// 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]);
}
GNaiveInstance::GNaiveInstance(GDomNode* pNode, GLearnerLoader& ll)
: GIncrementalLearner(pNode, ll), m_pHeap(NULL)
{
m_pAttrs = NULL;
m_pValueSums = NULL;
m_nNeighbors = (size_t)pNode->field("neighbors")->asInt();
beginIncrementalLearningInner(*m_pRelFeatures, *m_pRelLabels);
GDomNode* pAttrs = pNode->field("attrs");
GDomListIterator it(pAttrs);
if(it.remaining() != m_pRelFeatures->size())
throw Ex("Expected ", to_str(m_pRelFeatures->size()), " attrs, got ", to_str(it.remaining()), " attrs");
m_pHeap = new GHeap(1024);
for(size_t i = 0; i < m_pRelFeatures->size(); i++)
{
delete(m_pAttrs[i]);
m_pAttrs[i] = new GNaiveInstanceAttr(it.current(), m_pRelLabels->size(), m_pHeap);
it.advance();
}
}