public virtual void Output(Genome g, float[] output) { }
public virtual void GenomeInitialization(Genome g) { }
public virtual void Input(Genome g, ref float[] input) { }
public void TrainGeneration() { bool champ = _Champion != null; Genome[] gen = new Genome[param.PopulationDensity]; if (champ) for (int i = 0; i < param.PopulationDensity; i++) gen[i] = _Champion.DeepClone(); else { for (int i = 0; i < param.PopulationDensity; i++) { Genome genome = new Genome(); genome.Input = new Neuron[param.InputNeurons]; for (int j = 0; j < param.InputNeurons; j++) { genome.Input[j] = new Neuron(genome.NeuronIndex, 0); genome.NeuronIndex++; } genome.Output = new Neuron[param.OutputNeurons]; for (int j = 0; j < param.OutputNeurons; j++) { genome.Output[j] = new Neuron(genome.NeuronIndex, param.DefaultOutputNeuronThreshold); genome.NeuronIndex++; } gen[i] = genome; } } for (int i = 0; i < param.MutationsPerGeneration; i++) { #region Genome mutations for (int j = 0; j < param.PopulationDensity; j++) { Genome g = gen[j]; param.Domain.GenomeInitialization(g); int force = (param.ForceComplexify ? rnd.Next(4) : -1); //Remove neuron if (g.HiddenLayers.Count > 0 && param.RemoveNeuronChance > (float)rnd.NextDouble()) { List<Neuron> layer = g.HiddenLayers[rnd.Next(g.HiddenLayers.Count)]; if (layer.Count < 1) g.HiddenLayers.Remove(layer); else { Neuron n = layer[rnd.Next(layer.Count)]; foreach (NeuralConnection connection in n.Connections) { g.Connections.Remove(connection); if (connection.Out == n) connection.In.Connections.Remove(connection); else connection.Out.Connections.Remove(connection); } layer.Remove(n); n.Connections = null; if (layer.Count < 1) g.HiddenLayers.Remove(layer); layer = null; n = null; } } //Remove connection if (g.Connections.Count > 0 && param.RemoveConnectionChance > (float)rnd.NextDouble()) { NeuralConnection c = g.Connections[rnd.Next(g.Connections.Count)]; g.Connections.Remove(c); c.In.Connections.Remove(c); c.Out.Connections.Remove(c); List<List<Neuron>> layerstoremove = new List<List<Neuron>>(); foreach (List<Neuron> layer in g.HiddenLayers) { List<Neuron> toremove = new List<Neuron>(); foreach (Neuron n in layer) if (n.Connections.Count < 1) toremove.Add(n); foreach (Neuron n in toremove) layer.Remove(n); if (layer.Count < 1) layerstoremove.Add(layer); } foreach (List<Neuron> layer in layerstoremove) g.HiddenLayers.Remove(layer); } //Add neuron float rand = (float)rnd.NextDouble(); bool fhlayer = false; if (force == 0 || param.AddNeuronChance > rand || (fhlayer = (g.HiddenLayers.Count < 1 && param.ForceHiddenLayer))) { bool newlayer = (g.HiddenLayers.Count < 1 || param.AddNeuronToNewLayerChance > (float)rnd.NextDouble()); int index = rnd.Next(g.HiddenLayers.Count); List<Neuron> layer = (newlayer || fhlayer ? new List<Neuron>() : g.HiddenLayers[index]); Neuron newn = new Neuron(g.NeuronIndex, param.DefaultHiddenNeuronThreshold); layer.Add(newn); g.NeuronIndex++; NeuralConnection cin = new NeuralConnection(); cin.In = newn; cin.Weight = param.DefaultConnectionWeight; NeuralConnection cout = new NeuralConnection(); cout.Out = newn; cout.Weight = param.DefaultConnectionWeight; if (newlayer) g.HiddenLayers.Add(layer); if (newlayer || index == g.HiddenLayers.Count - 1 || rnd.Next(g.HiddenLayers.Count) == 0) cin.Out = g.Output[rnd.Next(g.Output.Length)]; else { List<Neuron> layercin = g.HiddenLayers[rnd.Next(index + 1, g.HiddenLayers.Count)]; cin.Out = layercin[rnd.Next(layercin.Count)]; } if (index == 0 || rnd.Next(g.HiddenLayers.Count) == 0) cout.In = g.Input[rnd.Next(g.Input.Length)]; else { List<Neuron> layercout = g.HiddenLayers[rnd.Next(0, index)]; cout.In = layercout[rnd.Next(layercout.Count)]; } if (param.FluctuateNewConnections) { float fluc = (float)(rnd.NextDouble() * (param.WeightFluctuationHigh - param.WeightFluctuationLow) + param.WeightFluctuationLow); float fluc2 = (float)(rnd.NextDouble() * (param.WeightFluctuationHigh - param.WeightFluctuationLow) + param.WeightFluctuationLow); if (rnd.Next(2) == 1) cin.Weight += fluc; else cin.Weight -= fluc; if (rnd.Next(2) == 1) cout.Weight += fluc2; else cout.Weight -= fluc2; } newn.Connections.Add(cin); newn.Connections.Add(cout); cin.Out.Connections.Add(cin); cout.In.Connections.Add(cout); g.Connections.Add(cin); g.Connections.Add(cout); } //Add connection if (!champ || g.Connections.Count < 1 || force == 1 || param.AddConnectionChance > (float)rnd.NextDouble()) { for (int k = 0; k < 10; k++) { Neuron From, To; bool last = false; int index = -1; if (g.HiddenLayers.Count < 1 || rnd.Next(g.HiddenLayers.Count) == 0) From = g.Input[rnd.Next(g.Input.Length)]; else { index = rnd.Next(g.HiddenLayers.Count); last = (index == g.HiddenLayers.Count - 1); List<Neuron> layer = g.HiddenLayers[index]; From = layer[rnd.Next(layer.Count)]; } if (last || g.HiddenLayers.Count < 1 || rnd.Next(g.HiddenLayers.Count) == 0) To = g.Output[rnd.Next(g.Output.Length)]; else { List<Neuron> layer = g.HiddenLayers[rnd.Next(index + 1, g.HiddenLayers.Count)]; To = layer[rnd.Next(layer.Count)]; } bool flag = false; foreach (NeuralConnection c in g.Connections) if (c.In.ID == From.ID && c.Out.ID == To.ID) { flag = true; break; } if (flag) continue; NeuralConnection connection = new NeuralConnection(); connection.In = From; connection.Out = To; connection.Weight = param.DefaultConnectionWeight; if (param.FluctuateNewConnections) { float fluc = (float)(rnd.NextDouble() * (param.WeightFluctuationHigh - param.WeightFluctuationLow) + param.WeightFluctuationLow); if (rnd.Next(2) == 1) connection.Weight += fluc; else connection.Weight -= fluc; } g.Connections.Add(connection); From.Connections.Add(connection); To.Connections.Add(connection); break; } } //Fluctuate weight if (force == 2 || param.WeightFluctuationChance > (float)rnd.NextDouble()) { int index = rnd.Next(g.Connections.Count); float fluc = (float)(rnd.NextDouble() * (param.WeightFluctuationHigh - param.WeightFluctuationLow) + param.WeightFluctuationLow); if (rnd.Next(2) == 1) g.Connections[index].Weight += fluc; else g.Connections[index].Weight -= fluc; } //Fluctuate threshold if (g.HiddenLayers.Count > 0 && (force == 3 || param.ThresholdFluctuationChance > (float)rnd.NextDouble())) { List<Neuron> hlayer = g.HiddenLayers[rnd.Next(g.HiddenLayers.Count)]; int index = rnd.Next(hlayer.Count); float fluc = (float)(rnd.NextDouble() * (param.ThresholdFluctuationHigh - param.ThresholdFluctuationLow) + param.ThresholdFluctuationLow); if (hlayer[index].Threshold <= 0 || rnd.Next(2) == 1) hlayer[index].Threshold += fluc; else hlayer[index].Threshold -= fluc; if (hlayer[index].Threshold < 0) hlayer[index].Threshold = 0; } } #endregion } param.Domain.GenerationInitialization(); for (int i = 0; i < param.TestsPerGeneration; i++) { param.Domain.TestInitialization(i, (int)_Generation, _GenerationFitness); /* Testing */ foreach (Genome g in gen) { if (g.Exhausted) continue; float[] input = new float[param.InputNeurons]; param.Domain.Input(g, ref input); for (int j = 0; j < param.InputNeurons; j++) g.Input[j].Value = input[j]; g.Process(); float[] output = new float[param.OutputNeurons]; for (int j = 0; j < param.OutputNeurons; j++) output[j] = g.Output[j].Value; param.Domain.Output(g, output); if(i != param.TestsPerGeneration - 1) g.ClearNeurons(); } } _Generation++; /* Champion selection */ Genome newchamp = null; float tempfit = (param.VolatileChampions ? -10000000 : _ChampionFitness); float tempgenfit = 0f; foreach (Genome g in gen) { if (g.Fitness > tempgenfit) tempgenfit = g.Fitness; if (g.Fitness <= tempfit) continue; newchamp = g; tempfit = g.Fitness; } _GenerationFitness = tempgenfit; if (newchamp != null) { _ChampionFitness = newchamp.Fitness; float[] output = new float[param.OutputNeurons]; for (int j = 0; j < param.OutputNeurons; j++) output[j] = newchamp.Output[j].Value; newchamp.Clear(); newchamp.CleanLayers(); _Champion = newchamp; if (param.LiveChampionViewer != null) { param.LiveChampionViewer.Genome = newchamp; param.LiveChampionViewer.Invalidate(); } param.Domain.ChampionSelected(newchamp, _ChampionFitness, output, _Generation); } }
