public void ActivateNeuron()
{
BiasNeuron bias = new BiasNeuron();
double w0 = 0;
neuron.Connect(bias, w0);
Assert.Throws(typeof(NotConfiguredException),
() => neuron.Activation());
InputNeuron i1 = new InputNeuron();
InputNeuron i2 = new InputNeuron();
InputNeuron i3 = new InputNeuron();
i1.Input = 1;
i2.Input = 1;
i3.Input = 1;
double w1 = 1;
double w2 = 1;
double w3 = 1;
neuron.Connect(i1, w1);
neuron.Connect(i2, w2);
neuron.Connect(i3, w3);
double tx = i1.Input * w1 + i2.Input * w2 + i3.Input * w3;
double expected_activation = 1 / (1 + Math.Pow(Math.E, -tx));
MyAssert.CloseTo(neuron.Activation(), expected_activation);
}
public void TestXNOR_Manualy()
{
Neuron a3_1 = new Neuron();
BiasNeuron bias_2 = new BiasNeuron();
a3_1.Connect(bias_2);
Neuron a2_1 = new Neuron();
a3_1.Connect(a2_1);
Neuron a2_2 = new Neuron();
a3_1.Connect(a2_2);
BiasNeuron bias_1 = new BiasNeuron();
a2_1.Connect(bias_1);
a2_2.Connect(bias_1);
InputNeuron a1_1 = new InputNeuron();
a2_1.Connect(a1_1);
a2_2.Connect(a1_1);
InputNeuron a1_2 = new InputNeuron();
a2_1.Connect(a1_2);
a2_2.Connect(a1_2);
a3_1.SetWeight(0, -10);
a3_1.SetWeight(1, 20);
a3_1.SetWeight(2, 20);
a2_1.SetWeight(0, -30);
a2_1.SetWeight(1, 20);
a2_1.SetWeight(2, 20);
a2_2.SetWeight(0, 10);
a2_2.SetWeight(1, -20);
a2_2.SetWeight(2, -20);
a1_1.Input = 0;
a1_2.Input = 0;
MyAssert.CloseTo(a3_1.Activation(), 1);
a1_1.Input = 0;
a1_2.Input = 1;
MyAssert.CloseTo(a3_1.Activation(), 0);
a1_1.Input = 1;
a1_2.Input = 0;
MyAssert.CloseTo(a3_1.Activation(), 0);
a1_1.Input = 1;
a1_2.Input = 1;
MyAssert.CloseTo(a3_1.Activation(), 1);
}
void NeuronActivation()
{
Neuron n = SimpleNeuron();
Assert.That(n.Activation(new List <double>()
{
0.2, 0.8, 0.1
}), Is.InRange(0.67, 0.68));
Assert.That(n.Activation(new List <double>()
{
0.1, 0.1, 0.1
}), Is.InRange(0.63, 0.64));
}
public void Neuron_Activation_CorrectLogic()
{
// arrange
Neuron neuron = new Neuron();
Sinaps sinaps1 = new Sinaps(new Neuron()
{
Data = 0.5
}, 0.2);
Sinaps sinaps2 = new Sinaps(new Neuron()
{
Data = 0.3
}, 0.4);
neuron.Sinapses.Add(sinaps1);
neuron.Sinapses.Add(sinaps2);
double expected = 0;
foreach (Sinaps sinaps in neuron.Sinapses)
{
expected += sinaps.GetData();
}
expected = (Math.Exp(2 * expected) - 1) / (Math.Exp(2 * expected) + 1);
// act
neuron.Activation();
// assert
double actual = neuron.Data;
Assert.AreEqual(expected, actual);
}
private IEnumerable <BakedOperation> RecursiveOp(
NeuralGenome genome,
Neuron target,
HashSet <InnovationNumber> solvedNeurons)
{
if (target.IsStarting)
{
solvedNeurons.Add(target.InnovationNb);
yield break;
}
Trace.Assert(!solvedNeurons.Contains(target.InnovationNb));
solvedNeurons.Add(target.InnovationNb);
yield return(() => target.Value = target.ValueCollector.InitialValue);
foreach (var gene in genome.GetGenesToNeuron(target.InnovationNb))
{
var synapse = gene.Synapse;
if (!synapse.enabled)
{
continue;
}
if (!solvedNeurons.Contains(synapse.incoming))
{
var neuronToSolve = genome.Neurons[synapse.incoming];
foreach (var op in RecursiveOp(genome, neuronToSolve, solvedNeurons))
{
yield return(op);
}
}
yield return(() =>
{
var incommingNeurVal = genome.Neurons[synapse.incoming].Value;
var newDelta = synapse.Weight * incommingNeurVal;
var newVal = target.ValueCollector.Collect(
target.Value,
newDelta);
target.Value = newVal;
});
}
if (target.ValueModifiers != null)
{
foreach (var valueModifier in target.ValueModifiers)
{
yield return(() =>
target.Value = (float)valueModifier(target.Value));
}
}
if (target.Activation != null)
{
yield return(() =>
target.Value = (float)target.Activation(target.Value));
}
}
public void Neuron_Activation_CorrectRange()
{
// arrange
Neuron neuron = new Neuron();
Sinaps sinaps1 = new Sinaps(new Neuron()
{
Data = 0.5
}, 0.5);
Sinaps sinaps2 = new Sinaps(new Neuron()
{
Data = 0.3
}, 0.4);
neuron.Sinapses.Add(sinaps1);
neuron.Sinapses.Add(sinaps2);
// act
neuron.Activation();
// assert
double actual = neuron.Data;
Assert.IsTrue(actual >= -1 && actual <= 1);
}
public void TestXNOR_Manualy()
{
Neuron a3_1 = new Neuron();
BiasNeuron bias_2 = new BiasNeuron();
a3_1.Connect(bias_2);
Neuron a2_1 = new Neuron();
a3_1.Connect(a2_1);
Neuron a2_2 = new Neuron();
a3_1.Connect(a2_2);
BiasNeuron bias_1 = new BiasNeuron();
a2_1.Connect(bias_1);
a2_2.Connect(bias_1);
InputNeuron a1_1 = new InputNeuron();
a2_1.Connect(a1_1);
a2_2.Connect(a1_1);
InputNeuron a1_2 = new InputNeuron();
a2_1.Connect(a1_2);
a2_2.Connect(a1_2);
a3_1.SetWeight(0, -10);
a3_1.SetWeight(1, 20);
a3_1.SetWeight(2, 20);
a2_1.SetWeight(0, -30);
a2_1.SetWeight(1, 20);
a2_1.SetWeight(2, 20);
a2_2.SetWeight(0, 10);
a2_2.SetWeight(1, -20);
a2_2.SetWeight(2, -20);
a1_1.Input = 0;
a1_2.Input = 0;
MyAssert.CloseTo(a3_1.Activation(), 1);
a1_1.Input = 0;
a1_2.Input = 1;
MyAssert.CloseTo(a3_1.Activation(), 0);
a1_1.Input = 1;
a1_2.Input = 0;
MyAssert.CloseTo(a3_1.Activation(), 0);
a1_1.Input = 1;
a1_2.Input = 1;
MyAssert.CloseTo(a3_1.Activation(), 1);
}
public void Activaction_CorrectValues()
{
Assert.AreEqual(0.5, Neuron.Activation(0.0));
Assert.IsTrue(Neuron.Activation(10.0) > 0.95);
Assert.IsTrue(Neuron.Activation(-10.0) < 0.05);
}
