public void Train(ILabeledExampleCollection <LblT, SparseVector <double> > dataset)
{
Utils.ThrowException(dataset == null ? new ArgumentNullException("dataset") : null);
Utils.ThrowException(dataset.Count == 0 ? new ArgumentValueException("dataset") : null);
mDatasetMtx = ModelUtils.GetTransposedMatrix(ModelUtils.ConvertToUnlabeledDataset(dataset));
mLabels = new ArrayList <LblT>();
foreach (LabeledExample <LblT, SparseVector <double> > labeledExample in dataset)
{
mLabels.Add(labeledExample.Label);
}
}
public void Train(ILabeledExampleCollection <LblT, SparseVector <double> > dataset)
{
Utils.ThrowException(dataset == null ? new ArgumentNullException("dataset") : null);
Utils.ThrowException(dataset.Count == 0 ? new ArgumentValueException("dataset") : null);
Dictionary <LblT, CentroidData> centroids = new Dictionary <LblT, CentroidData>(mLblCmp);
foreach (LabeledExample <LblT, SparseVector <double> > labeledExample in dataset)
{
if (!centroids.ContainsKey(labeledExample.Label))
{
CentroidData centroidData = new CentroidData();
centroidData.AddToSum(labeledExample.Example);
centroids.Add(labeledExample.Label, centroidData);
}
else
{
CentroidData centroidData = centroids[labeledExample.Label];
centroidData.AddToSum(labeledExample.Example);
}
}
foreach (CentroidData cenData in centroids.Values)
{
cenData.UpdateCentroidLen();
}
double learnRate = 1;
double[][] dotProd = null;
SparseMatrix <double> dsMtx = null;
if (mIterations > 0)
{
dotProd = new double[centroids.Count][];
dsMtx = ModelUtils.GetTransposedMatrix(ModelUtils.ConvertToUnlabeledDataset(dataset));
}
for (int iter = 1; iter <= mIterations; iter++)
{
mLogger.Info("Train", "Iteration {0} / {1} ...", iter, mIterations);
// compute dot products
mLogger.Info("Train", "Computing dot products ...");
int j = 0;
foreach (KeyValuePair <LblT, CentroidData> labeledCentroid in centroids)
{
mLogger.ProgressNormal(Logger.Level.Info, /*sender=*/ this, "Train", "Centroid {0} / {1} ...", j + 1, centroids.Count);
SparseVector <double> cenVec = labeledCentroid.Value.GetSparseVector();
dotProd[j] = ModelUtils.GetDotProductSimilarity(dsMtx, dataset.Count, cenVec);
j++;
}
// classify training examples
mLogger.Info("Train", "Classifying training examples ...");
int errCount = 0;
for (int instIdx = 0; instIdx < dataset.Count; instIdx++)
{
mLogger.ProgressFast(Logger.Level.Info, /*sender=*/ this, "Train", "Example {0} / {1} ...", instIdx + 1, dataset.Count);
double maxSim = double.MinValue;
CentroidData assignedCentroid = null;
CentroidData actualCentroid = null;
LabeledExample <LblT, SparseVector <double> > labeledExample = dataset[instIdx];
SparseVector <double> vec = labeledExample.Example;
int cenIdx = 0;
foreach (KeyValuePair <LblT, CentroidData> labeledCentroid in centroids)
{
double sim = dotProd[cenIdx][instIdx];
if (sim > maxSim)
{
maxSim = sim; assignedCentroid = labeledCentroid.Value;
}
if (labeledCentroid.Key.Equals(labeledExample.Label))
{
actualCentroid = labeledCentroid.Value;
}
cenIdx++;
}
if (assignedCentroid != actualCentroid)
{
assignedCentroid.AddToDiff(-learnRate, vec);
actualCentroid.AddToDiff(learnRate, vec);
errCount++;
}
}
mLogger.Info("Train", "Training set error rate: {0:0.00}%", (double)errCount / (double)dataset.Count * 100.0);
// update centroids
int k = 0;
foreach (CentroidData centroidData in centroids.Values)
{
mLogger.ProgressNormal(Logger.Level.Info, /*sender=*/ this, "Train", "Centroid {0} / {1} ...", ++k, centroids.Count);
centroidData.Update(mPositiveValuesOnly);
centroidData.UpdateCentroidLen();
}
learnRate *= mDamping;
}
mCentroidMtxTr = new SparseMatrix <double>();
mLabels = new ArrayList <LblT>();
int rowIdx = 0;
foreach (KeyValuePair <LblT, CentroidData> labeledCentroid in centroids)
{
mCentroidMtxTr[rowIdx++] = labeledCentroid.Value.GetSparseVector();
mLabels.Add(labeledCentroid.Key);
}
mCentroidMtxTr = mCentroidMtxTr.GetTransposedCopy();
}
public UnlabeledDataset <ExT> ToUnlabeledDataset()
{
return(ModelUtils.ConvertToUnlabeledDataset <LblT, ExT>(this));
}