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); }