/**
* Helper function for classify. Recursively compares all weight vectors to
* the input.
*/
protected double findMinDist(int[] res)
{
double minDist = Double.PositiveInfinity;
Object [] data = getWeights().toArray();
int winner = 0;
for (int x = 0; x < data.Length; x++)
{
if (inputMetric.getDistance((double[])data[x], getInput()) < minDist)
{
minDist = inputMetric.getDistance((double[])data[x], getInput());
winner = x;
}
}
Array.Copy(getWeights().getPosition(winner), res, res.Length);
return(minDist);
}
public void updateBuffer(double[] data)
{
// calculate the distance from the new input to all inputs in the buffer
// find the largest distance between the input and any entry in the
// buffer
// as well as the entry in the buffer that is closest to the input
int minDistIndex = 0;
int index = 0;
double minDist = Double.MaxValue;
double maxNewDist = 0;
foreach (double[] n in buffer)
{
double tmp = bufferMetric.getDistance(data, n);
if (tmp < minDist)
{
// closest buffer entry so far
minDist = tmp;
minDistIndex = index;
}
if (tmp > maxNewDist)
{
// largest distance so far
maxNewDist = tmp;
}
index++;
}
// check if we've received a 'distant' input
if (maxNewDist > maxDiameter)
{
// add the new input to the buffer
maxDiameter = maxNewDist;
buffer.Add((double[])data.Clone());
// keep the buffer to the set size...
if (buffer.Count > (data.GetLength(0) + 1))
{
// ...by removing the buffer entry that is closest to the new
// input
buffer.RemoveAt(minDistIndex);
}
}
}
