public void Output(ref VBuffer <Double> src, ref VBuffer <Float> dst, int numBurninIter, bool reset)
{
// Prediction for a single document.
// LdaSingleBox.InitializeBeforeTest() is NOT thread-safe.
if (!_predictionPreparationDone)
{
lock (_preparationSyncRoot)
{
if (!_predictionPreparationDone)
{
//do some preparation for building tables in native c++
_ldaTrainer.InitializeBeforeTest();
_predictionPreparationDone = true;
}
}
}
int len = InfoEx.NumTopic;
var values = dst.Values;
var indices = dst.Indices;
if (src.Count == 0)
{
dst = new VBuffer <Float>(len, 0, values, indices);
return;
}
// Make sure all the frequencies are valid and truncate if the sum gets too large.
int docSize = 0;
int termNum = 0;
for (int i = 0; i < src.Count; i++)
{
int termFreq = GetFrequency(src.Values[i]);
if (termFreq < 0)
{
// REVIEW: Should this log a warning message? And what should it produce?
// It currently produces a vbuffer of all NA values.
// REVIEW: Need a utility method to do this...
if (Utils.Size(values) < len)
{
values = new Float[len];
}
for (int k = 0; k < len; k++)
{
values[k] = Float.NaN;
}
dst = new VBuffer <Float>(len, values, indices);
return;
}
if (docSize >= InfoEx.NumMaxDocToken - termFreq)
{
break;
}
docSize += termFreq;
termNum++;
}
// REVIEW: Too much memory allocation here on each prediction.
List <KeyValuePair <int, float> > retTopics;
if (src.IsDense)
{
retTopics = _ldaTrainer.TestDocDense(src.Values, termNum, numBurninIter, reset);
}
else
{
retTopics = _ldaTrainer.TestDoc(src.Indices.Take(src.Count).ToArray(), src.Values.Take(src.Count).ToArray(), termNum, numBurninIter, reset);
}
int count = retTopics.Count;
Contracts.Assert(count <= len);
if (Utils.Size(values) < count)
{
values = new Float[count];
}
if (count < len && Utils.Size(indices) < count)
{
indices = new int[count];
}
double normalizer = 0;
for (int i = 0; i < count; i++)
{
int index = retTopics[i].Key;
Float value = retTopics[i].Value;
Contracts.Assert(value >= 0);
Contracts.Assert(0 <= index && index < len);
if (count < len)
{
Contracts.Assert(i == 0 || indices[i - 1] < index);
indices[i] = index;
}
else
{
Contracts.Assert(index == i);
}
values[i] = value;
normalizer += value;
}
if (normalizer > 0)
{
for (int i = 0; i < count; i++)
{
values[i] = (Float)(values[i] / normalizer);
}
}
dst = new VBuffer <Float>(len, count, values, indices);
}
public LdaState(IExceptionContext ectx, ModelLoadContext ctx)
: this()
{
ectx.AssertValue(ctx);
// *** Binary format ***
// <ColInfoEx>
// int: vocabnum
// long: memblocksize
// long: aliasMemBlockSize
// (serializing term by term, for one term)
// int: term_id, int: topic_num, KeyValuePair<int, int>[]: termTopicVector
InfoEx = new ColInfoEx(ectx, ctx);
_numVocab = ctx.Reader.ReadInt32();
ectx.CheckDecode(_numVocab > 0);
long memBlockSize = ctx.Reader.ReadInt64();
ectx.CheckDecode(memBlockSize > 0);
long aliasMemBlockSize = ctx.Reader.ReadInt64();
ectx.CheckDecode(aliasMemBlockSize > 0);
_ldaTrainer = new LdaSingleBox(
InfoEx.NumTopic,
_numVocab, /* Need to set number of vocabulary here */
InfoEx.AlphaSum,
InfoEx.Beta,
InfoEx.NumIter,
InfoEx.LikelihoodInterval,
InfoEx.NumThread,
InfoEx.MHStep,
InfoEx.NumSummaryTermPerTopic,
false,
InfoEx.NumMaxDocToken);
_ldaTrainer.AllocateModelMemory(_numVocab, InfoEx.NumTopic, memBlockSize, aliasMemBlockSize);
for (int i = 0; i < _numVocab; i++)
{
int termID = ctx.Reader.ReadInt32();
ectx.CheckDecode(termID >= 0);
int termTopicNum = ctx.Reader.ReadInt32();
ectx.CheckDecode(termTopicNum >= 0);
int[] topicId = new int[termTopicNum];
int[] topicProb = new int[termTopicNum];
for (int j = 0; j < termTopicNum; j++)
{
topicId[j] = ctx.Reader.ReadInt32();
topicProb[j] = ctx.Reader.ReadInt32();
}
//set the topic into _ldaTrainer inner topic table
_ldaTrainer.SetModel(termID, topicId, topicProb, termTopicNum);
}
//do the preparation
if (!_predictionPreparationDone)
{
_ldaTrainer.InitializeBeforeTest();
_predictionPreparationDone = true;
}
}
