// virtual
GDomNode* GLinearRegressor::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GLinearRegressor");
pNode->addField(pDoc, "beta", m_pBeta->serialize(pDoc));
pNode->addField(pDoc, "epsilon", m_epsilon.serialize(pDoc));
return pNode;
}
// virtual
GDomNode* GLNormDistance::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GLNormDistance");
pNode->addField(pDoc, "norm", pDoc->newDouble(m_norm));
pNode->addField(pDoc, "dwu", pDoc->newDouble(m_diffWithUnknown));
return pNode;
}
// virtual
GDomNode* GLinearDistribution::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GLinearDistribution");
pNode->addField(pDoc, "nd", pDoc->newDouble(m_noiseDev));
pNode->addField(pDoc, "w", m_pWBar->serialize(pDoc));
pNode->addField(pDoc, "a", m_pAInv->serialize(pDoc));
return pNode;
}
// virtual
GDomNode* GNaiveInstance::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GNaiveInstance");
pNode->addField(pDoc, "neighbors", pDoc->newInt(m_nNeighbors));
GDomNode* pAttrs = pNode->addField(pDoc, "attrs", pDoc->newList());
for(size_t i = 0; i < m_pRelFeatures->size(); i++)
pAttrs->addItem(pDoc, m_pAttrs[i]->serialize(pDoc, m_pRelLabels->size()));
return pNode;
}
// virtual
GDomNode* GRowDistanceScaled::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GRowDistance");
size_t dims = m_pRelation->size();
GDomNode* pScaleFactors = pNode->addField(pDoc, "scaleFactors", pDoc->newList());
for(size_t i = 0; i < dims; i++)
pScaleFactors->addItem(pDoc, pDoc->newDouble(m_pScaleFactors[i]));
return pNode;
}
// virtual
GDomNode* GPolynomial::serialize(GDom* pDoc)
{
GDomNode* pNode = baseDomNode(pDoc, "GPolynomial");
pNode->addField(pDoc, "controlPoints", pDoc->newInt(m_controlPoints));
GDomNode* pPolys = pNode->addField(pDoc, "polys", pDoc->newList());
for(size_t i = 0; i < m_polys.size(); i++)
pPolys->addItem(pDoc, m_polys[i]->serialize(pDoc));
return pNode;
}
// virtual
GDomNode* GNaiveBayes::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GNaiveBayes");
pNode->addField(pDoc, "sampleCount", pDoc->newInt(m_nSampleCount));
pNode->addField(pDoc, "ess", pDoc->newDouble(m_equivalentSampleSize));
GDomNode* pOutputs = pNode->addField(pDoc, "outputs", pDoc->newList());
for(size_t i = 0; i < m_pRelLabels->size(); i++)
pOutputs->addItem(pDoc, m_pOutputs[i]->serialize(pDoc));
return pNode;
}
// virtual
GDomNode* GGaussianProcess::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GGaussianProcess");
pNode->addField(pDoc, "wv", pDoc->newDouble(m_weightsPriorVar));
pNode->addField(pDoc, "nv", pDoc->newDouble(m_noiseVar));
pNode->addField(pDoc, "ms", pDoc->newInt(m_maxSamples));
pNode->addField(pDoc, "l", m_pLInv->serialize(pDoc));
pNode->addField(pDoc, "a", m_pAlpha->serialize(pDoc));
pNode->addField(pDoc, "feat", m_pStoredFeatures->serialize(pDoc));
pNode->addField(pDoc, "kernel", m_pKernel->serialize(pDoc));
return pNode;
}
// virtual
GDomNode* GEuclidSimilarity::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GEuclidSimilarity");
return pNode;
}
// virtual
GDomNode* GPearsonCorrelation::serialize(GDom* pDoc) const
{
GDomNode* pNode = baseDomNode(pDoc, "GPearsonCorrelation");
return pNode;
}
