public Collection <Example> GetProof(SparseVector x)
{
Collection <Example> res = new Collection <Example>();
List <ExampleDistancePair> list = new List <ExampleDistancePair>();
foreach (Example e in m_t_set)
{
list.Add(new ExampleDistancePair(e, SparseVector.Distance(x, e.X)));
}
list.Sort();
int[] votes = new int[m_catnum];
for (int i = 0; i < votes.Length; i++)
{
votes[i] = 0;
}
for (int i = 0; i < this.m_k; i++)
{
ExampleDistancePair pair = list[i];
res.Add(pair.Example);
}
return(res);
}
public override int Predict(SparseVector x)
{
int iResult;
if (m_t_set.Count < this.m_k)
{
throw new ApplicationException("K great than training set count.");
}
List <ExampleDistancePair> list = new List <ExampleDistancePair>();
foreach (Example e in m_t_set)
{
list.Add(new ExampleDistancePair(e, SparseVector.Distance(x, e.X)));
}
list.Sort();
int[] votes = new int[m_catnum];
for (int i = 0; i < votes.Length; i++)
{
votes[i] = 0;
}
for (int i = 0; i < this.m_k; i++)
{
ExampleDistancePair pair = list[i];
votes[(pair.Example.Label.Id + 1) / 2]++;
}
int max = 0;
int index = -1;
for (int i = 0; i < votes.Length; i++)
{
if (votes[i] > max)
{
index = i;
max = votes[i];
}
}
iResult = index * 2 - 1;
return(iResult);
}