// virtual
void GInstanceTable::train(GData* pData, int labelDims)
{
enableIncrementalLearning(pData->relation(), labelDims, NULL, NULL);
int dims = pData->relation()->size() - 1;
for(size_t i = 0; i < pData->rows(); i++)
{
double* pRow = pData->row(i);
trainIncremental(pRow, pRow + dims);
}
}
// virtual
void GNaiveBayes::trainInner(const GMatrix& features, const GMatrix& labels)
{
if(!features.relation().areNominal())
throw Ex("GNaiveBayes only supports nominal features. Perhaps you should wrap it in a GAutoFilter.");
if(!labels.relation().areNominal())
throw Ex("GNaiveBayes only supports nominal labels. Perhaps you should wrap it in a GAutoFilter.");
beginIncrementalLearningInner(features.relation(), labels.relation());
for(size_t n = 0; n < features.rows(); n++)
trainIncremental(features[n], labels[n]);
}
// virtual
void GNaiveInstance::trainInner(const GMatrix& features, const GMatrix& labels)
{
if(!features.relation().areContinuous())
throw Ex("GNaiveInstance only supports continuous features. Perhaps you should wrap it in a GAutoFilter.");
if(!labels.relation().areContinuous())
throw Ex("GNaiveInstance only supports continuous labels. Perhaps you should wrap it in a GAutoFilter.");
beginIncrementalLearningInner(features.relation(), labels.relation());
for(size_t i = 0; i < features.rows(); i++)
trainIncremental(features[i], labels[i]);
}
// virtual
void GNaiveBayes::trainSparse(GSparseMatrix& features, GMatrix& labels)
{
if(features.rows() != labels.rows())
throw Ex("Expected the features and labels to have the same number of rows");
size_t featureDims = features.cols();
GUniformRelation featureRel(featureDims, 2);
beginIncrementalLearning(featureRel, labels.relation());
GVec fullRow(featureDims);
for(size_t n = 0; n < features.rows(); n++)
{
features.fullRow(fullRow, n);
for(size_t i = 0; i < featureDims; i++)
{
if(fullRow[i] < 1e-6)
fullRow[i] = 0.0;
else
fullRow[i] = 1.0;
}
trainIncremental(fullRow, labels[n]);
}
}
