public void initializeNetworks(gateValue net, double lernValue, int neurons)
{
int[] layers = new int[] { 2, 1 };
switch (net)
{
case gateValue.AND:
if (neurons != 1)
{
layers = new int[] { 2, neurons, 1 }
}
;
else
{
layers = new int[] { 2, 1 }
};
networkAND = new AdalineMPLNetworkController(layers, lernValue);
break;
case gateValue.OR:
if (neurons != 1)
{
layers = new int[] { 2, neurons, 1 }
}
;
else
{
layers = new int[] { 2, 1 }
};
networkOR = new AdalineMPLNetworkController(layers, lernValue);
break;
case gateValue.NOT:
if (neurons != 1)
{
layers = new int[] { 1, neurons, 1 }
}
;
else
{
layers = new int[] { 1, 1 }
};
networkNOT = new AdalineMPLNetworkController(layers, lernValue);
break;
case gateValue.XOR:
layers = new int[] { 2, neurons, 1 };
networkXOR = new AdalineMPLNetworkController(layers, lernValue);
break;
}
}
public double getActualErrorForTest(gateValue net)
{
AdalineMPLNetworkController network = getNetwork(net);
List <TestData> lernData = getTestData(net);
double acumlator = 0;
foreach (TestData data in lernData)
{
acumlator += getActallError(ask(net, data.x), data.res[0]);
}
return(acumlator / (double)lernData.Count);
}
public double testGate(gateValue net)
{
AdalineMPLNetworkController network = getNetwork(net);
List <TestData> lernData = getTestData(net);
double correct = 0;
foreach (TestData data in lernData)
{
if (correctFunction(ask(net, data.x), data.res[0]))
{
correct++;
}
}
return(correct / (double)(lernData.Count));
}
public string lern(gateValue net, int iterations, double error)
{
AdalineMPLNetworkController network = getNetwork(net);
List <TestData> lernData = getTestData(net);
while (iterations != 0)
{
foreach (TestData actData in lernData)
{
network.lernArr(actData.x, actData.res, error);
//network.lern(actData.x, actData.res);
}
iterations--;
}
return("Act%: " + testGate(net));
}
public string lernEnd(gateValue net, double error)
{
AdalineMPLNetworkController network = getNetwork(net);
List <TestData> lernData = getTestData(net);
double iteration = 0;
do
{
foreach (TestData actData in lernData)
{
network.lernArr(actData.x, actData.res, error);
}
iteration++;
if (iteration >= 10e4)
{
break;
}
} while (testGate(net) != 1);
//if (testGate(net) != 1)
return("iter" + iteration + " " + "Act%: " + testGate(net));
}
public double ask(gateValue net, double[] input)
{
AdalineMPLNetworkController network = getNetwork(net);
return(network.ask(input)[0]);
}