public virtual double[][] SeparateLopExpertWeights(double[] learnedParams)
{
double[][] learnedWeights = new double[numLopExpert][];
double[][][] learnedWeights2D = SeparateLopExpertWeights2D(learnedParams);
for (int i = 0; i < numLopExpert; i++)
{
learnedWeights[i] = CRFLogConditionalObjectiveFunction.To1D(learnedWeights2D[i], lopExpertWeights[i].Length);
}
return(learnedWeights);
}
public virtual Pair <int, double> Process(Pair <int, IList <int> > threadIDAndDocIndices)
{
int tID = threadIDAndDocIndices.First();
if (tID < 0 || tID >= this._enclosing.multiThreadGrad)
{
throw new ArgumentException("threadID must be with in range 0 <= tID < multiThreadGrad(=" + this._enclosing.multiThreadGrad + ")");
}
IList <int> docIDs = threadIDAndDocIndices.Second();
double[][] partE;
// initialized below
double[][] partEhat = null;
// initialized below
if (this._enclosing.multiThreadGrad == 1)
{
partE = this._enclosing.E;
if (this.calculateEmpirical)
{
partEhat = this._enclosing.Ehat;
}
}
else
{
partE = this._enclosing.parallelE[tID];
// TODO: if we put this on the heap, this clearing will be unnecessary
CRFLogConditionalObjectiveFunction.Clear2D(partE);
if (this.calculateEmpirical)
{
partEhat = this._enclosing.parallelEhat[tID];
CRFLogConditionalObjectiveFunction.Clear2D(partEhat);
}
}
double probSum = 0;
foreach (int docIndex in docIDs)
{
if (this.calculateEmpirical)
{
probSum += this._enclosing.ExpectedAndEmpiricalCountsAndValueForADoc(partE, partEhat, docIndex);
}
else
{
probSum += this._enclosing.ExpectedCountsAndValueForADoc(partE, docIndex);
}
}
return(new Pair <int, double>(tID, probSum));
}
public ExpectationThreadsafeProcessor(CRFLogConditionalObjectiveFunction _enclosing, bool calculateEmpirical)
{
this._enclosing = _enclosing;
this.calculateEmpirical = calculateEmpirical;
}
public ExpectationThreadsafeProcessor(CRFLogConditionalObjectiveFunction _enclosing)
{
this._enclosing = _enclosing;
}
