/// <summary>
/// Takes a state of the board and returns probability of
/// first player win.
/// </summary>
/// <param name="vector"></param>
/// <returns></returns>
public double EvaluateState(Board board)
{
//IMLData input = ANNAdapter.Adapt(board);
IMLData input = ANNAdapter.Adapt192(board);
IMLData output = network.Compute(input);
return(output[0]);
}
/// <summary>
/// Trains state inside neural network to generate new value function.
/// </summary>
/// <param name="currentState"></param>
/// <param name="v"></param>
public void Train(Board board, double v)
{
BasicMLDataSet trainingSet = new BasicMLDataSet();
BasicMLData ideal = new BasicMLData(1);
ideal[0] = v;
//trainingSet.Add(ANNAdapter.Adapt(board), ideal);
trainingSet.Add(ANNAdapter.Adapt192(board), ideal);
IMLTrain train = new ResilientPropagation(network, trainingSet);
train.Iteration();
}