Sha256: 33af2bf9521c2ffae7017529b8bf45007dde834384db92e14b5e5e4e9bb0f93e
Contents?: true
Size: 1.89 KB
Versions: 9
Compression:
Stored size: 1.89 KB
Contents
#pragma once
#include "LDA.h"
namespace tomoto
{
class IDMRModel;
template<TermWeight _tw>
struct DocumentDMR : public DocumentLDA<_tw>
{
using BaseDocument = DocumentLDA<_tw>;
using DocumentLDA<_tw>::DocumentLDA;
uint64_t metadata = 0;
std::vector<uint64_t> multiMetadata;
Vector mdVec;
size_t mdHash = (size_t)-1;
mutable Matrix cachedAlpha;
RawDoc::MiscType makeMisc(const ITopicModel* tm) const override;
DEFINE_SERIALIZER_AFTER_BASE_WITH_VERSION(BaseDocument, 0, metadata);
DEFINE_TAGGED_SERIALIZER_AFTER_BASE_WITH_VERSION(BaseDocument, 1, 0x00010001, metadata, multiMetadata);
};
struct DMRArgs : public LDAArgs
{
Float alphaEps = 1e-10;
Float sigma = 1.0;
};
class IDMRModel : public ILDAModel
{
public:
using DefaultDocType = DocumentDMR<TermWeight::one>;
static IDMRModel* create(TermWeight _weight, const DMRArgs& args,
bool scalarRng = false);
virtual void setAlphaEps(Float _alphaEps) = 0;
virtual Float getAlphaEps() const = 0;
virtual void setOptimRepeat(size_t repeat) = 0;
virtual size_t getOptimRepeat() const = 0;
virtual size_t getF() const = 0;
virtual size_t getMdVecSize() const = 0;
virtual Float getSigma() const = 0;
virtual const Dictionary& getMetadataDict() const = 0;
virtual const Dictionary& getMultiMetadataDict() const = 0;
virtual std::vector<Float> getLambdaByMetadata(size_t metadataId) const = 0;
virtual std::vector<Float> getLambdaByTopic(Tid tid) const = 0;
virtual std::vector<Float> getTopicPrior(
const std::string& metadata,
const std::vector<std::string>& multiMetadata,
bool raw = false
) const = 0;
};
template<TermWeight _tw>
RawDoc::MiscType DocumentDMR<_tw>::makeMisc(const ITopicModel* tm) const
{
RawDoc::MiscType ret = DocumentLDA<_tw>::makeMisc(tm);
auto inst = static_cast<const IDMRModel*>(tm);
ret["metadata"] = inst->getMetadataDict().toWord(metadata);
return ret;
}
}
Version data entries
9 entries across 9 versions & 1 rubygems