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]);
}
}
public TicTacToeGenetic(FeedforwardNetwork network,
bool reset, int populationSize,
double mutationPercent, double percentToMate,
Type opponent)
{
this.setOpponent(opponent);
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.Chromosomes = new TicTacToeChromosome[this.PopulationSize];
for (int i = 0; i < this.Chromosomes.Length; i++)
{
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TicTacToeChromosome c = new TicTacToeChromosome(this,
chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
/// <summary>
/// Construct a genetic algorithm for a neural network that uses training sets.
/// </summary>
/// <param name="network">The neural network.</param>
/// <param name="reset">Should each neural network be reset to random values.</param>
/// <param name="input">The input training set.</param>
/// <param name="ideal">The ideal values for the input training set.</param>
/// <param name="populationSize">The initial population size.</param>
/// <param name="mutationPercent">The mutation percentage.</param>
/// <param name="percentToMate">The percentage of the population allowed to mate.</param>
public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network,
bool reset, double[][] input,
double[][] ideal, int populationSize,
double mutationPercent, double percentToMate)
{
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.input = input;
this.ideal = ideal;
this.Chromosomes = new TrainingSetNeuralChromosome[this.PopulationSize];
for (int i = 0; i < this.Chromosomes.Length; i++)
{
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TrainingSetNeuralChromosome c = new TrainingSetNeuralChromosome(
this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
/// <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]);
}
}
public static FeedforwardNetwork createNetwork()
{
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(9));
network
.AddLayer(new FeedforwardLayer(NeuralTicTacToe.NEURONS_HIDDEN_1));
if (NeuralTicTacToe.NEURONS_HIDDEN_2 > 0)
{
network.AddLayer(new FeedforwardLayer(
NeuralTicTacToe.NEURONS_HIDDEN_2));
}
network.AddLayer(new FeedforwardLayer(1));
network.Reset();
return(network);
}
public static FeedforwardNetwork CreateNetwork()
{
ActivationFunction threshold = new ActivationSigmoid();
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(threshold, Config.INPUT_SIZE));
network.AddLayer(new FeedforwardLayer(threshold,
Config.NEURONS_HIDDEN_1));
if (Config.NEURONS_HIDDEN_2 > 0)
{
network.AddLayer(new FeedforwardLayer(threshold,
Config.NEURONS_HIDDEN_2));
}
network.AddLayer(new FeedforwardLayer(threshold, Config.OUTPUT_SIZE));
network.Reset();
return(network);
}
public TicTacToeGenetic(FeedforwardNetwork network,
bool reset, int populationSize,
double mutationPercent, double percentToMate,
Type opponent)
{
this.setOpponent(opponent);
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.Chromosomes = new TicTacToeChromosome[this.PopulationSize];
var rangePartitioner = Partitioner.Create(0, this.Chromosomes.Length);
//Parallel.ForEach(rangePartitioner, (range, loopState) =>
{
for (int i = 0; i < Chromosomes.Length; i++)
{
Console.WriteLine("step: " + i);
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TicTacToeChromosome c = new TicTacToeChromosome(this,
chromosomeNetwork);
c.UpdateGenes();
lock (_locker)
{
SetChromosome(i, c);
}
}
};
Console.WriteLine("PreEpoch # Error:" + getScore());
SortChromosomes();
}
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]);
}
}
private void Entrenar(int red, int pre)
{
string[] patrones = new string[ejemplos];
switch (red)
{
case 1:
for (int i = 0; i < ejemplos; i++)
{
if (pre == 0)
{
patrones[i] = pre1[i, 1];
}
else
{
patrones[i] = pos1[i, 1];
}
}
break;
case 2:
for (int i = 0; i < ejemplos; i++)
{
if (pre == 0)
{
patrones[i] = pre2[i, 1];
}
else
{
patrones[i] = pos2[i, 1];
}
}
break;
case 3:
for (int i = 0; i < ejemplos; i++)
{
if (pre == 0)
{
patrones[i] = pre3[i, 1];
}
else
{
patrones[i] = pos3[i, 1];
}
}
break;
case 4:
for (int i = 0; i < ejemplos; i++)
{
if (pre == 0)
{
patrones[i] = pre4[i, 1];
}
else
{
patrones[i] = pos4[i, 1];
}
}
break;
case 5:
for (int i = 0; i < ejemplos; i++)
{
if (pre == 0)
{
patrones[i] = pre5[i, 1];
}
else
{
patrones[i] = pos5[i, 1];
}
}
break;
}
Double[][] entrada = new double[ejemplos + 3][];
double[] matriz = new double[30];
double[] matriz2 = new double[30];
double[] matriz3 = new double[30];
//Creamos las matrices de los patrones
for (int i = 0; i < ejemplos; i++)
{
double[] matrix = new double[30];
for (int o = 0; o < 30; o++)
{
matrix[o] = double.Parse(patrones[i][o].ToString());
}
entrada[i] = matrix;
}
//Creamos 3 matrices orientativas
bool control = true;
for (int i = 0; i < 30; i++)
{
matriz3[i] = 0;
if (control)
{
matriz[i] = 1;
matriz2[i] = 0;
control = false;
}
else
{
matriz[i] = 0;
matriz2[i] = 1;
control = true;
}
}
entrada[ejemplos] = matriz;
entrada[ejemplos + 1] = matriz2;
entrada[ejemplos + 2] = matriz3;
//Creamos la matriz ideal
Double[][] ideal = new double[ejemplos + 3][];
for (int i = 0; i < ejemplos; i++)
{
ideal[i] = new double[] { 1 }
}
;
for (int i = ejemplos; i < ejemplos + 3; i++)
{
ideal[i] = new double[] { 0 }
}
;
//COMENZAMOS A CREAR LA RED NEURONAL!!!!
FeedforwardNetwork neurosis = new FeedforwardNetwork();
neurosis.AddLayer(new FeedforwardLayer(30));
neurosis.AddLayer(new FeedforwardLayer(30));
neurosis.AddLayer(new FeedforwardLayer(1));
neurosis.Reset();
Train entrenador = new HeatonResearchNeural.Feedforward.Train.Backpropagation.Backpropagation
(neurosis, entrada, ideal, 0.09, 0.9);
for (int e = 0; e < 1000; e++)
{
Console.WriteLine("Error: " + entrenador.Error);
entrenador.Iteration();
if (entrenador.Error < 0.012)
{
break;
}
}
redes[red - 1, pre] = neurosis;
}
