("traintestmdp", 1);
string shypFileName = _args.getValue<string>("traintestmdp", 3);
_numIterations = _args.getValue<int>("traintestmdp", 2);
string tmpFname = _args.getValue<string>("traintestmdp", 4);
if (_verbose > 0)
cout << "Loading arff data for MDP learning..." << flush;
// load the arff
loadInputData(mdpTrainFileName, testFileName, shypFileName);
if (_verbose > 0)
cout << "Done." << endl << flush;
if (_verbose > 0)
cout << "Loading strong hypothesis..." << flush;
// The class that loads the weak hypotheses
UnSerialization us;
// loads them
us.loadHypotheses(shypFileName, _weakHypotheses, _pTrainData);
if (_numIterations<_weakHypotheses.size())
_weakHypotheses.resize(_numIterations);
if (_verbose > 0)
cout << "Done." << endl << flush;
assert( _weakHypotheses.size() >= _numIterations );
// calculate the sum of alphas
vector<BaseLearner*>::iterator it;
_sumAlphas=0.0;
for( it = _weakHypotheses.begin(); it != _weakHypotheses.end(); ++it )
{
BaseLearner* currBLearner = *it;
_sumAlphas += currBLearner->getAlpha();
}
}
示例2: loadInputData
// -----------------------------------------------------------------------
// -----------------------------------------------------------------------
void AdaBoostMDPClassifier::init()
{
string mdpTrainFileName = _args.getValue<string>("traintestmdp", 0);
string testFileName = _args.getValue<string>("traintestmdp", 1);
string shypFileName = _args.getValue<string>("traintestmdp", 3);
_numIterations = _args.getValue<int>("traintestmdp", 2);
string tmpFname = _args.getValue<string>("traintestmdp", 4);
_outputStream.open( tmpFname.c_str() );
if (_verbose > 0)
cout << "Loading arff data for MDP learning..." << flush;
// load the arff
loadInputData(mdpTrainFileName, testFileName, shypFileName);
if (_verbose > 0)
cout << "Done." << endl << flush;
if (_verbose > 0)
cout << "Loading strong hypothesis..." << flush;
// The class that loads the weak hypotheses
UnSerialization us;
// loads them
us.loadHypotheses(shypFileName, _weakHypotheses, _pData);
_weakHypotheses.resize(_numIterations);
if (_verbose > 0)
cout << "Done." << endl << flush;
assert( _weakHypotheses.size() >= _numIterations );
if (_verbose > 0)
cout << "Allocating grid world..." << flush;
createGridWorld();
if (_verbose > 0)
cout << "Done." << endl << flush;
}
示例3: resumeWeakLearners
int FilterBoostLearner::resumeWeakLearners(InputData* pTrainingData)
{
if (_resumeShypFileName.empty())
return 0;
if (_verbose > 0)
cout << "Reloading strong hypothesis file <" << _resumeShypFileName << ">.." << flush;
// The class that loads the weak hypotheses
UnSerialization us;
// loads them
us.loadHypotheses(_resumeShypFileName, _foundHypotheses, pTrainingData, _verbose);
if (_verbose > 0)
cout << "Done!" << endl;
// return the number of iterations found
return static_cast<int>( _foundHypotheses.size() );
}
示例4: load
void ParasiteLearner::load(nor_utils::StreamTokenizer& st)
{
// cout << "Sorry, you can't load a ParasiteLearner" << endl << flush;
// exit(1);
// Calling the super-class method
BaseLearner::load(st);
_signOfAlpha = UnSerialization::seekAndParseEnclosedValue<int>(st, "alphasign");
_nameBaseLearnerFile = UnSerialization::seekAndParseEnclosedValue<string>(st, "poolfile");
_selectedIdx = UnSerialization::seekAndParseEnclosedValue<int>(st, "learneridx");
if (_baseLearners.size() == 0) {
// load the base learners
if (_verbose >= 2)
cout << "loading " << _nameBaseLearnerFile << ".." << flush;
UnSerialization us;
us.loadHypotheses( _nameBaseLearnerFile, _baseLearners, _pTrainingData, _verbose);
if (_verbose >= 2)
cout << "finished " << endl << flush;
}
}
示例5: loadInputData
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';
//.........这里部分代码省略.........
示例6: loadInputData
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;
//.........这里部分代码省略.........
