private static double[][] TrainXOR(int epochs, FastCyclicNetwork network)
{
double[][] inputs = new double[][] {
new double[] { 0, 0 },
new double[] { 1, 0 },
new double[] { 0, 1 },
new double[] { 1, 1 }
};
double[][] outputs = new double[][] {
new double[] { 0 },
new double[] { 1 },
new double[] { 1 },
new double[] { 0 }
};
for (int i = 0; i < epochs; i++)
{
network.Train(inputs[i % inputs.Length], outputs[i % outputs.Length]);
}
return(inputs);
}
/// <summary>
/// Evaluate the provided IBlackBox against the XOR problem domain and return its fitness score.
/// </summary>
public FitnessInfo Evaluate(IBlackBox box)
{
double fitness = 0;
double output;
double pass = 1.0;
ISignalArray inputArr = box.InputSignalArray;
ISignalArray outputArr = box.OutputSignalArray;
_evalCount++;
// Train the network
FastCyclicNetwork net = (FastCyclicNetwork)box;
//----- Test 0,0
box.ResetState();
// Set the input values
inputArr[0] = 0.0;
inputArr[1] = 0.0;
// Activate the black box.
box.Activate();
if (!box.IsStateValid)
{ // Any black box that gets itself into an invalid state is unlikely to be
// any good, so lets just bail out here.
return(FitnessInfo.Zero);
}
// Read output signal.
output = Math.Min(1, Math.Max(0, outputArr[0]));
var output0 = output;
Debug.Assert(output >= 0.0, "Unexpected negative output.");
// Calculate this test case's contribution to the overall fitness score.
//fitness += 1.0 - output; // Use this line to punish absolute error instead of squared error.
fitness += 1.0 - (output * output);
if (output > 0.5)
{
pass = 0.0;
}
//----- Test 1,1
// Reset any black box state from the previous test case.
box.ResetState();
// Set the input values
inputArr[0] = 1.0;
inputArr[1] = 1.0;
// Activate the black box.
box.Activate();
if (!box.IsStateValid)
{ // Any black box that gets itself into an invalid state is unlikely to be
// any good, so lets just bail out here.
return(FitnessInfo.Zero);
}
// Read output signal.
output = Math.Min(1, Math.Max(0, outputArr[0]));
var output1 = output;
Debug.Assert(output >= 0.0, "Unexpected negative output.");
// Calculate this test case's contribution to the overall fitness score.
//fitness += 1.0 - output; // Use this line to punish absolute error instead of squared error.
fitness += 1.0 - (output * output);
if (output > 0.5)
{
pass = 0.0;
}
//----- Test 0,1
// Reset any black box state from the previous test case.
box.ResetState();
// Set the input values
inputArr[0] = 0.0;
inputArr[1] = 1.0;
// Activate the black box.
box.Activate();
if (!box.IsStateValid)
{ // Any black box that gets itself into an invalid state is unlikely to be
// any good, so lets just bail out here.
return(FitnessInfo.Zero);
}
// Read output signal.
output = Math.Min(1, Math.Max(0, outputArr[0]));
var output2 = output;
Debug.Assert(output >= 0.0, "Unexpected negative output.");
// Calculate this test case's contribution to the overall fitness score.
// fitness += output; // Use this line to punish absolute error instead of squared error.
fitness += 1.0 - ((1.0 - output) * (1.0 - output));
if (output <= 0.5)
{
pass = 0.0;
}
//----- Test 1,0
// Reset any black box state from the previous test case.
box.ResetState();
// Set the input values
inputArr[0] = 1.0;
inputArr[1] = 0.0;
// Activate the black box.
box.Activate();
if (!box.IsStateValid)
{ // Any black box that gets itself into an invalid state is unlikely to be
// any good, so lets just bail out here.
return(FitnessInfo.Zero);
}
// Read output signal.
output = Math.Min(1, Math.Max(0, outputArr[0]));
var output3 = output;
Debug.Assert(output >= 0.0, "Unexpected negative output.");
// Calculate this test case's contribution to the overall fitness score.
