public double Run(KLearnData sample)
{
double error = 0.0;
_Network.Compute(sample._Input);
int winner = _Network.GetWinner();
// get layer of the network
KLayer layer = _Network[0];
layer[winner].AddSymbol(sample._Symbol);
// update weight of the winner only
// check learning radius
if (_LearningRadius == 0)
{
KNeuron neuron = layer[winner];
for (int i = 0, n = neuron._Weights.Length; i < n; i++)
{
neuron[i] += (sample._Input[i] - neuron[i]) * 0.01;
}
}
else
{
// winner's X and Y
int wx = winner % _Width;
int wy = winner / _Width;
// walk through all neurons of the layer
for (int j = 0, m = layer._Neurons.Length; j < m; j++)
{
KNeuron neuron = layer[j];
int dx = (j % _Width) - wx;
int dy = (j / _Width) - wy;
// update factor ( Gaussian based )
double factor = Math.Exp(-(double)(dx * dx + dy * dy) / _SquaredRadius2);
// update weight of the neuron
for (int i = 0, n = neuron._Weights.Length; i < n; i++)
{
// calculate the error
double e = (sample._Input[i] - neuron[i]) * factor;
error += Math.Abs(e);
// update weight
neuron[i] += e * _LearningRate;
}
}
}
return 0;
}
public static KLearnData[] GetLearnData()
{
double[,] dumyData = new double[row,col]
// DISTANCE,EA_ATTACK,EA_STAND,EA_NEAR,EA_DODGE,MA_DODGE,MA_ATTACK,MA_STAND,MA_NEAR,MA_FAR , OUTPUT
{ {0, 1 , 0 , 0 , 0 , 1 , 0 , 0 , 0 , 0 ,(double) ACTION.DODGE },
{0.1, 0 , 1 , 0 , 0 , 0 , 1 , 0 , 0 , 0 ,(double) ACTION.ATTACK },
{0.2, 0 , 1 , 0 , 0 , 0 , 1 , 0 , 0 , 0 ,(double) ACTION.ATTACK },
{0.4, 0 , 1 , 0 , 0 , 0 , 0 , 0 , 1 , 0 ,(double) ACTION.ATTACK },
};
KLearnData[] data = new KLearnData[row];
for (int j = 0; j < row; j++)
{
data[j] = new KLearnData();
int size = col - 1;
data[j]._Input = new double[size];
for (int i = 0; i < col; i++)
{
if (i == col - 1)
{
int na = (int) dumyData[j, i];
data[j]._Symbol = ((ACTION)na).ToString();
}
else
{
data[j]._Input[i] = dumyData[j, i];
}
}
}
return data;
}
static public KLearnData[] GetLearnData()
{
double[,] dumyData = new double[row, col]
// DISTANCE,EA_ATTACK,EA_STAND,EA_NEAR,EA_DODGE,MA_DODGE,MA_ATTACK,MA_STAND,MA_NEAR,MA_FAR , OUTPUT
{
{ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, (double)ACTION.DODGE },
{ 0.1, 0, 1, 0, 0, 0, 1, 0, 0, 0, (double)ACTION.ATTACK },
{ 0.2, 0, 1, 0, 0, 0, 1, 0, 0, 0, (double)ACTION.ATTACK },
{ 0.4, 0, 1, 0, 0, 0, 0, 0, 1, 0, (double)ACTION.ATTACK },
};
KLearnData[] data = new KLearnData[row];
for (int j = 0; j < row; j++)
{
data[j] = new KLearnData();
int size = col - 1;
data[j]._Input = new double[size];
for (int i = 0; i < col; i++)
{
if (i == col - 1)
{
int na = (int)dumyData[j, i];
data[j]._Symbol = ((ACTION)na).ToString();
}
else
{
data[j]._Input[i] = dumyData[j, i];
}
}
}
return(data);
}
public void AddData(InputData input,OUTPUT output )
{
StringBuilder s = new StringBuilder();
s.Append("[");
int c = 0;
foreach (var item in input._Input)
{
switch ((INPUT)c)
{
case INPUT.ES_STAND:
case INPUT.MS_STAND:
case INPUT.MS_STAND2:
case INPUT.MS_STAND3:
s.Append("[");
s.Append(item.ToString());
break;
case INPUT.ES_DODGE:
case INPUT.MS_DODGE:
case INPUT.MS_DODGE2:
case INPUT.MS_DODGE3:
s.Append(item.ToString());
s.Append("]");
break;
default:
s.Append(item.ToString());
break;
}
c++;
}
s.Append("] [");
s.Append(output.ToString());
s.Append("]");
_form.listBox1.Items.Insert(0,s.ToString());
KLearnData data = new KLearnData();
data._Input = input._Input;
data._Symbol = output.ToString();
_Learning.Run(data);
}
public double Run(KLearnData sample)
{
double error = 0.0;
_Network.Compute(sample._Input);
int winner = _Network.GetWinner();
// get layer of the network
KLayer layer = _Network[0];
layer[winner].AddSymbol(sample._Symbol);
// update weight of the winner only
// check learning radius
if (_LearningRadius == 0)
{
KNeuron neuron = layer[winner];
for (int i = 0, n = neuron._Weights.Length; i < n; i++)
{
neuron[i] += (sample._Input[i] - neuron[i]) * 0.01;
}
}
else
{
// winner's X and Y
int wx = winner % _Width;
int wy = winner / _Width;
// walk through all neurons of the layer
for (int j = 0, m = layer._Neurons.Length; j < m; j++)
{
KNeuron neuron = layer[j];
int dx = (j % _Width) - wx;
int dy = (j / _Width) - wy;
// update factor ( Gaussian based )
double factor = Math.Exp(-(double)(dx * dx + dy * dy) / _SquaredRadius2);
// update weight of the neuron
for (int i = 0, n = neuron._Weights.Length; i < n; i++)
{
// calculate the error
double e = (sample._Input[i] - neuron[i]) * factor;
error += Math.Abs(e);
// update weight
neuron[i] += e * _LearningRate;
}
}
}
return(0);
}
public double RunEpoch(KLearnData[] input)
{
double error = 0.0;
// walk through all training samples
foreach (KLearnData sample in input)
{
error += Run(sample);
}
// return summary error
return error;
}
