public static League sortTeamMembers(List<TeamMember> members, int numTeams)
{
League league = new League(members, numTeams);
Population population = new Population(members.Count, league, new FitnessCalculator(), new EliteSelection());
population.MutationRate = .5;
population.CrossoverRate = .2;
population.RunEpoch();
//run epoch until you get the same best chromosome 10 times
double bestFitness = -Double.MaxValue;
int count = 0;
while(count < 5000)
{
population.RunEpoch();
double fitness = ((League)population.BestChromosome).getFitness();
Console.WriteLine("Fitness: " + fitness);
if (fitness > bestFitness)
{
bestFitness = fitness;
count = 0;
}
else
{
count++;
}
}
//while (((League)population.BestChromosome).getFitness() != 80.0)
// population.RunEpoch();
League best = ((League)population.BestChromosome);
return best;
}
private void atualizaDadosPara(int iteracao,Population populacao)
{
log = "Geração: " + iteracao +
"\n Método de Seleção : " + populacao.SelectionMethod +
"\n Avaliação Média: " + populacao.FitnessAvg +
"\n Melhor Avaliação : " + populacao.FitnessMax +
"\n Melhor indivíduo: " + populacao.BestChromosome.ToString();
this.logBox.Text = log;
desenhaTabuleiro((ShortArrayChromosome)populacao.BestChromosome);
}
private static void HardestOpponent()
{
var challenges = Library.Monsters.ToDictionary(x => x, x => 0.0);
while (true)
{
var minScore = challenges.Values.Min();
var monster = challenges.First(x => x.Value == minScore).Key;
var simulator = monster.MaxPartyMembers > 1 ?
(ASimulator)new PartySimulator(monster, 40, 40, false) :
(ASimulator)new SingleHeroSimulator(monster, 40, 40);
var fitnessEvaluator = new PartyFitness(simulator);
var population = new Population(20,
new ShortArrayChromosome(fitnessEvaluator.ChromosomeLength, fitnessEvaluator.ChromosomeMaxValue),
fitnessEvaluator,
new RankSelection());
population.RandomSelectionPortion = 0.15;
double lastFitness = 0;
int stagnationLimit = minScore < 1e-4 ? 1000 : 10000;
for (int stagnation = 0; stagnation < stagnationLimit; stagnation++)
{
population.RunEpoch();
if (population.BestChromosome.Fitness > lastFitness)
{
lastFitness = population.BestChromosome.Fitness;
stagnation = 0;
if (lastFitness > challenges[monster])
{
challenges[monster] = lastFitness;
Console.WriteLine(monster.Name);
Console.Write(fitnessEvaluator.Translate(population.BestChromosome).Trim());
Console.WriteLine(" " + population.BestChromosome.Fitness);
Console.WriteLine();
}
}
population.MutationRate = stagnation > 1000 ?
Math.Min(stagnation / 3000.0, 0.25) :
0.1;
}
}
}
private void button1_Click(object sender, EventArgs e)
{
//ShortArrayChromosome[] individos = new ShortArrayChromosome[nPopulacao];
//for (int i = 0; i < individos.Length; i++)
//{
// individos[i] = new ShortArrayChromosome(nRainhas, nRainhas);
// individos[i].Generate();
// Console.WriteLine(i + " : " + individos[i].ToString());
//}
configuraAlgoritimo();
int selecao = selecaoBox.SelectedIndex;
ISelectionMethod metodoDeSelecao = (selecao == 0) ? (ISelectionMethod)new RouletteWheelSelection() :
(selecao == 1) ? (ISelectionMethod)new EliteSelection() :
(ISelectionMethod)new RankSelection();
AvaliadorDeRainhas avaliador = new AvaliadorDeRainhas();
Population populacao = new Population(nPopulacao, new ShortArrayChromosome(nRainhas, nRainhas - 1), avaliador, metodoDeSelecao);
populacao.CrossoverRate = crossoverRate;
populacao.MutationRate = mutationRate;
//populacao.AutoShuffling = true;
