private void ComputeEmpiricalStatistics(IList <F> geFeatures)
{
//allocate memory to the containers and initialize them
geFeature2EmpiricalDist = new double[][] { };
geFeature2DatumList = new List <IList <int> >(geFeatures.Count);
IDictionary <F, int> geFeatureMap = Generics.NewHashMap();
ICollection <int> activeUnlabeledExamples = Generics.NewHashSet();
for (int n = 0; n < geFeatures.Count; n++)
{
F geFeature = geFeatures[n];
geFeature2DatumList.Add(new List <int>());
Arrays.Fill(geFeature2EmpiricalDist[n], 0);
geFeatureMap[geFeature] = n;
}
//compute the empirical label distribution for each GE feature
for (int i = 0; i < labeledDataset.Size(); i++)
{
IDatum <L, F> datum = labeledDataset.GetDatum(i);
int labelID = labeledDataset.labelIndex.IndexOf(datum.Label());
foreach (F feature in datum.AsFeatures())
{
if (geFeatureMap.Contains(feature))
{
int geFnum = geFeatureMap[feature];
geFeature2EmpiricalDist[geFnum][labelID]++;
}
}
}
//now normalize and smooth the label distribution for each feature.
for (int n_1 = 0; n_1 < geFeatures.Count; n_1++)
{
ArrayMath.Normalize(geFeature2EmpiricalDist[n_1]);
SmoothDistribution(geFeature2EmpiricalDist[n_1]);
}
//now build the inverted index from each GE feature to unlabeled datums that contain it.
for (int i_1 = 0; i_1 < unlabeledDataList.Count; i_1++)
{
IDatum <L, F> datum = unlabeledDataList[i_1];
foreach (F feature in datum.AsFeatures())
{
if (geFeatureMap.Contains(feature))
{
int geFnum = geFeatureMap[feature];
geFeature2DatumList[geFnum].Add(i_1);
activeUnlabeledExamples.Add(i_1);
}
}
}
System.Console.Out.WriteLine("Number of active unlabeled examples:" + activeUnlabeledExamples.Count);
}