public Tuple <Matrix <float>, Vector <float> > BuildData(List <TrainingInstance> trainingInstances)
{
IState[] states = trainingInstances.Select(x => x.State).ToArray();
double[] pathCosts = trainingInstances.Select(x => x.Response).ToArray();
Matrix <float> trainingData = DenseMatrix.Create(states.Length, numInputs, 0);
for (int r = 0; r < trainingData.RowCount; r++)
{
IState state = states[r];
byte[] stateArr = state.Arr;
multHeuristic.H(state);
for (int i = 0; i < multHeuristic.HArr.Length; i++)
{
trainingData[r, i] = multHeuristic.HArr[i] / multHeuristic.Scale[i];
}
float maxOutOfPlace = 0;
for (int i = 0; i < Global.SIZE; i++)
{
byte val = stateArr[i];
int diff = (val > i ? val - i : i - val);
if (diff > 0 && val > maxOutOfPlace)
{
maxOutOfPlace = val;
}
}
trainingData[r, multHeuristic.HArr.Length] = stateArr[middleTokenIndex] == middleTokenIndex ? 1 : 0;
trainingData[r, multHeuristic.HArr.Length + 1] = maxOutOfPlace / Global.SIZE;
}
Vector <float> response = DenseVector.Create(pathCosts.Length, 0);
for (int i = 0; i < pathCosts.Length; i++)
{
double y = pathCosts[i];
if (responseFunc != null)
{
y = responseFunc(y);
}
response[i] = (float)y;
}
return(new Tuple <Matrix <float>, Vector <float> >(trainingData, response));
}
public Tuple <Matrix <float>, Vector <float> > BuildData(List <TrainingInstance> trainingInstances)
{
IState[] states = trainingInstances.Select(x => x.State).ToArray();
double[] pathCosts = trainingInstances.Select(x => x.Response).ToArray();
Matrix <float> trainingData = DenseMatrix.Create(states.Length, numInputs, 0);
for (int r = 0; r < trainingData.RowCount; r++)
{
IState state = states[r];
byte[] stateArr = state.Arr;
multHeuristic.H(state);
for (int i = 0; i < multHeuristic.HArr.Length; i++)
{
trainingData[r, i] = multHeuristic.HArr[i] / multHeuristic.Scale[i];
}
trainingData[r, multHeuristic.HArr.Length] = (float)state.Op.Vals[0] / Global.SIZE;
}
Vector <float> response = DenseVector.Create(pathCosts.Length, 0);
for (int i = 0; i < pathCosts.Length; i++)
{
double y = pathCosts[i];
if (responseFunc != null)
{
y = responseFunc(y);
}
response[i] = (float)y;
}
return(new Tuple <Matrix <float>, Vector <float> >(trainingData, response));
}
public virtual int H(IState state, bool verbose = false)
{
multHeuristic.H(state);
return(multHeuristic.HArr.Max());
}