示例7: run
float ParasiteLearner::run()
{
if (_baseLearners.size() == 0) {
// load the base learners
if (_verbose >= 2)
cout << "loading " << _nameBaseLearnerFile << ".." << flush;
UnSerialization us;
us.loadHypotheses( _nameBaseLearnerFile, _baseLearners, _pTrainingData, _verbose);
if (_verbose >= 2)
cout << "finished " << endl << flush;
}
if ( _numBaseLearners == -1 || _numBaseLearners > _baseLearners.size())
_numBaseLearners = _baseLearners.size();
const int numClasses = _pTrainingData->getNumClasses();
const int numExamples = _pTrainingData->getNumExamples();
float tmpAlpha;
float bestE = numeric_limits<float>::max();
float sumGamma, bestSumGamma = -numeric_limits<float>::max();
float tmpE, gamma;
float eps_min,eps_pls;
int tmpSignOfAlpha;
// This is the bottleneck, squeeze out every microsecond
if (_closed) {
bestSumGamma = 0;
if ( nor_utils::is_zero(_theta) ) {
for (int j = 0; j < _numBaseLearners; ++j) {
sumGamma = 0;
for (int i = 0; i < numExamples; ++i) {
vector<Label> labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l)
sumGamma += labels[l].weight *
_baseLearners[j]->classify(_pTrainingData,i,l) * labels[l].y;
}
if (fabs(sumGamma) > fabs(bestSumGamma)) {
_selectedIdx = j;
bestSumGamma = sumGamma;
}
}
eps_pls = eps_min = 0;
for (int i = 0; i < numExamples; ++i) {
vector<Label> labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l) {
gamma = _baseLearners[_selectedIdx]->classify(_pTrainingData,i,l) *
labels[l].y;
if ( gamma > 0 )
eps_pls += labels[l].weight;
else if ( gamma < 0 )
eps_min += labels[l].weight;
}
}
if (eps_min > eps_pls) {
float tmpSwap = eps_min;
eps_min = eps_pls;
eps_pls = tmpSwap;
_signOfAlpha = -1;
}
_alpha = getAlpha(eps_min, eps_pls);
bestE = BaseLearner::getEnergy( eps_min, eps_pls );
}
else {
for (int j = 0; j < _numBaseLearners; ++j) {
eps_pls = eps_min = 0;
for (int i = 0; i < numExamples; ++i) {
vector<Label> labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l) {
gamma = _baseLearners[j]->classify(_pTrainingData,i,l) * labels[l].y;
if ( gamma > 0 )
eps_pls += labels[l].weight;
else if ( gamma < 0 )
eps_min += labels[l].weight;
}
}
if (eps_min > eps_pls) {
float tmpSwap = eps_min;
eps_min = eps_pls;
eps_pls = tmpSwap;
tmpSignOfAlpha = -1;
}
else
tmpSignOfAlpha = 1;
tmpAlpha = getAlpha(eps_min, eps_pls, _theta);
tmpE = BaseLearner::getEnergy( eps_min, eps_pls, tmpAlpha, _theta );
if (tmpE < bestE && eps_pls > eps_min + _theta) {
_alpha = tmpAlpha;
_selectedIdx = j;
_signOfAlpha = tmpSignOfAlpha;
bestE = tmpE;
}
}
}
}
else {
if ( nor_utils::is_zero(_theta) ) {
for (int j = 0; j < _numBaseLearners; ++j) {
sumGamma = 0;
for (int i = 0; i < numExamples; ++i) {
//.........这里部分代码省略.........
示例8: loadInputData
void VJCascadeClassifier::run(const string& dataFileName, const string& shypFileName,
int numIterations, const string& outResFileName )
{
// loading data
InputData* pData = loadInputData(dataFileName, shypFileName);
const int numOfExamples = pData->getNumExamples();
//get the index of positive label
const NameMap& namemap = pData->getClassMap();
_positiveLabelIndex = namemap.getIdxFromName( _positiveLabelName );
if (_verbose > 0)
cout << "Loading strong hypothesis..." << flush;
// The class that loads the weak hypotheses
UnSerialization us;
// Where to put the weak hypotheses
vector<vector<BaseLearner*> > weakHypotheses;
// For stagewise thresholds
vector<AlphaReal> thresholds(0);
// loads them
//us.loadHypotheses(shypFileName, weakHypotheses, pData);
us.loadCascadeHypotheses(shypFileName, weakHypotheses, thresholds, pData);
// store result
vector<CascadeOutputInformation> cascadeData(0);
vector<CascadeOutputInformation>::iterator it;
cascadeData.resize(numOfExamples);
for( it=cascadeData.begin(); it != cascadeData.end(); ++it )
{
it->active=true;
}
if (!_outputInfoFile.empty())
{
outputHeader();
}
for(int stagei=0; stagei < weakHypotheses.size(); ++stagei )
{
// for posteriors
vector<AlphaReal> posteriors(0);
// calculate the posteriors after stage
VJCascadeLearner::calculatePosteriors( pData, weakHypotheses[stagei], posteriors, _positiveLabelIndex );
// update the data (posteriors, active element index etc.)