int iteracao = 0;
int pararEm = nParada;
while (iteracao < nGeracoes)
{
//populacao.Shuffle();
//populacao.Selection();
//populacao.Crossover();
//populacao.Mutate();
populacao.RunEpoch();
//MessageBox.Show("iteração: " + iteracao + "\n Avaliacao Media: " + populacao.FitnessAvg + "\n Melhor individo: " + populacao.BestChromosome.ToString());
if (nParada > 0 && iteracao == pararEm)
{
atualizaDadosPara(iteracao, populacao);
MessageBox.Show("Visualização\nGeração: " + iteracao+"\n OK para Continuar");
pararEm += nParada;
}
if (populacao.BestChromosome.Fitness == nRainhas)
break;
iteracao++;
}
//constroiTabuleiroNoConsole((ShortArrayChromosome)populacao.BestChromosome);
atualizaDadosPara(iteracao,populacao);
}
static void Main(string[] args)
{
DoubleArrayChromosome weightValues = new DoubleArrayChromosome(new WeightRandomGenerator(), new WeightRandomGenerator(), new WeightRandomGenerator(),6);
Population weightPop = new Population(40, weightValues, new AriesFF(), new EliteSelection());
int counter = 0;
bool stopEvo = false;
AriesFF fintessEval = new AriesFF();
double error = 0;
while (!stopEvo)
{
weightPop.RunEpoch();
counter++;
IChromosome best = weightPop.BestChromosome;
error = fintessEval.Evaluate(best);
stopEvo = counter > 1000 || error < 0.12;
}
Console.WriteLine("Stopped after " + counter.ToString());
DoubleArrayChromosome solution = (DoubleArrayChromosome)weightPop.BestChromosome;
Console.WriteLine(solution.ToString());
Console.ReadKey();
}
// Worker thread
void SearchSolution()
{
// create population
Population population = new Population(populationSize,
new BinaryChromosome(chromosomeLength),
userFunction,
(selectionMethod == 0) ? (ISelectionMethod)new EliteSelection() :
(selectionMethod == 1) ? (ISelectionMethod)new RankSelection() :
(ISelectionMethod)new RouletteWheelSelection()
);
// set optimization mode
userFunction.Mode = (optimizationMode == 0) ?
OptimizationFunction1D.Modes.Maximization :
OptimizationFunction1D.Modes.Minimization;
// iterations
int i = 1;
// solution
double[,] data = new double[(showOnlyBest) ? 1 : populationSize, 2];
// loop
while (!needToStop)
{
// run one epoch of genetic algorithm
population.RunEpoch();
// show current solution
if (showOnlyBest)
{
data[0, 0] = userFunction.Translate(population.BestChromosome);
data[0, 1] = userFunction.OptimizationFunction(data[0, 0]);
}
else
{
for (int j = 0; j < populationSize; j++)
{
data[j, 0] = userFunction.Translate(population[j]);
data[j, 1] = userFunction.OptimizationFunction(data[j, 0]);
}
}
chart.UpdateDataSeries("solution", data);
// set current iteration's info
SetText(currentIterationBox, i.ToString());
SetText(currentValueBox, userFunction.Translate(population.BestChromosome).ToString("F3"));
// increase current iteration
i++;
//
if ((iterations != 0) && (i > iterations))
break;
}
// enable settings controls
EnableControls(true);
}
// Worker thread
void SearchSolution( )
{
// constants
double[] constants = new double[10] { 1, 2, 3, 5, 7, 11, 13, 17, 19, 23 };
// create fitness function
TimeSeriesPredictionFitness fitness = new TimeSeriesPredictionFitness(
data, windowSize, predictionSize, constants );
// create gene function
IGPGene gene = ( functionsSet == 0 ) ?
(IGPGene) new SimpleGeneFunction( windowSize + constants.Length ) :
(IGPGene) new ExtendedGeneFunction( windowSize + constants.Length );
// create population
Population population = new Population( populationSize,
( geneticMethod == 0 ) ?
