static void Main(string[] args)
{
var dir = ConfigurationManager.AppSettings["FileDirectory"];
var dataSet = new DataSet();
dataSet.Id = 1;//int.Parse(currDir.Split('\\').Last());
var files = Directory.GetFiles(dir+"/singatures").ToList();
//files.Remove(files.Last());
IFeatureExtractor featureEngine = new RadonTransformFeatureExtractor();
foreach (var file in files)
{
dataSet.Signatures.Add(featureEngine.Extract(file));
}
double max = 0;
foreach (var st in dataSet.Signatures)
{
for (int i = 0; i < 8; i++)
{
for(int j=0; j< 21;j++)
if (max < st.Data[i, j])
max = st.Data[i, j];
}
}
foreach (var st in dataSet.Signatures)
{
for (int i = 0; i < 8; i++)
{
for (int j = 0; j < 21; j++)
st.Data[i, j] /= max;
}
}
//var network = new NeuralNetworkBp();
//network.Train(dataSet);
//var toCheck = Directory.GetFiles(dir +"/test").First();
//var check = featureEngine.Extract(toCheck);
//for (int i = 0; i < 8; i++)
//{
// for (int j = 0; j < 21; j++)
// check.Data[i, j] /= max;
//}
//network.Verify(check);
//var forgeryName = Directory.GetFiles(dir + "/forgery").First();
//var forgery = featureEngine.Extract(forgeryName);
//network.Verify(forgery);
}
public int Train(DataSet dataSet)
{
var data = dataSet.Signatures.Select(i => i.Data.Cast<double>().ToArray()).ToArray();
var dict = new Dictionary<string, double[]>();
for (int i = 0; i < data.Count(); i++)
{
dict.Add((i+1).ToString(), data[i]);
}
network = new NeuralNetwork<string>(new Layer3<string>(8*21, (int)((double)((8*21 + 4)*FirstLayerParameter)),
(int)((double)((8 * 21 + 4) * SecondLayerParameter)), 4), dict);
return 1;
}
public int Train(DataSet dataSet)
{
Network = new BasicNetwork();
Network.AddLayer(new BasicLayer(null, true, 8 * 21));
var first = ((8 * 21 + 4) * FirstLayerParameter);
Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, (int)first));
var second = ((8 * 21 + 4) * SecondLayerParameter);
Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, (int)second));
Network.AddLayer(new BasicLayer(null, false, 1));
// Network.AddLayer(new );
Network.Structure.FinalizeStructure();
Network.Reset();
//IMLData x = new BasicNeuralData();
var set = new double[dataSet.Signatures.Count + dataSet.Forgeries.Count][];
var ideal = new double[dataSet.Signatures.Count + dataSet.Forgeries.Count][];
for (int i = 0; i < dataSet.Signatures.Count; i++)
{
set[i] = dataSet.Signatures[i].Data.Cast<double>().ToArray();
ideal[i] = new double[] {1};
}
for (int i = dataSet.Signatures.Count; i < dataSet.Signatures.Count + dataSet.Forgeries.Count; i++)
{
set[i] = dataSet.Forgeries[i- dataSet.Signatures.Count].Data.Cast<double>().ToArray();
ideal[i] = new double[] { 0 };
}
IMLDataSet trainingSet = new BasicMLDataSet(set, ideal);
IMLTrain train = new ResilientPropagation(Network, trainingSet);
int epoch = 1;
var errors = new List<double>();
do
{
train.Iteration();
// Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
errors.Add(train.Error);
} while ( epoch < 10000);
train.FinishTraining();
return 1;
}
public DataSet GetTeachingDataset(string[] files, string[] forgeries = null)
{
if(forgeries == null)
forgeries = new string[0];
var dataSet = new DataSet();
foreach (var file in files)
{
dataSet.Signatures.Add(Extract(file));
}
foreach (var forgery in forgeries)
{
dataSet.Forgeries.Add(Extract(forgery));
}
//WTF is going on?
double max = 0;
foreach (var st in dataSet.Signatures)
{
for (int i = 0; i < 8; i++)
{
for (int j = 0; j < 21; j++)
if (max < st.Data[i, j])
max = st.Data[i, j];
}
}
foreach (var st in dataSet.Forgeries)
{
for (int i = 0; i < 8; i++)
{
for (int j = 0; j < 21; j++)
if (max < st.Data[i, j])
max = st.Data[i, j];
}
}
//foreach (var st in dataSet.Signatures)
//{
// for (int i = 0; i < 8; i++)
// {
// for (int j = 0; j < 21; j++)
// st.Data[i, j] /= max;
// }
//}
//foreach (var st in dataSet.Forgeries)
//{
// for (int i = 0; i < 8; i++)
// {
// for (int j = 0; j < 21; j++)
// st.Data[i, j] /= max;
// }
//}
return dataSet;
}