// fitness += output; // Use this line to punish absolute error instead of squared error.
fitness += 1.0 - ((1.0 - output) * (1.0 - output));
if (output <= 0.5)
{
pass = 0.0;
}
// If all four outputs were correct, that is, all four were on the correct side of the
// threshold level - then we add 10 to the fitness.
fitness += pass * 10.0;
if (fitness > 3.6)
{
Console.WriteLine("Before Nodes={5} Connections={6} [0,0]={0:N3} [1,1]={1:N3} [0,1]={2:N3} [1,0]={3:N3} fitness={4:N3}", output0, output1, output2, output3, fitness, net.HiddenCount, net.ConnectionCount);
}
fitness = 0;
net.BackpropLearningRate = 0.1;
net.Momentum = 0.9;
double[][] exampleInputs = new double[][] {
new double[] { 0, 0 },
new double[] { 0, 1 },
new double[] { 1, 0 },
new double[] { 1, 1 }
};
double[][] exampleOutputs = new double[][] {
new double[] { 0 },
new double[] { 1 },
new double[] { 1 },
new double[] { 0 }
};
net.RandomizeWeights();
for (int i = 0; i < 4000; i++)
{
net.Train(exampleInputs[i % 4], exampleOutputs[i % 4]);
}
//----- Test 0,0
box.ResetState();
// Set the input values
inputArr[0] = 0.0;
inputArr[1] = 0.0;
// Activate the black box.
box.Activate();
if (!box.IsStateValid)
{ // Any black box that gets itself into an invalid state is unlikely to be
// any good, so lets just bail out here.
return(FitnessInfo.Zero);
}
// Read output signal.
output = Math.Min(1, Math.Max(0, outputArr[0]));
output0 = output;
Debug.Assert(output >= 0.0, "Unexpected negative output.");
// Calculate this test case's contribution to the overall fitness score.
//fitness += 1.0 - output; // Use this line to punish absolute error instead of squared error.
fitness += 1.0 - (output * output);
if (output > 0.5)
{
pass = 0.0;
}
//----- Test 1,1
// Reset any black box state from the previous test case.
box.ResetState();
// Set the input values
inputArr[0] = 1.0;
inputArr[1] = 1.0;
// Activate the black box.
box.Activate();
if (!box.IsStateValid)
{ // Any black box that gets itself into an invalid state is unlikely to be
// any good, so lets just bail out here.
return(FitnessInfo.Zero);
}
// Read output signal.
output = Math.Min(1, Math.Max(0, outputArr[0]));
output1 = output;
Debug.Assert(output >= 0.0, "Unexpected negative output.");
// Calculate this test case's contribution to the overall fitness score.
//fitness += 1.0 - output; // Use this line to punish absolute error instead of squared error.
fitness += 1.0 - (output * output);
if (output > 0.5)
{
pass = 0.0;
}
//----- Test 0,1
// Reset any black box state from the previous test case.
box.ResetState();
// Set the input values
inputArr[0] = 0.0;
inputArr[1] = 1.0;
// Activate the black box.
box.Activate();
if (!box.IsStateValid)
{ // Any black box that gets itself into an invalid state is unlikely to be
// any good, so lets just bail out here.
return(FitnessInfo.Zero);
}
// Read output signal.
output = Math.Min(1, Math.Max(0, outputArr[0]));
output2 = output;
Debug.Assert(output >= 0.0, "Unexpected negative output.");
// Calculate this test case's contribution to the overall fitness score.
// fitness += output; // Use this line to punish absolute error instead of squared error.
fitness += 1.0 - ((1.0 - output) * (1.0 - output));
if (output <= 0.5)
{
pass = 0.0;
}
//----- Test 1,0
// Reset any black box state from the previous test case.
box.ResetState();
// Set the input values
inputArr[0] = 1.0;
inputArr[1] = 0.0;
// Activate the black box.
box.Activate();
if (!box.IsStateValid)
{ // Any black box that gets itself into an invalid state is unlikely to be
// any good, so lets just bail out here.
return(FitnessInfo.Zero);
}
// Read output signal.
output = Math.Min(1, Math.Max(0, outputArr[0]));
output3 = output;
Debug.Assert(output >= 0.0, "Unexpected negative output.");
// Calculate this test case's contribution to the overall fitness score.
// fitness += output; // Use this line to punish absolute error instead of squared error.
fitness += 1.0 - ((1.0 - output) * (1.0 - output));
if (output <= 0.5)
{
pass = 0.0;
}
// If all four outputs were correct, that is, all four were on the correct side of the
// threshold level - then we add 10 to the fitness.
fitness += pass * 10.0;
if (fitness >= StopFitness)
{
_stopConditionSatisfied = true;
}
if (fitness > 3.6)
{
Console.WriteLine("After Nodes={5} Connections={6} [0,0]={0:N3} [1,1]={1:N3} [0,1]={2:N3} [1,0]={3:N3} fitness={4:N3}", output0, output1, output2, output3, fitness, net.HiddenCount, net.ConnectionCount);
}
return(new FitnessInfo(fitness, fitness));
}