private void CanItLearnRulesWith(IList<IMLDataPair> inputData, IList<IMLDataPair> verfData, int hiddenLayerCount, int neuronCount, IActivationFunction actFunc, double learnRate, double momentum, int batchSize, int maxEpochs)
{
var model = new DbModel();
var funcName = actFunc.GetType().Name;
var tdCount = inputData.Count();
if (model.TicTacToeResult.Any(r => r.HiddenLayerCount == hiddenLayerCount &&
r.NeuronPerLayercount == neuronCount &&
r.ActivationFunction == funcName &&
r.LearningRate == learnRate &&
r.BatchSize == batchSize &&
r.Momentum == momentum &&
r.Name == Name &&
r.Epochs == maxEpochs &&
r.TrainingDataCount == tdCount))
return;
var nn = CreateNetwork(inputData, hiddenLayerCount, neuronCount, actFunc);
var train = new Backpropagation(nn, new BasicMLDataSet(inputData), learnRate, momentum);
train.BatchSize = batchSize;
int epoch = 1;
do
{
train.Iteration();
epoch++;
} while (epoch < maxEpochs);
int good = verfData.Count(verf => { var output = nn.Compute(verf.Input); return Enumerable.Range(0, 9).All(i => Math.Round(output[i]) == Math.Round(verf.Ideal[i])); });
int bad = VerfDataCount - good;
var result = new TicTacToeResult()
{
HiddenLayerCount = hiddenLayerCount,
NeuronPerLayercount = neuronCount,
ActivationFunction = funcName,
Bad = bad,
Good = good,
TrainingDataCount = tdCount,
Momentum = momentum,
LearningRate = learnRate,
BatchSize = batchSize,
Epochs = epoch,
Error = train.Error,
Name = Name,
};
model.TicTacToeResult.Add(result);
model.SaveChanges();
}
