/// <summary>
/// The constructor
/// </summary>
public NNSim()
{
this.xor_template = new TrainingTemplate();
xor_template.addTrainingRow(new TrainingRow(new List <double> {
0, 0
}, new List <double> {
0
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
0, 1
}, new List <double> {
1
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
1, 0
}, new List <double> {
1
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
1, 1
}, new List <double> {
0
}));
this.input1Neuron = new Neuron("input1", 0, 1);
this.input2Neuron = new Neuron("input2", 0, 1);
this.hidden1Neuron = new Neuron("hidden1", 0, 1);
this.hidden2Neuron = new Neuron("hidden2", 0, 1);
this.outputNeuron = new Neuron("output", 0, 1);
input1Neuron.fireRule = new SigmoidFireRule();
input2Neuron.fireRule = new SigmoidFireRule();
hidden1Neuron.fireRule = new SigmoidFireRule();
hidden1Neuron.fireRule = new SigmoidFireRule();
outputNeuron.fireRule = new SigmoidFireRule();
input1Neuron.addNewInput("network_in_1", 1, 1);
input2Neuron.addNewInput("network_in_2", 1, 1);
hidden1Neuron.inputs.Add(input1Neuron.output);
hidden1Neuron.inputs.Add(input2Neuron.output);
hidden2Neuron.inputs.Add(input1Neuron.output);
hidden2Neuron.inputs.Add(input2Neuron.output);
outputNeuron.inputs.Add(hidden1Neuron.output);
outputNeuron.inputs.Add(hidden2Neuron.output);
errors = new double[4];
}
private void button1_Click(object sender, EventArgs e)
{
neuron = new Neuron("AND-Neuron", 0, 2);
neuron.addNewInput("input1", 0, 0);
neuron.addNewInput("input2", 0, 0);
PerceptronNetwork pn = new PerceptronNetwork(neuron);
TrainingTemplate andTemplate = new TrainingTemplate("AND Template");
andTemplate.addTrainingRow(new TrainingRow(new List <double> {
0, 0
}, new List <double> {
0
}));
andTemplate.addTrainingRow(new TrainingRow(new List <double> {
0, 1
}, new List <double> {
0
}));
andTemplate.addTrainingRow(new TrainingRow(new List <double> {
1, 0
}, new List <double> {
0
}));
andTemplate.addTrainingRow(new TrainingRow(new List <double> {
1, 1
}, new List <double> {
1
}));
TrainingTemplate orTemplate = new TrainingTemplate("OR Template");
orTemplate.addTrainingRow(new TrainingRow(new List <double> {
0, 0
}, new List <double> {
0
}));
orTemplate.addTrainingRow(new TrainingRow(new List <double> {
0, 1
}, new List <double> {
1
}));
orTemplate.addTrainingRow(new TrainingRow(new List <double> {
1, 0
}, new List <double> {
1
}));
orTemplate.addTrainingRow(new TrainingRow(new List <double> {
1, 1
}, new List <double> {
1
}));
TrainingTemplate xorTemplate = new TrainingTemplate("XOR Template");
xorTemplate.addTrainingRow(new TrainingRow(new List <double> {
0, 0
}, new List <double> {
0
}));
xorTemplate.addTrainingRow(new TrainingRow(new List <double> {
0, 1
}, new List <double> {
1
}));
xorTemplate.addTrainingRow(new TrainingRow(new List <double> {
1, 0
}, new List <double> {
1
}));
xorTemplate.addTrainingRow(new TrainingRow(new List <double> {
1, 1
}, new List <double> {
0
}));
templatesList = new List <TrainingTemplate>();
ErrorHistory errorProg = new ErrorHistory();
double error = pn.train(xorTemplate, 100, errorProg);
labelWeight1.Text = neuron.inputs[0].weight.ToString("N3");
labelWeight2.Text = neuron.inputs[1].weight.ToString("N3");
labelError.Text = error.ToString("N3");
for (int X = 0; X < errorProg.errorPoints.Count; X++)
{
chart1.Series["Error"].Points.AddXY(X, errorProg.errorPoints[X]);
}
//chart1.DataBind(errorProg);
}
static void Main(string[] args)
{
//We create the training template
TrainingTemplate xor_template = new TrainingTemplate();
xor_template.addTrainingRow(new TrainingRow(new List <double> {
0, 0
}, new List <double> {
0
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
0, 1
}, new List <double> {
1
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
1, 0
}, new List <double> {
1
}));
xor_template.addTrainingRow(new TrainingRow(new List <double> {
1, 1
}, new List <double> {
0
}));
//We create the network
SimpleNetwork sn = new SimpleNetwork();
// We create the neurons
Neuron input1Neuron = new Neuron("input1", 0, 1);
Neuron input2Neuron = new Neuron("input2", 0, 1);
Neuron hidden1Neuron = new Neuron("hidden1", 0, 1);
Neuron hidden2Neuron = new Neuron("hidden2", 0, 1);
Neuron outputNeuron = new Neuron("output", 0, 1);
//We asign them Sigmoid fire functions
input1Neuron.fireRule = new SigmoidFireRule();
input2Neuron.fireRule = new SigmoidFireRule();
hidden1Neuron.fireRule = new SigmoidFireRule();
hidden1Neuron.fireRule = new SigmoidFireRule();
outputNeuron.fireRule = new SigmoidFireRule();
// We bind them together
input1Neuron.addNewInput("network_in_1", 1, 1);
input2Neuron.addNewInput("network_in_2", 1, 1);
hidden1Neuron.inputs.Add(input1Neuron.output);
hidden1Neuron.inputs.Add(input2Neuron.output);
hidden2Neuron.inputs.Add(input1Neuron.output);
hidden2Neuron.inputs.Add(input2Neuron.output);
outputNeuron.inputs.Add(hidden1Neuron.output);
// We put them into layers
sn.inputLayer.neurons.Add(input1Neuron);
sn.inputLayer.neurons.Add(input2Neuron);
sn.hiddenLayer.neurons.Add(hidden1Neuron);
sn.hiddenLayer.neurons.Add(hidden2Neuron);
sn.outputLayer.neurons.Add(outputNeuron);
// We train
double error = sn.train(xor_template, 5000, new ErrorHistory());
Console.WriteLine(error);
Console.ReadKey();
}