/// <summary>
/// Leave-one-out optimization
/// </summary>
public void Train_LOO(double hMin, double hMax, double step)
{
var hOpt = double.MaxValue;
var minErrCnt = int.MaxValue;
for (double h = hMin; h <= hMax; h += step)
{
var errCnt = 0;
m_H = h;
for (int i = 0; i < TrainingSample.Count; i++)
{
var pData = TrainingSample.ElementAt(i);
using (var mask = this.ApplySampleMask((p, c, idx) => idx != i))
{
var cls = this.Classify(pData.Key);
if (cls != pData.Value)
{
errCnt++;
}
}
}
if (errCnt < minErrCnt)
{
minErrCnt = errCnt;
hOpt = h;
}
}
m_H = hOpt;
}
/// <summary>
/// Leave-one-out optimization
/// </summary>
public void Train_LOO(int?minK = null, int?maxK = null)
{
if (!minK.HasValue || minK.Value < 1)
{
minK = 1;
}
if (!maxK.HasValue || maxK.Value > TrainingSample.Count)
{
maxK = TrainingSample.Count;
}
var kOpt = int.MaxValue;
var minErrCnt = int.MaxValue;
for (int k = minK.Value; k <= maxK.Value; k++)
{
var errCnt = 0;
m_K = k;
for (int i = 0; i < TrainingSample.Count; i++)
{
var pData = TrainingSample.ElementAt(i);
using (var mask = this.ApplySampleMask((p, c, idx) => idx != i))
{
var cls = this.Classify(pData.Key);
if (cls != pData.Value)
{
errCnt++;
}
}
}
if (errCnt < minErrCnt)
{
minErrCnt = errCnt;
kOpt = k;
}
}
m_K = kOpt;
}