(IChromosome) new GPTreeChromosome( gene ) :
(IChromosome) new GEPChromosome( gene, headLength ),
fitness,
( selectionMethod == 0 ) ? (ISelectionMethod) new EliteSelection( ) :
( selectionMethod == 1 ) ? (ISelectionMethod) new RankSelection( ) :
(ISelectionMethod) new RouletteWheelSelection( )
);
// iterations
int i = 1;
// solution array
int solutionSize = data.Length - windowSize;
double[,] solution = new double[solutionSize, 2];
double[] input = new double[windowSize + constants.Length];
// calculate X values to be used with solution function
for ( int j = 0; j < solutionSize; j++ )
{
solution[j, 0] = j + windowSize;
}
// prepare input
Array.Copy( constants, 0, input, windowSize, constants.Length );
// loop
while ( !needToStop )
{
// run one epoch of genetic algorithm
population.RunEpoch( );
try
{
// get best solution
string bestFunction = population.BestChromosome.ToString( );
// calculate best function and prediction error
double learningError = 0.0;
double predictionError = 0.0;
// go through all the data
for ( int j = 0, n = data.Length - windowSize; j < n; j++ )
{
// put values from current window as variables
for ( int k = 0, b = j + windowSize - 1; k < windowSize; k++ )
{
input[k] = data[b - k];
}
// evalue the function
solution[j, 1] = PolishExpression.Evaluate( bestFunction, input );
// calculate prediction error
if ( j >= n - predictionSize )
{
predictionError += Math.Abs( solution[j, 1] - data[windowSize + j] );
}
else
{
learningError += Math.Abs( solution[j, 1] - data[windowSize + j] );
}
}
// update solution on the chart
chart.UpdateDataSeries( "solution", solution );
// set current iteration's info
SetText( currentIterationBox, i.ToString( ) );
SetText( currentLearningErrorBox, learningError.ToString( "F3" ) );
SetText( currentPredictionErrorBox, predictionError.ToString( "F3" ) );
}
catch
{
// remove any solutions from chart in case of any errors
chart.UpdateDataSeries( "solution", null );
}
// increase current iteration
i++;
//
if ( ( iterations != 0 ) && ( i > iterations ) )
break;
}
// show solution
SetText( solutionBox, population.BestChromosome.ToString( ) );
for ( int j = windowSize, k = 0, n = data.Length; j < n; j++, k++ )
{
AddSubItem( dataList, j, solution[k, 1].ToString( ) );
}
// enable settings controls
EnableControls( true );
}
/// <summary>
/// Perform migration between two populations.
/// </summary>
///
/// <param name="anotherPopulation">Population to do migration with.</param>
/// <param name="numberOfMigrants">Number of chromosomes from each population to migrate.</param>
/// <param name="migrantsSelector">Selection algorithm used to select chromosomes to migrate.</param>
///
/// <remarks><para>The method performs migration between two populations - current and the
/// <paramref name="anotherPopulation">specified one</paramref>. During migration
/// <paramref name="numberOfMigrants">specified number</paramref> of chromosomes is choosen from
/// each population using <paramref name="migrantsSelector">specified selection algorithms</paramref>
/// and put into another population replacing worst members there.</para></remarks>
///
public void Migrate( Population anotherPopulation, int numberOfMigrants, ISelectionMethod migrantsSelector )
{
int currentSize = this.size;
int anotherSize = anotherPopulation.Size;
// create copy of current population
List<IChromosome> currentCopy = new List<IChromosome>( );
for ( int i = 0; i < currentSize; i++ )
{
currentCopy.Add( population[i].Clone( ) );
}
// create copy of another population
List<IChromosome> anotherCopy = new List<IChromosome>( );
for ( int i = 0; i < anotherSize; i++ )
{
anotherCopy.Add( anotherPopulation.population[i].Clone( ) );
}
// apply selection to both populations' copies - select members to migrate
migrantsSelector.ApplySelection( currentCopy, numberOfMigrants );
migrantsSelector.ApplySelection( anotherCopy, numberOfMigrants );
// sort original populations, so the best chromosomes are in the beginning
population.Sort( );
anotherPopulation.population.Sort( );
// remove worst chromosomes from both populations to free space for new members
population.RemoveRange( currentSize - numberOfMigrants, numberOfMigrants );
anotherPopulation.population.RemoveRange( anotherSize - numberOfMigrants, numberOfMigrants );
// put migrants to corresponding populations
population.AddRange( anotherCopy );
anotherPopulation.population.AddRange( currentCopy );
// find best chromosomes in each population
FindBestChromosome( );
anotherPopulation.FindBestChromosome( );
}
// Worker thread
void SearchSolution()
{
// create fitness function
TSPFitnessFunction fitnessFunction = new TSPFitnessFunction(map);
// create population
Population population = new Population(populationSize,
(greedyCrossover) ? new TSPChromosome(map) : new PermutationChromosome(citiesCount),
fitnessFunction,
(selectionMethod == 0) ? (ISelectionMethod)new EliteSelection() :
(selectionMethod == 1) ? (ISelectionMethod)new RankSelection() :
(ISelectionMethod)new RouletteWheelSelection()
);
// iterations
int i = 1;
// path
double[,] path = new double[citiesCount + 1, 2];
// loop
while (!needToStop)
{
// run one epoch of genetic algorithm
population.RunEpoch();
// display current path
ushort[] bestValue = ((PermutationChromosome)population.BestChromosome).Value;
for (int j = 0; j < citiesCount; j++)
{
path[j, 0] = map[bestValue[j], 0];
path[j, 1] = map[bestValue[j], 1];
}
path[citiesCount, 0] = map[bestValue[0], 0];
path[citiesCount, 1] = map[bestValue[0], 1];
mapControl.UpdateDataSeries("path", path);
// set current iteration's info
SetText(currentIterationBox, i.ToString());
SetText(pathLengthBox, fitnessFunction.PathLength(population.BestChromosome).ToString());
// increase current iteration
i++;
//
if ((iterations != 0) && (i > iterations))
break;
}
// enable settings controls
EnableControls(true);
}
private static void Optimize(string monsterName, string[] startingBuild = null, ushort startingBuildFrontRow = 0)
{
var monster = Library.Monsters.First(x => x.Name == monsterName);
var simulator = monster.MaxPartyMembers > 1 ?
