internal void CopyModel(LdaSingleBox trainer, int wordId)
{
int length = NumTopic;
LdaInterface.GetWordTopic(trainer._engine, wordId, _topics, _probabilities, ref length);
LdaInterface.SetWordTopic(_engine, wordId, _topics, _probabilities, length);
}
internal List <KeyValuePair <int, float> > TestDoc(ReadOnlySpan <int> termID, ReadOnlySpan <double> termVal, int termNum, int numBurninIter, bool reset)
{
Debug.Assert(termNum > 0);
Debug.Assert(termVal.Length >= termNum);
Debug.Assert(termID.Length >= termNum);
int[] pID = new int[termNum];
int[] pVal = new int[termVal.Length];
for (int i = 0; i < termVal.Length; i++)
{
pVal[i] = (int)termVal[i];
}
int[] pTopic = new int[NumTopic];
int[] pProb = new int[NumTopic];
termID.Slice(0, termNum).CopyTo(pID);
int numTopicReturn = NumTopic;
LdaInterface.TestOneDoc(_engine, pID, pVal, termNum, pTopic, pProb, ref numTopicReturn, numBurninIter, reset);
// PREfast suspects that the value of numTopicReturn could be changed in _engine->TestOneDoc, which might result in read overrun in the following loop.
if (numTopicReturn > NumTopic)
{
Debug.Assert(false);
numTopicReturn = NumTopic;
}
var topicRet = new List <KeyValuePair <int, float> >();
for (int i = 0; i < numTopicReturn; i++)
{
topicRet.Add(new KeyValuePair <int, float>(pTopic[i], (float)pProb[i]));
}
return(topicRet);
}
internal void SetModel(int termID, int[] topicID, int[] topicProb, int topicNum)
{
Debug.Assert(termID >= 0);
Debug.Assert(topicNum <= NumTopic);
Array.Copy(topicID, _topics, topicNum);
Array.Copy(topicProb, _probabilities, topicNum);
LdaInterface.SetWordTopic(_engine, termID, _topics, _probabilities, topicNum);
}
internal void AllocateModelMemory(int numTopic, int numVocab, long tableSize, long aliasTableSize)
{
Debug.Assert(numTopic >= 0);
Debug.Assert(numVocab >= 0);
Debug.Assert(tableSize >= 0);
Debug.Assert(aliasTableSize >= 0);
LdaInterface.AllocateModelMemory(_engine, numVocab, numTopic, tableSize, aliasTableSize);
}
internal void Test(int numBurninIter, float[] logLikelihood)
{
Debug.Assert(numBurninIter >= 0);
var pLogLikelihood = new float[numBurninIter];
LdaInterface.Test(_engine, numBurninIter, pLogLikelihood);
logLikelihood = pLogLikelihood.Select(item => (float)item).ToArray();
}
public void Dispose()
{
if (_isDisposed)
{
return;
}
_isDisposed = true;
LdaInterface.DestroyEngine(_engine);
_engine.Ptr = IntPtr.Zero;
}
internal void Train(string trainOutput)
{
if (string.IsNullOrWhiteSpace(trainOutput))
{
LdaInterface.Train(_engine, null);
}
else
{
LdaInterface.Train(_engine, trainOutput);
}
}
internal KeyValuePair <int, int>[] GetModel(int wordId)
{
int length = NumTopic;
LdaInterface.GetWordTopic(_engine, wordId, _topics, _probabilities, ref length);
var wordTopicVector = new KeyValuePair <int, int> [length];
for (int i = 0; i < length; i++)
{
wordTopicVector[i] = new KeyValuePair <int, int>(_topics[i], _probabilities[i]);
}
return(wordTopicVector);
}
internal KeyValuePair <int, float>[] GetTopicSummary(int topicId)
{
int length = _numSummaryTerms;
LdaInterface.GetTopicSummary(_engine, topicId, _summaryTerm, _summaryTermProb, ref length);
