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();
}
void Process(int learnIterations, double desiredError,
ActivationNetwork net, ISupervisedLearning lerning,
double[][] input, double[][] desiredOutput,
ErrorCalculator errorCalculator)
{
for (int i = 0; i < learnIterations; i++) {
double[][] result = Compute(net, input);
double error = errorCalculator.CalculateEpoch(desiredOutput, result);
Console.WriteLine("{0}\t{1}", i, error);
if (error < desiredError) break;
lerning.RunEpoch(input, desiredOutput);
}
}
void Proc1()
{
int learnIterations = 100;
double desiredError = 0;
Model model = getModelAnd();
var activationFunc1 = new ThresholdFunction();
var activationFunc2 = new SigmoidFunction(1);
IActivationFunction activationFunc = activationFunc2;
int inputsCount = model.InputsCount;
int neuronsCount = 1;
ActivationNetwork net = new ActivationNetwork(activationFunc, inputsCount, neuronsCount);
PerceptronLearning lerning = new PerceptronLearning(net);
ErrorCalculator errorCalculator = new ErrorCalculator();
Process(learnIterations, desiredError, net, lerning,
model.input, model.desiredOutput, errorCalculator);
Console.ReadLine();
}