(ASimulator)new PartySimulator(monster, 40, 40, false) :
(ASimulator)new SingleHeroSimulator(monster, 40, 40);
double lastFitness = 0;
while(true)
{
var fitnessEvaluator = new PartyFitness(simulator);
var ancestor = (startingBuild == null) ?
new ShortArrayChromosome(fitnessEvaluator.ChromosomeLength, fitnessEvaluator.ChromosomeMaxValue) :
fitnessEvaluator.TranslateBack(simulator.TranslateBack(startingBuild), startingBuildFrontRow);
var population = new Population(20,
ancestor,
fitnessEvaluator,
new RankSelection());
population.RandomSelectionPortion = 0.15;
for(int stagnation = 0; stagnation < 5000; stagnation++)
{
population.RunEpoch();
if (population.BestChromosome.Fitness > lastFitness)
{
lastFitness = population.BestChromosome.Fitness;
Console.Write(fitnessEvaluator.Translate(population.BestChromosome).Trim());
Console.WriteLine(" " + population.BestChromosome.Fitness);
Console.WriteLine();
stagnation = 0;
}
population.MutationRate = stagnation > 1000 ?
Math.Min(stagnation / 3000.0, 0.25) :
0.1;
}
}
}
public MelodySequence Generate()
{
NoteGene baseGene = new NoteGene(GPGeneType.Function);
baseGene.Function = NoteGene.FunctionTypes.Concatenation;
GPCustomTree tree = new GPCustomTree(baseGene);
if (base_seq != null)
{
tree.Generate(base_seq.ToArray());
tree.Mutate();
tree.Crossover(tree);
int length = base_seq.Length;
int depth = (int)Math.Ceiling(Math.Log(length, 2));
GPCustomTree.MaxInitialLevel = depth - 2;
GPCustomTree.MaxLevel = depth + 5;
}
var selection = new EliteSelection();
pop = new Population(30, tree, fitnessFunction, selection);
pop.AutoShuffling = true;
pop.CrossoverRate = 0.9;
pop.MutationRate = 0.1;
int percentage = MaxGenerations / 100;
for (int i = 0; i < MaxGenerations; i++)
{
if(i>percentage)
{
if (OnPercentage != null)
OnPercentage(this, i, pop.FitnessAvg);
percentage += MaxGenerations / 100;
}
pop.RunEpoch();
if ((int)(i) % 100 == 0)
Console.WriteLine(i / (float)MaxGenerations * 100 + "% : " + pop.FitnessAvg);
}
GPCustomTree best = pop.BestChromosome as GPCustomTree;
var notes = best.GenerateNotes();
return new MelodySequence(notes);
}
protected override void StartTraining(bool aIsRunning)
{
if (aIsRunning)
{
mStopWatch = new Stopwatch();
mStopWatch.Start();
Stopwatch st = new Stopwatch();
st.Start();
min = Double.MaxValue;
mintest = Double.MaxValue;
// create grid application
Settings settings = Settings.LoadSettings();
GA = new GApplication(new GConnection(settings.AlchemiURL, settings.AlchemiPort, settings.AlchemiUser, settings.AlchemiPassword));
GA.ApplicationName = "Plain Neural Network model";
// add GridThread module (this executable) as a dependency
GA.Manifest.Add(new ModuleDependency(typeof(AForge.PolishExpression).Module));
GA.Manifest.Add(new ModuleDependency(typeof(AForge.Neuro.ActivationNetwork).Module));
GA.Manifest.Add(new ModuleDependency(typeof(SingleNNTraining).Module));
// subscribe to events
GA.ThreadFinish += new GThreadFinish(ThreadFinished);
GA.ThreadFailed += new GThreadFailed(ThreadFailed);
GA.ApplicationFinish += new GApplicationFinish(ApplicationFinished);
// Набор от данни използван за обучение
mInput = new double[ExtractEnd - ExtractBegin + 1][];
mOutput = new double[ExtractEnd - ExtractBegin + 1][];
// Тестов набор от данни
mTestInput = new double[TestEnd - TestBegin + 1][];
mTestOutput = new double[TestEnd - TestBegin + 1][];
for (int i = 0; i < ExtractEnd - ExtractBegin + 1; i++)
{
mInput[i] = new double[IndependentVariables.Count];
mOutput[i] = new double[DependentVariables.Count];
}