public virtual void ChampionSelected(Genome g, float fitness, float[] output, uint generation) { }
public void TrainGeneration() { bool champ = _Champion != null; Genome[] gen = new Genome[param.PopulationDensity]; if (champ) { for (int i = 0; i < param.PopulationDensity; i++) { gen[i] = _Champion.DeepClone(); } } else { for (int i = 0; i < param.PopulationDensity; i++) { Genome genome = new Genome(); genome.Input = new Neuron[param.InputNeurons]; for (int j = 0; j < param.InputNeurons; j++) { genome.Input[j] = new Neuron(genome.NeuronIndex, 0); genome.NeuronIndex++; } genome.Output = new Neuron[param.OutputNeurons]; for (int j = 0; j < param.OutputNeurons; j++) { genome.Output[j] = new Neuron(genome.NeuronIndex, param.DefaultOutputNeuronThreshold); genome.NeuronIndex++; } gen[i] = genome; } } for (int i = 0; i < param.MutationsPerGeneration; i++) { #region Genome mutations for (int j = 0; j < param.PopulationDensity; j++) { Genome g = gen[j]; param.Domain.GenomeInitialization(g); int force = (param.ForceComplexify ? rnd.Next(4) : -1); //Remove neuron if (g.HiddenLayers.Count > 0 && param.RemoveNeuronChance > (float)rnd.NextDouble()) { List <Neuron> layer = g.HiddenLayers[rnd.Next(g.HiddenLayers.Count)]; if (layer.Count < 1) { g.HiddenLayers.Remove(layer); } else { Neuron n = layer[rnd.Next(layer.Count)]; foreach (NeuralConnection connection in n.Connections) { g.Connections.Remove(connection); if (connection.Out == n) { connection.In.Connections.Remove(connection); } else { connection.Out.Connections.Remove(connection); } } layer.Remove(n); n.Connections = null; if (layer.Count < 1) { g.HiddenLayers.Remove(layer); } layer = null; n = null; } } //Remove connection if (g.Connections.Count > 0 && param.RemoveConnectionChance > (float)rnd.NextDouble()) { NeuralConnection c = g.Connections[rnd.Next(g.Connections.Count)]; g.Connections.Remove(c); c.In.Connections.Remove(c); c.Out.Connections.Remove(c); List <List <Neuron> > layerstoremove = new List <List <Neuron> >(); foreach (List <Neuron> layer in g.HiddenLayers) { List <Neuron> toremove = new List <Neuron>(); foreach (Neuron n in layer) { if (n.Connections.Count < 1) { toremove.Add(n); } } foreach (Neuron n in toremove) { layer.Remove(n); } if (layer.Count < 1) { layerstoremove.Add(layer); } } foreach (List <Neuron> layer in layerstoremove) { g.HiddenLayers.Remove(layer); } } //Add neuron float rand = (float)rnd.NextDouble(); bool fhlayer = false; if (force == 0 || param.AddNeuronChance > rand || (fhlayer = (g.HiddenLayers.Count < 1 && param.ForceHiddenLayer))) { bool newlayer = (g.HiddenLayers.Count <1 || param.AddNeuronToNewLayerChance> (float) rnd.NextDouble()); int index = rnd.Next(g.HiddenLayers.Count); List <Neuron> layer = (newlayer || fhlayer ? new List <Neuron>() : g.HiddenLayers[index]); Neuron newn = new Neuron(g.NeuronIndex, param.DefaultHiddenNeuronThreshold); layer.Add(newn); g.NeuronIndex++; NeuralConnection cin = new NeuralConnection(); cin.In = newn; cin.Weight = param.DefaultConnectionWeight; NeuralConnection cout = new NeuralConnection(); cout.Out = newn; cout.Weight = param.DefaultConnectionWeight; if (newlayer) { g.HiddenLayers.Add(layer); } if (newlayer || index == g.HiddenLayers.Count - 1 || rnd.Next(g.HiddenLayers.Count) == 0) { cin.Out = g.Output[rnd.Next(g.Output.Length)]; } else { List <Neuron> layercin = g.HiddenLayers[rnd.Next(index + 1, g.HiddenLayers.Count)]; cin.Out = layercin[rnd.Next(layercin.Count)]; } if (index == 0 || rnd.Next(g.HiddenLayers.Count) == 0) { cout.In = g.Input[rnd.Next(g.Input.Length)]; } else { List <Neuron> layercout = g.HiddenLayers[rnd.Next(0, index)]; cout.In = layercout[rnd.Next(layercout.Count)]; } if (param.FluctuateNewConnections) { float fluc = (float)(rnd.NextDouble() * (param.WeightFluctuationHigh - param.WeightFluctuationLow) + param.WeightFluctuationLow); float fluc2 = (float)(rnd.NextDouble() * (param.WeightFluctuationHigh - param.WeightFluctuationLow) + param.WeightFluctuationLow); if (rnd.Next(2) == 1) { cin.Weight += fluc; } else { cin.Weight -= fluc; } if (rnd.Next(2) == 1) { cout.Weight += fluc2; } else { cout.Weight -= fluc2; } } newn.Connections.Add(cin); newn.Connections.Add(cout); cin.Out.Connections.Add(cin); cout.In.Connections.Add(cout); g.Connections.Add(cin); g.Connections.Add(cout); } //Add connection if (!champ || g.Connections.Count < 1 || force == 1 || param.AddConnectionChance > (float)rnd.NextDouble()) { for (int k = 0; k < 10; k++) { Neuron From, To; bool last = false; int index = -1; if (g.HiddenLayers.Count < 1 || rnd.Next(g.HiddenLayers.Count) == 0) { From = g.Input[rnd.Next(g.Input.Length)]; } else { index = rnd.Next(g.HiddenLayers.Count); last = (index == g.HiddenLayers.Count - 1); List <Neuron> layer = g.HiddenLayers[index]; From = layer[rnd.Next(layer.Count)]; } if (last || g.HiddenLayers.Count < 1 || rnd.Next(g.HiddenLayers.Count) == 0) { To = g.Output[rnd.Next(g.Output.Length)]; } else { List <Neuron> layer = g.HiddenLayers[rnd.Next(index + 1, g.HiddenLayers.Count)]; To = layer[rnd.Next(layer.Count)]; } bool flag = false; foreach (NeuralConnection c in g.Connections) { if (c.In.ID == From.ID && c.Out.ID == To.ID) { flag = true; break; } } if (flag) { continue; } NeuralConnection connection = new NeuralConnection(); connection.In = From; connection.Out = To; connection.Weight = param.DefaultConnectionWeight; if (param.FluctuateNewConnections) { float fluc = (float)(rnd.NextDouble() * (param.WeightFluctuationHigh - param.WeightFluctuationLow) + param.WeightFluctuationLow); if (rnd.Next(2) == 1) { connection.Weight += fluc; } else { connection.Weight -= fluc; } } g.Connections.Add(connection); From.Connections.Add(connection); To.Connections.Add(connection); break; } } //Fluctuate weight if (force == 2 || param.WeightFluctuationChance > (float)rnd.NextDouble()) { int index = rnd.Next(g.Connections.Count); float fluc = (float)(rnd.NextDouble() * (param.WeightFluctuationHigh - param.WeightFluctuationLow) + param.WeightFluctuationLow); if (rnd.Next(2) == 1) { g.Connections[index].Weight += fluc; } else { g.Connections[index].Weight -= fluc; } } //Fluctuate threshold if (g.HiddenLayers.Count > 0 && (force == 3 || param.ThresholdFluctuationChance > (float)rnd.NextDouble())) { List <Neuron> hlayer = g.HiddenLayers[rnd.Next(g.HiddenLayers.Count)]; int index = rnd.Next(hlayer.Count); float fluc = (float)(rnd.NextDouble() * (param.ThresholdFluctuationHigh - param.ThresholdFluctuationLow) + param.ThresholdFluctuationLow); if (hlayer[index].Threshold <= 0 || rnd.Next(2) == 1) { hlayer[index].Threshold += fluc; } else { hlayer[index].Threshold -= fluc; } if (hlayer[index].Threshold < 0) { hlayer[index].Threshold = 0; } } } #endregion } param.Domain.GenerationInitialization(); for (int i = 0; i < param.TestsPerGeneration; i++) { param.Domain.TestInitialization(i, (int)_Generation, _GenerationFitness); /* Testing */ foreach (Genome g in gen) { if (g.Exhausted) { continue; } float[] input = new float[param.InputNeurons]; param.Domain.Input(g, ref input); for (int j = 0; j < param.InputNeurons; j++) { g.Input[j].Value = input[j]; } g.Process(); float[] output = new float[param.OutputNeurons]; for (int j = 0; j < param.OutputNeurons; j++) { output[j] = g.Output[j].Value; } param.Domain.Output(g, output); if (i != param.TestsPerGeneration - 1) { g.ClearNeurons(); } } } _Generation++; /* Champion selection */ Genome newchamp = null; float tempfit = (param.VolatileChampions ? -10000000 : _ChampionFitness); float tempgenfit = 0f; foreach (Genome g in gen) { if (g.Fitness > tempgenfit) { tempgenfit = g.Fitness; } if (g.Fitness <= tempfit) { continue; } newchamp = g; tempfit = g.Fitness; } _GenerationFitness = tempgenfit; if (newchamp != null) { _ChampionFitness = newchamp.Fitness; float[] output = new float[param.OutputNeurons]; for (int j = 0; j < param.OutputNeurons; j++) { output[j] = newchamp.Output[j].Value; } newchamp.Clear(); newchamp.CleanLayers(); _Champion = newchamp; if (param.LiveChampionViewer != null) { param.LiveChampionViewer.Genome = newchamp; param.LiveChampionViewer.Invalidate(); } param.Domain.ChampionSelected(newchamp, _ChampionFitness, output, _Generation); } }
public NeuralNetwork(NeuralNetworkParameters param, Genome champ) { this.param = param; _Champion = champ; }
public override void Output(Genome g, float[] output) { g.Fitness += 1f - Math.Abs(output[0] - (float)outpt); }
public NeuralNetwork(NeuralNetworkParameters param, Genome champ) { this.param = param; _Champion = champ; }
public override void Input(Genome g, ref float[] input) { input[0] = inpt1; input[1] = inpt2; }
public override void GenomeInitialization(Genome g) { }
public override void ChampionSelected(Genome g, float fitness, float[] output, uint generation) { Console.WriteLine("Champion selected : Generation {0} : Fitness {1:0.0000} : {2} + {3} = {4}", generation, fitness, inpt1, inpt2, output[0]); }
public override void Output(Genome g, float[] output) { float diff = Math.Abs(output[0] - (float)outpt); if (diff > 1f) diff = 1f; g.Fitness += 1f - diff; }
public Genome DeepClone() { /*using (MemoryStream stream = new MemoryStream()) //Old extremely slow deep-clone method { BinaryFormatter formatter = new BinaryFormatter(); formatter.Serialize(stream, this); stream.Position = 0; return (Genome)formatter.Deserialize(stream); }*/ Genome g = new Genome(); g.NeuronIndex = this.NeuronIndex; g.Input = new Neuron[this.Input.Length]; g.HiddenLayers = new List<List<Neuron>>(this.HiddenLayers.Capacity); g.Output = new Neuron[this.Output.Length]; for (int i = 0; i < this.Input.Length; i++) g.Input[i] = new Neuron(this.Input[i].ID, this.Input[i].Threshold); for (int i = 0; i < this.HiddenLayers.Count; i++) { List<Neuron> layer = new List<Neuron>(this.HiddenLayers[i].Capacity); for (int j = 0; j < this.HiddenLayers[i].Count; j++) layer.Add(new Neuron(this.HiddenLayers[i][j].ID, this.HiddenLayers[i][j].Threshold)); g.HiddenLayers.Add(layer); } for (int i = 0; i < this.Output.Length; i++) g.Output[i] = new Neuron(this.Output[i].ID, this.Output[i].Threshold); foreach (NeuralConnection connection in this.Connections) { Neuron from = null, to = null; foreach (Neuron n in g.Input) if (connection.In.ID == n.ID) from = n; foreach (List<Neuron> layer in g.HiddenLayers) foreach (Neuron n in layer) { if (connection.In.ID == n.ID) from = n; else if (connection.Out.ID == n.ID) to = n; if (from != null && to != null) break; } if (to == null) foreach (Neuron n in g.Output) if (connection.Out.ID == n.ID) to = n; NeuralConnection newc = new NeuralConnection(); newc.In = from; newc.Out = to; newc.Weight = connection.Weight; g.Connections.Add(newc); from.Connections.Add(newc); to.Connections.Add(newc); } return g; }