public void CheckAccuracy()
{
Random rand = new Random(31415);
float[] GetSample()
{
var s = new float[3];
s[0] = rand.Next(2);
s[1] = rand.Next(2);
s[2] = ((int)s[0]) ^ ((int)s[1]);
return(s);
}
float Normalize(float value)
{
return(value * 2f - 1);
}
const int inputCount = 2;
const int hiddenCount = 5;
const int outputCount = 1;
var referenceNetwork = new ReferenceFFNetwork(inputCount, hiddenCount, outputCount);
var neatNetwork = new Network(GetNeatNetworkConnections(referenceNetwork.GetWeights(), inputCount, hiddenCount, outputCount),
inputCount + BiasCount,
outputCount,
new DummyNeatChromosomeEncoder());
var sample = GetSample();
neatNetwork.Sensors[0] = Normalize(sample[0]);
neatNetwork.Sensors[1] = Normalize(sample[1]);
neatNetwork.Activate();
var err = MathF.Abs(Normalize(neatNetwork.Effectors[0]) - sample[2]);
for (var i = 0; i < 100; i++)
{
var s = GetSample();
neatNetwork.ActivateAndTrain(s, 0.1f);
referenceNetwork.Train(new[] { s }, 0.1f);
}
neatNetwork.Sensors[0] = Normalize(sample[0]);
neatNetwork.Sensors[1] = Normalize(sample[1]);
neatNetwork.Activate();
var referenceNetworkOutput = referenceNetwork.ComputeOutputs(sample.Take(2).ToArray());
Assert.Equal(referenceNetworkOutput[0], neatNetwork.Effectors[0], 7);
Assert.True(err > MathF.Abs(Normalize(neatNetwork.Effectors[0]) - sample[2]));
}
public void CheckFeedforwardBp()
{
const int inputCount = 2;
const int hiddenCount = 2;
const int outputCount = 1;
var referenceNetwork = new ReferenceFFNetwork(inputCount, hiddenCount, outputCount);
var neatNetwork = new Network(GetNeatNetworkConnections(referenceNetwork.GetWeights(), inputCount, hiddenCount, outputCount),
inputCount + BiasCount,
outputCount,
new DummyNeatChromosomeEncoder());
var sampleNetworkOutput = referenceNetwork.ComputeOutputs(new[] { 0f, 1f });
neatNetwork.Sensors[0] = 1f;
neatNetwork.Sensors[1] = 0f;
neatNetwork.Activate();
var neatNetworkOutputBeforeTraining = neatNetwork.Effectors.ToArray();
//the sample network and the NEAT network are identical
Assert.Equal(sampleNetworkOutput, neatNetworkOutputBeforeTraining);
referenceNetwork.Train(new[] { new[] { 0f, 1f, 1f } }, 0.3f);
neatNetwork.ActivateAndTrain(new[] { 1f, 0f, 1f }, 0.3f);
//NEAT network effector values have not changed after training before Activate call
Assert.Equal(neatNetworkOutputBeforeTraining, neatNetwork.Effectors.ToArray());
sampleNetworkOutput = referenceNetwork.ComputeOutputs(new[] { 0f, 1f });
neatNetwork.Activate();
//backpropagation works fine
Assert.Equal(sampleNetworkOutput, neatNetwork.Effectors.ToArray());
Assert.NotEqual(neatNetworkOutputBeforeTraining, neatNetwork.Effectors.ToArray());
}
