void GNaiveBayes::predictDistribution(const GVec& in, GPrediction* out)
{
if(m_nSampleCount <= 0)
throw Ex("You must call train before you call eval");
for(size_t n = 0; n < m_pRelLabels->size(); n++)
m_pOutputs[n]->eval(in.data(), &out[n], m_equivalentSampleSize);
}
void GNaiveBayes::predict(const GVec& in, GVec& out)
{
if(m_nSampleCount <= 0)
throw Ex("You must call train before you call eval");
for(size_t n = 0; n < m_pRelLabels->size(); n++)
out[n] = m_pOutputs[n]->predict(in.data(), m_equivalentSampleSize, &m_rand);
}
// virtual
void GNaiveInstance::trainIncremental(const GVec& pIn, const GVec& pOut)
{
if(!m_pHeap)
m_pHeap = new GHeap(1024);
double* pOutputs = (double*)m_pHeap->allocAligned(sizeof(double) * m_pRelLabels->size());
GVec::copy(pOutputs, pOut.data(), m_pRelLabels->size());
for(size_t i = 0; i < m_pRelFeatures->size(); i++)
{
if(pIn[i] != UNKNOWN_REAL_VALUE)
m_pAttrs[i]->addInstance(pIn[i], pOutputs);
}
}
// virtual
void GWag::trainInner(const GMatrix& features, const GMatrix& labels)
{
GNeuralNetLearner* pTemp = NULL;
std::unique_ptr<GNeuralNetLearner> hTemp;
size_t weights = 0;
GVec pWeightBuf;
GVec pWeightBuf2;
for(size_t i = 0; i < m_models; i++)
{
m_pNN->train(features, labels);
if(pTemp)
{
// Average m_pNN with pTemp
if(!m_noAlign)
m_pNN->nn().align(pTemp->nn());
pTemp->nn().weightsToVector(pWeightBuf.data());
m_pNN->nn().weightsToVector(pWeightBuf2.data());
pWeightBuf *= (double(i) / (i + 1));
pWeightBuf.addScaled(1.0 / (i + 1), pWeightBuf2);
pTemp->nn().vectorToWeights(pWeightBuf.data());
}
else
{
// Copy the m_pNN
GDom doc;
GDomNode* pNode = m_pNN->serialize(&doc);
GLearnerLoader ll;
pTemp = new GNeuralNetLearner(pNode);
hTemp.reset(pTemp);
weights = pTemp->nn().weightCount();
pWeightBuf.resize(weights);
pWeightBuf2.resize(weights);
}
}
pTemp->nn().weightsToVector(pWeightBuf.data());
m_pNN->nn().vectorToWeights(pWeightBuf.data());
}
// virtual
void GNaiveBayes::trainIncremental(const GVec& in, const GVec& out)
{
for(size_t n = 0; n < m_pRelLabels->size(); n++)
m_pOutputs[n]->AddTrainingSample(in.data(), (int)out[n]);
m_nSampleCount++;
}