public ClassifierResult <LblT> Classify(ExT example)
{
Utils.ThrowException((m_examples == null || m_similarity == null) ? new InvalidOperationException() : null);
Utils.ThrowException(example == null ? new ArgumentNullException("example") : null);
ArrayList <KeyDat <double, LabeledExample <LblT, ExT> > > tmp = new ArrayList <KeyDat <double, LabeledExample <LblT, ExT> > >(m_examples.Count);
foreach (LabeledExample <LblT, ExT> labeled_example in m_examples)
{
double sim = m_similarity.GetSimilarity(example, labeled_example.Example);
tmp.Add(new KeyDat <double, LabeledExample <LblT, ExT> >(sim, labeled_example));
}
tmp.Sort(new DescSort <KeyDat <double, LabeledExample <LblT, ExT> > >());
Dictionary <LblT, double> voting = new Dictionary <LblT, double>(m_lbl_cmp);
int n = Math.Min(m_k, tmp.Count);
double value;
if (m_soft_voting) // "soft" voting
{
for (int i = 0; i < n; i++)
{
KeyDat <double, LabeledExample <LblT, ExT> > item = tmp[i];
if (!voting.TryGetValue(item.Dat.Label, out value))
{
voting.Add(item.Dat.Label, item.Key);
}
else
{
voting[item.Dat.Label] = value + item.Key;
}
}
}
else // normal voting
{
for (int i = 0; i < n; i++)
{
KeyDat <double, LabeledExample <LblT, ExT> > item = tmp[i];
if (!voting.TryGetValue(item.Dat.Label, out value))
{
voting.Add(item.Dat.Label, 1);
}
else
{
voting[item.Dat.Label] = value + 1.0;
}
}
}
ClassifierResult <LblT> classifier_result = new ClassifierResult <LblT>();
foreach (KeyValuePair <LblT, double> item in voting)
{
classifier_result.Items.Add(new KeyDat <double, LblT>(item.Value, item.Key));
}
classifier_result.Items.Sort(new DescSort <KeyDat <double, LblT> >());
return(classifier_result);
}
public static ClassifierResult <LblT> ClassifyGroup <LblT, ExT>(IEnumerable <ExT> examples, IModel <LblT, ExT> model, GroupClassifyMethod method, IEqualityComparer <LblT> lbl_cmp)
{
Dictionary <LblT, double> tmp = new Dictionary <LblT, double>(lbl_cmp);
foreach (ExT example in examples)
{
ClassifierResult <LblT> result = model.Classify(example); // throws InvalidOperationException, ArgumentNullException
foreach (KeyDat <double, LblT> lbl_info in result)
{
if (method == GroupClassifyMethod.Vote)
{
if (!tmp.ContainsKey(lbl_info.Dat))
{
tmp.Add(lbl_info.Dat, 1);
}
else
{
tmp[lbl_info.Dat]++;
}
break;
}
else
{
if (!tmp.ContainsKey(lbl_info.Dat))
{
tmp.Add(lbl_info.Dat, lbl_info.Key);
}
else
{
switch (method)
{
case GroupClassifyMethod.Max:
tmp[lbl_info.Dat] = Math.Max(lbl_info.Key, tmp[lbl_info.Dat]);
break;
case GroupClassifyMethod.Sum:
tmp[lbl_info.Dat] += lbl_info.Key;
break;
}
}
}
}
}
ClassifierResult <LblT> aggr_result = new ClassifierResult <LblT>();
foreach (KeyValuePair <LblT, double> item in tmp)
{
aggr_result.Items.Add(new KeyDat <double, LblT>(item.Value, item.Key));
}
aggr_result.Items.Sort(new DescSort <KeyDat <double, LblT> >());
return(aggr_result);
}
public ClassifierResult <LblT> Classify(SparseVector <double> .ReadOnly example)
{
Utils.ThrowException(m_centroids == null ? new InvalidOperationException() : null);
Utils.ThrowException(example == null ? new ArgumentNullException("example") : null);
ClassifierResult <LblT> result = new ClassifierResult <LblT>();
foreach (Pair <LblT, SparseVector <double> .ReadOnly> labeled_centroid in m_centroids)
{
double sim = m_similarity.GetSimilarity(labeled_centroid.Second, example);
result.Items.Add(new KeyDat <double, LblT>(sim, labeled_centroid.First));
}
result.Items.Sort(new DescSort <KeyDat <double, LblT> >());
return(result);
}