for (int i = 0; i < TestEnd - TestBegin + 1; i++)
{
mTestInput[i] = new double[IndependentVariables.Count];
mTestOutput[i] = new double[DependentVariables.Count];
}
MainDataSetTableAdapters.VARIABLESTableAdapter adptVariables = new Plig.TimeSeries.Client.MainDataSetTableAdapters.VARIABLESTableAdapter();
mReinitializations = Convert.ToInt32(txtReinitializations.Text);
results = new List<NNResultSet>(mReinitializations);
mEpochs = Convert.ToInt32(txtEpochs.Text);
ReinitsFinished = 0;
int index_of_variable = 0;
foreach (int variable_id in IndependentVariables.Keys)
{
double minObsValue = adptVariables.MinObservationValue(ExtractBegin, ExtractEnd, variable_id).Value;
double factor = 1.7 / (adptVariables.MaxObservationValue(ExtractBegin, ExtractEnd, variable_id).Value - adptVariables.MinObservationValue(ExtractBegin, ExtractEnd, variable_id).Value);
Dictionary<int, double?> observations = VariableObservations(variable_id);
foreach (int counter in observations.Keys)
{
if ((counter >= ExtractBegin) && (counter <= ExtractEnd))
{
mInput[counter - ExtractBegin][index_of_variable] = (observations[counter].Value - minObsValue) * factor - 0.85;
}
if ((counter >= TestBegin) && (counter <= TestEnd))
{
mTestInput[counter - TestBegin][index_of_variable] = (observations[counter].Value - minObsValue) * factor - 0.85;
}
}
index_of_variable++;
}
index_of_variable = 0;
foreach (int variable_id in DependentVariables.Keys)
{
double minObsValue = adptVariables.MinObservationValue(ExtractBegin, ExtractEnd, variable_id).Value;
double factor = 1.7 / (adptVariables.MaxObservationValue(ExtractBegin, ExtractEnd, variable_id).Value - adptVariables.MinObservationValue(ExtractBegin, ExtractEnd, variable_id).Value);
Dictionary<int, double?> observations = VariableObservations(variable_id);
foreach (int counter in observations.Keys)
{
if ((counter >= ExtractBegin) && (counter <= ExtractEnd))
{
mOutput[counter - ExtractBegin][index_of_variable] = (observations[counter].Value - minObsValue) * factor - 0.85;
}
if ((counter >= TestBegin) && (counter <= TestEnd))
{
mTestOutput[counter - TestBegin][index_of_variable] = (observations[counter].Value - minObsValue) * factor - 0.85;
}
}
index_of_variable++;
}
double momentum = 0.0;
double learning_rate = 0.1;
learning_rate = Convert.ToDouble(txtLearningRate.Text);
momentum = Convert.ToDouble(txtMomentum.Text);
int min_neurons = 1;
int max_neurons = 1;
if (rbFixedHiddenNeurons.Checked)
{
min_neurons = Convert.ToInt32(txtHiddenNeurons.Text);
max_neurons = min_neurons;
}
if (rbFlexibleHiddenNeurons.Checked)
{
min_neurons = Convert.ToInt32(txtMinHiddenNeurons.Text);
max_neurons = Convert.ToInt32(txtMaxHiddenNeurons.Text);
}
int TotalNNCalculations = 0;
for (int hidden_neurons = min_neurons; hidden_neurons <= max_neurons; hidden_neurons++)
{
for (int i = 0; i < mReinitializations; i++)
// Parallel.For(0, mReinitializations - 1, i =>
{
// create thread
SingleNNTraining single_nn = new SingleNNTraining(mInput, mOutput, mEpochs);
single_nn.HiddenNeurons = hidden_neurons;
single_nn.Iterations = mEpochs;
single_nn.Momentum = momentum;
single_nn.LearningRate = learning_rate;
// добавяне на набора от данни за тестване
single_nn.TestInput = mTestInput;
single_nn.TestOutput = mTestOutput;
// add thread to application
GA.Threads.Add(single_nn);
TotalNNCalculations++;
}
// );
}
pbProgressTraining.Minimum = 0;
pbProgressTraining.Maximum = TotalNNCalculations - 1;
GA.Start();
st.Stop();
lbResults.Items.Add("Време необходимо за конструиране на данните: " + st.Elapsed.ToString());
