static void Main(string[] args)
{
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(2));
network.AddLayer(new FeedforwardLayer(3));
network.AddLayer(new FeedforwardLayer(1));
network.Reset();
// train the neural network
TrainingSetNeuralGeneticAlgorithm train = new TrainingSetNeuralGeneticAlgorithm(
network, true, XOR_INPUT, XOR_IDEAL, 5000, 0.1, 0.25);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error);
epoch++;
} while ((epoch < 5000) && (train.Error > 0.001));
network = train.Network;
// test the neural network
Console.WriteLine("Neural Network Results:");
for (int i = 0; i < XOR_IDEAL.Length; i++)
{
double [] actual = network.ComputeOutputs(XOR_INPUT[i]);
Console.WriteLine(XOR_INPUT[i][0] + "," + XOR_INPUT[i][1]
+ ", actual=" + actual[0] + ",ideal=" + XOR_IDEAL[i][0]);
}
}
/// <summary>
/// Create, train and use a neural network for XOR.
/// </summary>
/// <param name="args">Not used</param>
static void Main(string[] args)
{
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(2));
network.AddLayer(new FeedforwardLayer(3));
network.AddLayer(new FeedforwardLayer(1));
network.Reset();
// train the neural network
Train train = new HeatonResearchNeural.Feedforward.Train.Backpropagation.Backpropagation(network, XOR_INPUT, XOR_IDEAL,
0.7, 0.9);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error);
epoch++;
} while ((epoch < 5000) && (train.Error > 0.001));
// test the neural network
Console.WriteLine("Neural Network Results:");
for (int i = 0; i < XOR_IDEAL.Length; i++)
{
double[] actual = network.ComputeOutputs(XOR_INPUT[i]);
Console.WriteLine(XOR_INPUT[i][0] + "," + XOR_INPUT[i][1]
+ ", actual=" + actual[0] + ",ideal=" + XOR_IDEAL[i][0]);
}
}
private Suit ScanSuit(Bitmap bmp)
{
int height = bmp.Height;
int width = bmp.Width;
var lista = new List <double>();
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
var c = bmp.GetPixel(x, y);
if (c.R < 200)
{
lista.Add(1.0);
}
else
{
lista.Add(0.0);
}
}
}
double[] saida = NetworkSuits.ComputeOutputs(lista.ToArray());
double comp = 0.0;
int pos = 0;
for (int i = 0; i < saida.Length; i++)
{
if (saida[i] > 0.3 && saida[i] > comp)
{
comp = saida[i];
pos = i + 1;
}
}
if (pos == 1)
{
return(Suit.Hearts);
}
if (pos == 2)
{
return(Suit.Spades);
}
if (pos == 3)
{
return(Suit.Diamonds);
}
if (pos == 4)
{
return(Suit.Clubs);
}
return(Suit.NOT_RECOGNIZED);
}
/// <summary>
/// Scans rank of face cards
/// </summary>
/// <param name="cardImage"></param>
/// <returns></returns>
private Rank ScanFaceRank(Bitmap bmp)
{
int height = bmp.Height;
int width = bmp.Width;
var lista = new List <double>();
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
var c = bmp.GetPixel(x, y);
if (c.R < 200)
{
lista.Add(1.0);
}
else
{
lista.Add(0.0);
}
}
}
double comp = 0.0;
int pos = 0;
double[] saida = NetworkLetra.ComputeOutputs(lista.ToArray());
for (int i = 0; i < saida.Length; i++)
{
if (saida[i] > 0.3 && saida[i] > comp)
{
comp = saida[i];
pos = i + 1;
}
}
if (pos == 1)
{
return(Rank.Ace);
}
if (pos == 2)
{
return(Rank.Jack);
}
if (pos == 3)
{
return(Rank.King);
}
if (pos == 4)
{
return(Rank.Queen);
}
return(Rank.NOT_RECOGNIZED);
}
static void Main(string[] args)
{
input = GetInput();
label = GetLabel();
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(4));
network.AddLayer(new FeedforwardLayer(5));
network.AddLayer(new FeedforwardLayer(1));
network.Reset();
// train the neural network
TrainingSetNeuralGeneticAlgorithm train = new TrainingSetNeuralGeneticAlgorithm(
network, false, input, label, 5000, 0.1, 0.25);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error);
epoch++;
} while ((epoch < 5000) && (train.Error > 0.001));
network = train.Network;
// test the neural network
Console.WriteLine("Neural Network Results:");
for (int i = 0; i < label.Length; i++)
{
double[] actual = network.ComputeOutputs(input[i]);
Console.WriteLine(input[i][0] + "," + input[i][1]
+ ", actual=" + actual[0] + ",ideal=" + label[i][0]);
}
}
/// <summary>
/// Scans rank of card and returns result.
/// For recognizing rank, counts suits on image
/// </summary>
/// <param name="cardImage">Card image to be scanned</param>
/// <returns>Rank of card</returns>
private Rank ScanRank(Bitmap bmp)
{
int height = bmp.Height;
int width = bmp.Width;
var lista = new List <double>();
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
var c = bmp.GetPixel(x, y);
if (c.R < 200)
{
lista.Add(1.0);
}
else
{
lista.Add(0.0);
}
}
}
double comp = 0.0;
int pos = 0;
double[] saida = NetworkNumero.ComputeOutputs(lista.ToArray());
for (int i = 0; i < saida.Length; i++)
{
if (saida[i] > 0.5 && saida[i] > comp)
{
comp = saida[i];
pos = i + 1;
}
}
if (comp > 0.5)
{
if (pos == 1)
{
return(Rank.Two);
}
if (pos == 2)
{
return(Rank.Three);
}
if (pos == 3)
{
return(Rank.Four);
}
if (pos == 4)
{
return(Rank.Five);
}
if (pos == 5)
{
return(Rank.Six);
}
if (pos == 6)
{
return(Rank.Seven);
}
if (pos == 7)
{
return(Rank.Eight);
}
if (pos == 8)
{
return(Rank.Nine);
}
if (pos == 9)
{
return(Rank.Ten);
}
if (pos == 10)
{
return(Rank.Ace);
}
}
return(Rank.NOT_RECOGNIZED);
}