var topicSummary = new KeyValuePair <int, float> [length];
for (int i = 0; i < length; i++)
{
topicSummary[i] = new KeyValuePair <int, float>(_summaryTerm[i], _summaryTermProb[i]);
}
return(topicSummary);
}
internal int LoadDoc(ReadOnlySpan <int> termID, ReadOnlySpan <double> termVal, int termNum, int numVocab)
{
Debug.Assert(numVocab == NumVocab);
Debug.Assert(termNum > 0);
Debug.Assert(termID.Length >= termNum);
Debug.Assert(termVal.Length >= termNum);
int[] pID = new int[termNum];
int[] pVal = new int[termVal.Length];
for (int i = 0; i < termVal.Length; i++)
{
pVal[i] = (int)termVal[i];
}
termID.Slice(0, termNum).CopyTo(pID);
return(LdaInterface.FeedInData(_engine, pID, pVal, termNum, NumVocab));
}
internal LdaSingleBox(int numTopic, int numVocab, float alpha, float beta, int numIter, int likelihoodInterval, int numThread, int mhstep, int numSummaryTerms, bool denseOutput, int maxDocToken)
{
NumTopic = numTopic;
NumVocab = numVocab;
_alpha = alpha;
_beta = beta;
_mhStep = mhstep;
_numSummaryTerms = numSummaryTerms;
_denseOutput = denseOutput;
_likelihoodInterval = likelihoodInterval;
_numThread = numThread;
_topics = new int[numTopic];
_probabilities = new int[numTopic];
_summaryTerm = new int[_numSummaryTerms];
_summaryTermProb = new float[_numSummaryTerms];
_engine = LdaInterface.CreateEngine(numTopic, numVocab, alpha, beta, numIter, likelihoodInterval, numThread, mhstep, maxDocToken);
}
internal List <KeyValuePair <int, float> > GetDocTopicVector(int docID)
{
int numTopicReturn = NumTopic;
LdaInterface.GetDocTopic(_engine, docID, _topics, _probabilities, ref numTopicReturn);
var topicRet = new List <KeyValuePair <int, float> >();
int currentTopic = 0;
for (int i = 0; i < numTopicReturn; i++)
{
if (_denseOutput)
{
while (currentTopic < _topics[i])
{
//use a value to smooth the count so that we get dense output on each topic
//the smooth value is usually set to 0.1
topicRet.Add(new KeyValuePair <int, float>(currentTopic, (float)_alpha));
currentTopic++;
}
topicRet.Add(new KeyValuePair <int, float>(_topics[i], _probabilities[i] + (float)_alpha));
currentTopic++;
}
else
{
topicRet.Add(new KeyValuePair <int, float>(_topics[i], (float)_probabilities[i]));
}
}
if (_denseOutput)
{
while (currentTopic < NumTopic)
{
topicRet.Add(new KeyValuePair <int, float>(currentTopic, (float)_alpha));
currentTopic++;
}
}
return(topicRet);
}
internal void GetModelStat(out long memBlockSize, out long aliasMemBlockSize)
{
LdaInterface.GetModelStat(_engine, out memBlockSize, out aliasMemBlockSize);
}
internal void SetAlphaSum(float averageDocLength)
{
LdaInterface.SetAlphaSum(_engine, averageDocLength);
}
internal void AllocateDataMemory(int docNum, long corpusSize)
{
Debug.Assert(docNum >= 0);
Debug.Assert(corpusSize >= 0);
LdaInterface.AllocateDataMemory(_engine, docNum, corpusSize);
}
internal void InitializeBeforeTrain()
{
LdaInterface.InitializeBeforeTrain(_engine);
}
internal void InitializeBeforeTest()
{
LdaInterface.InitializeBeforeTest(_engine);
}
internal void CleanData()
{
LdaInterface.CleanData(_engine);
}
internal void CleanModel()
{
LdaInterface.CleanModel(_engine);
}