int populationCount = Convert.ToInt32(txtPopulationCount.Text);
Population population = new Population(populationCount,
new DoubleArrayChromosome(new AForge.Math.Random.UniformOneGenerator(),
new AForge.Math.Random.GaussianGenerator(0, 1),new AForge.Math.Random.GaussianGenerator(0, 1),
(mOutput.Length + mInput.Length + 1) * 10 + mOutput.Length),
new NeuralNetworkFitness(mInput, mOutput, new int[] { IndependentVariables.Count, 10, DependentVariables.Count }),
new EliteSelection());
int geneticEpochs = Convert.ToInt32(txtGeneticEpochs.Text);
pbGeneticProgress.Maximum = geneticEpochs;
for (int i = 0; i < geneticEpochs; i++)
{
population.RunEpoch();
pbGeneticProgress.Value = i + 1;
Application.DoEvents();
}
NeuralNetworkFitness nnf = new NeuralNetworkFitness(mInput, mOutput, new int[] { IndependentVariables.Count, 5, DependentVariables.Count });
mBestGeneticActivationNetwork = (ActivationNetwork)nnf.Translate(population.BestChromosome);
}
else
{
if (GA.Running)
{
foreach (GThread thread in GA.Threads)
{
thread.Abort();
}
// GA.Stop();
lbResults.Items.Add("Обучението е прекъснато.");
}
}
}
public override void takeTurn()
{
Board.useDictionary = true;
bool tempRecording = GameController.recording;
GameController.recording = false;
fitness.cards = Board.current.viewableCards.FindAll(x => x.Deck != Card.Decks.nobles);
var ga = new Population(popSize, new BuyOrderChromosome(2*fitness.cards.Count), fitness, new RankSelection(), random);
lastBestChromosome = ga.population[0] as BuyOrderChromosome;
ga.CrossoverRate = 0.5;
// if (!predicted.Equals(Board.current.PrevMove)) RecordHistory.current.record("!!! Prediction failed.");
// predicted = null;
int i = 0;
turnTimer.Restart();
while (fitness.timesEvaluated < evaluations)//(turnTimer.Elapsed < Board.current.notCurrentPlayer.turnTimer.Elapsed && i < 40)
{
ga.RunEpoch();
ga.AddChromosome(lastBestChromosome);
if (ga.BestChromosome.Fitness > lastBestChromosome.Fitness) lastBestChromosome = ga.BestChromosome as BuyOrderChromosome;
if (i % 6 == 0) Board.current.ResetDictionary();
CONSOLE.Overwrite(6, "Generations " + i + " Evaluation " + fitness.timesEvaluated);
RecordHistory.current.plot(i + "," + ga.FitnessMax + Environment.NewLine);
if ((GameController.turn % 3 == 0) && (i < 4)) takeSnapshot(ga);
//setData(i);
i++;
}
fitness.timesEvaluated = 0;
GameController.recording = tempRecording;
if (lastBestChromosome == null) throw new Exception("Null chromosome after evaluation " + i);
lastBestChromosome.Evaluate(fitness);
Move m = fitness.simulateMyTurn(lastBestChromosome, Board.current).PrevMove;
RecordHistory.current.record(this + " took move " + m);
//foreach (BuyOrderChromosome p in chromosomes)
//{
// RecordHistory.writeToFile("Chromosomes.txt", string.Format("{0:0.00}",p.Fitness) + "|" + p.depth + "|" + p.Value.String() + "|" + string.Format("{0:0.00}",p.parentFitness));
//}
//RecordHistory.writeToFile("Chromosomes.txt", "");
//List<BuyOrderChromosome> chromosomes = BuyOrderChromosome.diversify(ga.population);
//CONSOLE.Overwrite(12, "XOver Impnts over gen.: " + xoverimpnts.String());
//CONSOLE.Overwrite(13, "Mut Impnts over gen.: " + mutimpnts.String());
//CONSOLE.Overwrite(14, "Crossover Improvements: " + BuyOrderChromosome.crossOverImprovements + " / " + BuyOrderChromosome.totalCrossOvers);
//CONSOLE.Overwrite(15, "Mutation Improvements: " + BuyOrderChromosome.mutationImprovements + " / " + BuyOrderChromosome.totalMutations);
//CONSOLE.Overwrite(16, "Number of Diverse Chromosomes: " + chromosomes.Count);
