internal double Predict(double [] validStim, int outputIndex, out double confidence)
{
List <KDTreePoint> list = new List <KDTreePoint>();
tree.Search(validStim, simplexVertices, ref list);
double result = 0.0f;
double b = 0.0;
double av = 0.0;
double scale = 4.0;
double averageRes = 0.0f;
double averageDist = 0.0f;
for (int n = 0; n < simplexVertices; n++)
{
KDTreePoint point = (KDTreePoint)list[n];
averageRes += trainTarg[(int)point.index, outputIndex];
averageDist += Math.Sqrt(point.distSquared);
}
if (averageDist != 0.0)
{
av = scale / averageDist;
}
else
{
av = scale;
}
averageRes /= (double)simplexVertices;
averageDist /= (double)simplexVertices;
double stdDevRes = 0.0f;
double stdDevDist = 0.0f;
int nSignCount = 0;
for (int n = 0; n < simplexVertices; n++)
{
KDTreePoint point = (KDTreePoint)list[n];
double dist = Math.Sqrt(point.distSquared);
b += Math.Exp(-dist * av);
double dTemp = trainTarg[(int)point.index, outputIndex];
result += Math.Exp(-dist * av) * dTemp;
double fTemp1 = dTemp - averageRes;
double fTemp2 = dist - averageDist;
stdDevRes += fTemp1 * fTemp1;
stdDevDist += fTemp2 * fTemp2;
if (dTemp >= 0.0)
{
nSignCount++;
}
}
stdDevRes = Math.Sqrt(stdDevRes / (double)simplexVertices);
stdDevDist = Math.Sqrt(stdDevDist / (double)simplexVertices);
if (nSignCount < simplexVertices / 2)
{
nSignCount = simplexVertices - nSignCount;
}
double fSignCount = (double)nSignCount / (double)simplexVertices;
confidence = fSignCount * (1.0f - stdDevRes) * (1.0f - stdDevDist);
if (b != 0.0)
{
b = 1.0 / b;
}
else
{
return(0.0);
}
result *= (double)b;
return(result);
}
/// <summary>
/// Compares one KDTReePoint to another based on distance.
/// </summary>
/// <param name="obj"></param>
/// <returns></returns>
public int CompareTo(object obj)
{
KDTreePoint point = (KDTreePoint)obj;
return(distSquared.CompareTo(point.distSquared));
}
