static void Main(string[] args)
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
int[] layout = ReadLayout(args[1]);
var activationFunctions = new ActivationFunction[layout.Length - 1];
for (int i = 0; i < layout.Length - 1; ++i)
{
activationFunctions[i] = Sigmoid();
}
FFANN ffann = new FFANN(layout, activationFunctions);
Console.WriteLine(ffann.WeightCount());
int n = ffann.WeightCount();
double[] weights = new double[n];
Random rnd = new Random();
for (int i = 0; i < n; ++i)
{
weights[i] = rnd.NextDouble();
}
ffann.SetWeights(weights);
Dataset dataset = new PointDataset(layout[0] / 2);
dataset.Read(args[0]);
Console.WriteLine(ffann.CalculateError(dataset));
Backpropagation train = new Backpropagation(ffann, 0.2, dataset);
train.MaxIteration = 5000;
train.MaxError = 1e-6;
int batchSize;
switch (args[2])
{
case "1":
batchSize = dataset.Size;
break;
case "2":
batchSize = 1;
break;
default:
batchSize = 20;
break;
}
train.Train(batchSize);
Application.Run(new Recognition(ffann));
}
static void Main(string[] args)
{
int[] layout = { 1, 6, 1 };
ActivationFunction[] activationFunctions = { Sigmoid(), Sigmoid() };
FFANN ffann = new FFANN(layout, activationFunctions);
int n = ffann.WeightCount();
double[] weights = new double[n];
Random rnd = new Random();
for (int i = 0; i < n; ++i)
{
weights[i] = rnd.NextDouble();
}
ffann.SetWeights(weights);
Dataset dataset = new Dataset();
dataset.Read("dummyData.txt");
Console.WriteLine(ffann.CalculateError(dataset));
Backpropagation train = new Backpropagation(ffann, 0.1, dataset);
train.MaxIteration = 100000;
train.MaxError = 1e-6;
train.Train(1);
for (int i = 0; i < dataset.Size; ++i)
{
Console.WriteLine(ffann.GetOutput(dataset.GetInput(i))[0].ToString("0.0000") + " " + dataset.GetOutput(i)[0]);
}
}
static EliminationGA()
{
Ffann = new FFANN(new[] { 2, 8, 3 }, new[] { ActivationFunctions.Sigmoid(), ActivationFunctions.Sigmoid() });
ChromosomeSize = Ffann.WeightCount();
Evaluator = new Evaluator(DataPath, Ffann);
Selection = new TournamentSelection(TournamentSize);
var discreteRecombination = new DiscreteRecombination();
var simpleArithmetic = new SimpleArithmeticRecombination();
var singleArithmetic = new SingleArithmeticRecombination();
Crossover = new MixedCrossover(discreteRecombination, simpleArithmetic, singleArithmetic);
var gausMutation = new GausMutation(Sigma1, P1);
var newGausMutation = new NewGausMutation(Sigma2, P2);
Mutation = new MixedMutation(gausMutation, newGausMutation, P);
}