//totalChromosomes += ga.population.Count;
//diverseChromosomes += chromosomes.Count;
//CONSOLE.Overwrite(17, "Total diverse/chromosomes: " + ((double)diverseChromosomes / totalChromosomes));
//for (int j = 0; j < chromosomes.Count; j++)
//{
// CONSOLE.Overwrite(18 + j, chromosomes[j].Value.String() + " depth: " +chromosomes[j].depth + " fitness: " + chromosomes[j].Fitness);
//}
takeAction(m);
Board.useDictionary = false;
}
/// <summary>
/// Records the population fitness data in /snapshot_x.csv
/// </summary>
/// <param name="ga"></param>
private void takeSnapshot(Population ga)
{
List<double> fitnesses = ga.getFitnesses();
List<double> parents = ga.getParentFitnesses();
List<string> snap = new List<string>();
for (int j = 0; j < fitnesses.Count; j++) snap.Add(fitnesses[j].ToString() + "," + parents[j].ToString());
RecordHistory.current.snapshot(snap);
}
public IA(Game game, int players)
: base(game)
{
rndSeedGen = new Random();
rndControl = new Random();
rndMovControl = new Random();
this.comidas = null;
this.jugadores = null;
this.numWeights = HIDDEN_UNITS0 * (INPUT_UNITS + 1) + HIDDEN_UNITS1 * (HIDDEN_UNITS0 + 1) + OUTPUT_UNITS * (HIDDEN_UNITS1 + 1);
redes = new ActivationNetwork[players];
for (int i = 0; i < redes.Length; i++)
{
redes[i] = new ActivationNetwork(new SigmoidFunction(400), INPUT_UNITS, HIDDEN_UNITS0, HIDDEN_UNITS1, OUTPUT_UNITS);
}
inputVector = new double[INPUT_UNITS];
outputVector = new double[OUTPUT_UNITS];
doneEvents = new ManualResetEvent[players];
for (int i = 0; i < players; i++) doneEvents[i] = new ManualResetEvent(false);
//Se puede jugar con los parametros de los rangos para modificar la evolucion de las redes
//Tambien se puede modificar el metodo de seleccion.
chromosomeGenerator = new UniformGenerator(new Range(-10f, 10f), rndSeedGen.Next(-100, 100));
mutationAdditionGenerator = new UniformGenerator(new Range(-8f, 8f), rndSeedGen.Next(-100, 100));
mutationMultiplierGenerator = new UniformGenerator(new Range(-8f, 8f), rndSeedGen.Next(-100, 100));
fitnessFunction = new GameFitnessFunction();
selectionMethod = new EliteSelection();
padre = new gameChromosome(chromosomeGenerator, mutationMultiplierGenerator, mutationAdditionGenerator, numWeights);
poblacion = new Population(WorldGame.JUGADORES, padre, fitnessFunction, selectionMethod);
}
/// <summary>
/// Runs learning epoch.
/// </summary>
///
/// <param name="input">Array of input vectors.</param>
/// <param name="output">Array of output vectors.</param>
///
/// <returns>Returns summary squared learning error for the entire epoch.</returns>
///
/// <remarks><para><note>While running the neural network's learning process, it is required to
/// pass the same <paramref name="input"/> and <paramref name="output"/> values for each
/// epoch. On the very first run of the method it will initialize evolutionary fitness
/// function with the given input/output. So, changing input/output in middle of the learning
/// process, will break it.</note></para></remarks>
///
public double RunEpoch( double[][] input, double[][] output )
{
Debug.Assert( input.Length > 0 );
Debug.Assert( output.Length > 0 );
Debug.Assert( input.Length == output.Length );
Debug.Assert( network.InputsCount == input.Length );
// check if it is a first run and create population if so
if ( population == null )
{
// sample chromosome
DoubleArrayChromosome chromosomeExample = new DoubleArrayChromosome(
chromosomeGenerator, mutationMultiplierGenerator, mutationAdditionGenerator,
numberOfNetworksWeights );
// create population ...
