public LVQ3x(List <float[]>[] trainData, List <float[]>[] testData, float learningRate, int codebookVectorCount, int iterationCount, string name, float window, float breaker, float decay)
: base(trainData, testData, learningRate, codebookVectorCount, iterationCount, name, decay)
{
s = (1 - window) / (1 + window);
this.breaker = breaker;
distances = new CodebookDistance[classCount * codebookVectorCount];
}
protected override void Train(float[] data, int trueClassIndex)
{
for (int i = 0; i < classCount; i++)
{
for (int j = 0; j < codebookVectorCount; j++)
{
distances[i * codebookVectorCount + j] = new CodebookDistance(i, j, Distance(codebookVector[i, j], data), trueClassIndex == i);
}
}
Array.Sort(distances, (d1, d2) => d1.distance.CompareTo(d2.distance));
float min = float.MaxValue;
float max = float.MinValue;
for (int i = 0; i < classCount; i++)
{
minDistance[i] = distances[i];
if (max < distances[i].distance)
{
max = distances[i].distance;
}
if (min > distances[i].distance)
{
min = distances[i].distance;
}
}
if (min / max > s)
{
bool sameClass = true;
for (int i = 0; i < classCount; i++)
{
if (!minDistance[i].trueClass)
{
sameClass = false;
break;
}
}
for (int i = 0; i < classCount; i++)
{
if (!sameClass)
{
if (minDistance[i].trueClass)
{
for (int j = 0; j < mnistSize; j++)
{
codebookVector[minDistance[i].classIndex, minDistance[i].codebookIndex][j] += learningRate * (data[j] - codebookVector[minDistance[i].classIndex, minDistance[i].codebookIndex][j]);
}
}
else
{
for (int j = 0; j < mnistSize; j++)
{
codebookVector[minDistance[i].classIndex, minDistance[i].codebookIndex][j] -= learningRate * (data[j] - codebookVector[minDistance[i].classIndex, minDistance[i].codebookIndex][j]);
}
}
}
else
{
for (int j = 0; j < mnistSize; j++)
{
codebookVector[minDistance[i].classIndex, minDistance[i].codebookIndex][j] += breaker * learningRate * (data[j] - codebookVector[minDistance[i].classIndex, minDistance[i].codebookIndex][j]);
}
}
}
}
}