updateCascadeData(pData, weakHypotheses, stagei, posteriors, thresholds, _positiveLabelIndex, cascadeData);
if (!_outputInfoFile.empty())
{
_output << stagei + 1 << "\t";
_output << weakHypotheses[stagei].size() << "\t";
outputCascadeResult( pData, cascadeData );
}
int numberOfActiveInstance = 0;
for( int i = 0; i < numOfExamples; ++i )
if (cascadeData[i].active) numberOfActiveInstance++;
if (_verbose > 0 )
cout << "Number of active instances: " << numberOfActiveInstance << "(" << numOfExamples << ")" << endl;
}
vector<vector<int> > confMatrix(2);
confMatrix[0].resize(2);
fill( confMatrix[0].begin(), confMatrix[0].end(), 0 );
confMatrix[1].resize(2);
fill( confMatrix[1].begin(), confMatrix[1].end(), 0 );
// print accuracy
for(int i=0; i<numOfExamples; ++i )
{
vector<Label>& labels = pData->getLabels(i);
if (labels[_positiveLabelIndex].y>0) // pos label
if (cascadeData[i].forecast==1)
confMatrix[1][1]++;
else
confMatrix[1][0]++;
else // negative label
if (cascadeData[i].forecast==0)
confMatrix[0][0]++;
else
confMatrix[0][1]++;
}
double acc = 100.0 * (confMatrix[0][0] + confMatrix[1][1]) / ((double) numOfExamples);
// output it
cout << endl;
cout << "Error Summary" << endl;
cout << "=============" << endl;
cout << "Accuracy: " << setprecision(4) << acc << endl;
//.........这里部分代码省略.........
示例9: alpha
//.........这里部分代码省略.........
_positiveLabelIndex = namemap.getIdxFromName(_positiveLabelName);
// FIXME: output posteriors
// OutputInfo* pTrainPosteriorsOut = NULL;
// OutputInfo* pTestPosteriorsOut = NULL;
// if (! _trainPosteriorsFileName.empty()) {
// pTrainPosteriorsOut = new OutputInfo(_trainPosteriorsFileName, "pos", true);
// pTrainPosteriorsOut->initialize(pTrainingData);
// dynamic_cast<PosteriorsOutput*>( pTrainPosteriorsOut->getOutputInfoObject("pos") )->addClassIndex(_positiveLabelIndex );
// }
// if (! _testPosteriorsFileName.empty() && !_testFileName.empty() ) {
// pTestPosteriorsOut = new OutputInfo(_testPosteriorsFileName, "pos", true);
// pTestPosteriorsOut->initialize(pTestData);
// dynamic_cast<PosteriorsOutput*>( pTestPosteriorsOut->getOutputInfoObject("pos") )->addClassIndex(_positiveLabelIndex );
// }
const int numExamples = pTrainingData->getNumExamples();
vector<BaseLearner*> inWeakHypotheses;
if (_fullRun) {
// TODO : the full training is implementet, testing is needed
AdaBoostMHLearner* sHypothesis = new AdaBoostMHLearner();
sHypothesis->run(args, pTrainingData, _baseLearnerName, _numIterations, inWeakHypotheses );
delete sHypothesis;
}
else {
cout << "[+] Loading uncalibrated shyp file... ";
//read the shyp file of the trained classifier
UnSerialization us;
us.loadHypotheses(_unCalibratedShypFileName, inWeakHypotheses, pTrainingData);
if (_inShypLimit > 0 && _inShypLimit < inWeakHypotheses.size() ) {
inWeakHypotheses.resize(_inShypLimit);
}
if (_numIterations > inWeakHypotheses.size()) {
_numIterations = inWeakHypotheses.size();
}
cout << "weak hypotheses loaded, " << inWeakHypotheses.size() << " retained.\n";
}
// some initializations
_foundHypotheses.resize(0);
double faceRejectionFraction = 0.;
double estimatedExecutionTime = 0.;
vector<double> rejectionDistributionVector;
_rejectionThresholds.resize(0);
set<int> trainingIndices;
for (int i = 0; i < numExamples; i++) {
trainingIndices.insert(pTrainingData->getRawIndex(i) );
}
// init v_t (see the paper)
initializeRejectionDistributionVector(_numIterations, rejectionDistributionVector);
if (_verbose == 1)
cout << "Learning in progress..." << endl;
///////////////////////////////////////////////////////////////////////
// Starting the SoftCascade main loop