population = new Population( populationSize, chromosomeExample,
new EvolutionaryFitness( network, input, output ), selectionMethod );
// ... and configure it
population.CrossoverRate = crossOverRate;
population.MutationRate = mutationRate;
population.RandomSelectionPortion = randomSelectionRate;
}
// run genetic epoch
population.RunEpoch( );
// get best chromosome of the population
DoubleArrayChromosome chromosome = (DoubleArrayChromosome) population.BestChromosome;
double[] chromosomeGenes = chromosome.Value;
// put best chromosome's value into neural network's weights
int v = 0;
for ( int i = 0; i < network.Layers.Length; i++ )
{
Layer layer = network.Layers[i];
for ( int j = 0; j < layer.Neurons.Length; j++ )
{
ActivationNeuron neuron = layer.Neurons[j] as ActivationNeuron;
for ( int k = 0; k < neuron.Weights.Length; k++ )
{
neuron.Weights[k] = chromosomeGenes[v++];
}
neuron.Threshold = chromosomeGenes[v++];
}
}
Debug.Assert( v == numberOfNetworksWeights );
return 1.0 / chromosome.Fitness;
}
// Worker thread
void SearchSolution( )
{
// create fitness function
SymbolicRegressionFitness fitness = new SymbolicRegressionFitness( data, new double[] { 1, 2, 3, 5, 7 } );
// create gene function
IGPGene gene = ( functionsSet == 0 ) ?
(IGPGene) new SimpleGeneFunction( 6 ) :
(IGPGene) new ExtendedGeneFunction( 6 );
// create population
Population population = new Population( populationSize,
( geneticMethod == 0 ) ?
(IChromosome) new GPTreeChromosome( gene ) :
(IChromosome) new GEPChromosome( gene, 15 ),
fitness,
( selectionMethod == 0 ) ? (ISelectionMethod) new EliteSelection( ) :
( selectionMethod == 1 ) ? (ISelectionMethod) new RankSelection( ) :
(ISelectionMethod) new RouletteWheelSelection( )
);
// iterations
int i = 1;
// solution array
double[,] solution = new double[50, 2];
double[] input = new double[6] { 0, 1, 2, 3, 5, 7 };
// calculate X values to be used with solution function
for ( int j = 0; j < 50; j++ )
{
solution[j, 0] = chart.RangeX.Min + (double) j * chart.RangeX.Length / 49;
}
// loop
while ( !needToStop )
{
// run one epoch of genetic algorithm
population.RunEpoch( );
try
{
// get best solution
string bestFunction = population.BestChromosome.ToString( );
// calculate best function
for ( int j = 0; j < 50; j++ )
{
input[0] = solution[j, 0];
solution[j, 1] = PolishExpression.Evaluate( bestFunction, input );
}
chart.UpdateDataSeries( "solution", solution );
// calculate error
double error = 0.0;
for ( int j = 0, k = data.GetLength( 0 ); j < k; j++ )
{
input[0] = data[j, 0];
error += Math.Abs( data[j, 1] - PolishExpression.Evaluate( bestFunction, input ) );
}
// set current iteration's info
SetText( currentIterationBox, i.ToString( ) );
SetText( currentErrorBox, error.ToString( "F3" ) );
}
catch
{
// remove any solutions from chart in case of any errors
chart.UpdateDataSeries( "solution", null );
}
// increase current iteration
i++;
//
if ( ( iterations != 0 ) && ( i > iterations ) )
break;
}
// show solution
SetText( solutionBox, population.BestChromosome.ToString( ) );
// enable settings controls
EnableControls( true );
}
private void OnWorkerDoWorkEvolutionary(object sender, DoWorkEventArgs e)
{
ArrayList parameters = e.Argument as ArrayList;
Debug.Assert(parameters != null);
bool useRed = (bool)parameters[0], useGreen = (bool)parameters[1], useBlue = (bool)parameters[2];
List<int> slotSizes = (List<int>)parameters[3];
ProgramFitnessFunction fitnessFunction = new ProgramFitnessFunction(this.puzzle, slotSizes);
ISelectionMethod selectionMethod = new EliteSelection();
const int PopulationSize = 500;
Population population = new Population(
PopulationSize,
new ProgramChromosome(slotSizes.Sum(), slotSizes.Count, useRed, useGreen, useBlue),
fitnessFunction,
selectionMethod);
int iteration = 0;
population.MutationRate = 0.6;
population.CrossoverRate = 0.6;
while (true)
{
population.RunEpoch();
backgroundWorker.ReportProgress(++iteration, population.FitnessMax - 1);
if (backgroundWorker.CancellationPending)
{
e.Cancel = true;
return;
}
if (population.FitnessMax - 1 == this.puzzle.StarsCount)
{
e.Result = fitnessFunction.ChromosomeToProgram((ProgramChromosome)population.BestChromosome);
return;
}
}
}
