// virtual
void GBucket::trainInner(GMatrix& features, GMatrix& labels)
{
size_t nLearnerCount = m_models.size();
double dBestError = 1e200;
GSupervisedLearner* pLearner;
m_nBestLearner = (size_t)m_rand.next(nLearnerCount);
double err;
for(size_t i = 0; i < nLearnerCount; i++)
{
pLearner = m_models[i];
try
{
err = pLearner->heuristicValidate(features, labels);
}
catch(std::exception& e)
{
onError(e);
continue;
}
if(err < dBestError)
{
dBestError = err;
m_nBestLearner = i;
}
pLearner->clear();
}
pLearner = m_models[m_nBestLearner];
pLearner->train(features, labels);
}
// virtual
void GBucket::trainInner(const GMatrix& features, const GMatrix& labels)
{
size_t nLearnerCount = m_models.size();
double dBestError = 1e200;
GSupervisedLearner* pLearner;
m_nBestLearner = (size_t)m_rand.next(nLearnerCount);
double err;
for(size_t i = 0; i < nLearnerCount; i++)
{
pLearner = m_models[i];
err = pLearner->crossValidate(features, labels, 2);
if(err < dBestError)
{
dBestError = err;
m_nBestLearner = i;
}
pLearner->clear();
}
pLearner = m_models[m_nBestLearner];
pLearner->train(features, labels);
}
// virtual
void GResamplingAdaBoost::trainInnerInner(const GMatrix& features, const GMatrix& labels)
{
clear();
// Initialize all instances with uniform weights
GVec pDistribution(features.rows());
pDistribution.fill(1.0 / features.rows());
size_t drawRows = size_t(m_trainSize * features.rows());
size_t* pDrawnIndexes = new size_t[drawRows];
std::unique_ptr<size_t[]> hDrawnIndexes(pDrawnIndexes);
// Train the ensemble
size_t labelDims = labels.cols();
double penalty = 1.0 / labelDims;
GVec prediction(labelDims);
for(size_t es = 0; es < m_ensembleSize; es++)
{
// Draw a training set from the distribution
GCategoricalSamplerBatch csb(features.rows(), pDistribution, m_rand);
csb.draw(drawRows, pDrawnIndexes);
GMatrix drawnFeatures(features.relation().clone());
GReleaseDataHolder hDrawnFeatures(&drawnFeatures);
GMatrix drawnLabels(labels.relation().clone());
GReleaseDataHolder hDrawnLabels(&drawnLabels);
size_t* pIndex = pDrawnIndexes;
for(size_t i = 0; i < drawRows; i++)
{
drawnFeatures.takeRow((GVec*)&features[*pIndex]);
drawnLabels.takeRow((GVec*)&labels[*pIndex]);
pIndex++;
}
// Train an instance of the model and store a clone of it
m_pLearner->train(drawnFeatures, drawnLabels);
GDom doc;
GSupervisedLearner* pClone = m_pLoader->loadLearner(m_pLearner->serialize(&doc));
// Compute model weight
double err = 0.5;
for(size_t i = 0; i < features.rows(); i++)
{
pClone->predict(features[i], prediction);
const GVec& target = labels[i];
for(size_t j = 0; j < labelDims; j++)
{
if((int)target[j] != (int)prediction[j])
err += penalty;
}
}
err /= features.rows();
if(err >= 0.5)
{
delete(pClone);
break;
}
double weight = 0.5 * log((1.0 - err) / err);
m_models.push_back(new GWeightedModel(weight, pClone));
// Update the distribution to favor mis-classified instances
for(size_t i = 0; i < features.rows(); i++)
{
err = 0.0;
pClone->predict(features[i], prediction);
const GVec& target = labels[i];
for(size_t j = 0; j < labelDims; j++)
{
if((int)target[j] != (int)prediction[j])
err += penalty;
}
err /= labelDims;
pDistribution[i] *= exp(weight * (err * 2.0 - 1.0));
}
pDistribution.sumToOne();
}
normalizeWeights();
}
// virtual
void GResamplingAdaBoost::trainInnerInner(GMatrix& features, GMatrix& labels)
{
clear();
// Initialize all instances with uniform weights
double* pDistribution = new double[features.rows()];
ArrayHolder<double> hDistribution(pDistribution);
GVec::setAll(pDistribution, 1.0 / features.rows(), features.rows());
size_t drawRows = size_t(m_trainSize * features.rows());
size_t* pDrawnIndexes = new size_t[drawRows];
ArrayHolder<size_t> hDrawnIndexes(pDrawnIndexes);
// Train the ensemble
size_t labelDims = labels.cols();
double penalty = 1.0 / labelDims;
GTEMPBUF(double, prediction, labelDims);
for(size_t es = 0; es < m_ensembleSize; es++)
{
// Draw a training set from the distribution
GCategoricalSamplerBatch csb(features.rows(), pDistribution, m_rand);
csb.draw(drawRows, pDrawnIndexes);
GMatrix drawnFeatures(features.relation());
GReleaseDataHolder hDrawnFeatures(&drawnFeatures);
GMatrix drawnLabels(labels.relation());
GReleaseDataHolder hDrawnLabels(&drawnLabels);
size_t* pIndex = pDrawnIndexes;
for(size_t i = 0; i < drawRows; i++)
{
drawnFeatures.takeRow(features[*pIndex]);
drawnLabels.takeRow(labels[*pIndex]);
pIndex++;
}
// Train an instance of the model and store a clone of it
m_pLearner->train(drawnFeatures, drawnLabels);
GDom doc;
GSupervisedLearner* pClone = m_pLoader->loadSupervisedLearner(m_pLearner->serialize(&doc));
// Compute model weight
double err = 0.0;
for(size_t i = 0; i < features.rows(); i++)
{
pClone->predict(features[i], prediction);
double* pTarget = labels[i];
double* pPred = prediction;
for(size_t j = 0; j < labelDims; j++)
{
if((int)*(pTarget++) != (int)*(pPred++))
err += penalty;
}
}
err /= features.rows();
if(err >= 0.5)
{
delete(pClone);
break;
}
double weight = 0.5 * log((1.0 - err) / err);
m_models.push_back(new GWeightedModel(weight, pClone));
// Update the distribution to favor mis-classified instances
double* pDist = pDistribution;
for(size_t i = 0; i < features.rows(); i++)
{
err = 0.0;
pClone->predict(features[i], prediction);
double* pTarget = labels[i];
double* pPred = prediction;
for(size_t j = 0; j < labelDims; j++)
{
if((int)*(pTarget++) != (int)*(pPred++))
err += penalty;
}
err /= labelDims;
*pDist *= exp(weight * (err * 2.0 - 1.0));
pDist++;
}
GVec::sumToOne(pDistribution, features.rows());
}
normalizeWeights();
}
