static Model getModelAnd()
{
if (modelAnd == null) {
int sampleCount = 4;
int inputsCount = 2;
int outputsCount = 1;
double[][] input;
double[][] desiredOutput;
input = new double[sampleCount][];
desiredOutput = new double[sampleCount][];
for (int i = 0; i < input.Length; i++) {
input[i] = new double[inputsCount];
desiredOutput[i] = new double[outputsCount];
}
input[0][0] = 0;
input[0][1] = 0;
input[1][0] = 0;
input[1][1] = 1;
input[2][0] = 1;
input[2][1] = 0;
input[3][0] = 1;
input[3][1] = 1;
desiredOutput[0][0] = 0;
desiredOutput[1][0] = 1;
desiredOutput[2][0] = 1;
desiredOutput[3][0] = 1;
modelAnd = new Model(inputsCount, input, desiredOutput);
}
return modelAnd;
}
void Proc2(Model model)
{
int learnIterations = 100;
double desiredError = 0;
var activationFunc1 = new ThresholdFunction();
var activationFunc2 = new SigmoidFunction(1);
IActivationFunction activationFunc = activationFunc2;
int inputsCount = model.InputsCount;
int[] neuronsCount = new int[3];
neuronsCount[0] = model.InputsCount;
neuronsCount[1] = model.InputsCount * 2;
neuronsCount[2] = model.desiredOutput[0].Length;
ActivationNetwork net = new ActivationNetwork(activationFunc, inputsCount, neuronsCount);
BackPropagationLearning lerning = new BackPropagationLearning(net);
ErrorCalculator errorCalculator = new ErrorCalculator();
Process(learnIterations, desiredError, net, lerning,
model.input, model.desiredOutput, errorCalculator);
Console.ReadLine();
}
