/// <summary>
/// Generate a random genome.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="eval">The evaluator.</param>
/// <returns>The random genome.</returns>
private TreeGenome RandomGenome(IGenerateRandom rnd, EvaluateExpression eval)
{
var result = new TreeGenome(eval);
result.Root = eval.Grow(rnd, 5);
return(result);
}
/// <summary>
/// Create an initial random population.
/// </summary>
/// <param name="rnd">A random number generator.</param>
/// <param name="eval">The expression evaluator.</param>
/// <returns>The new population.</returns>
private IPopulation InitPopulation(IGenerateRandom rnd, EvaluateExpression eval)
{
IPopulation result = new BasicPopulation(PopulationSize, null);
var defaultSpecies = new BasicSpecies();
defaultSpecies.Population = result;
for (int i = 0; i < PopulationSize; i++)
{
TreeGenome genome = RandomGenome(rnd, eval);
defaultSpecies.Add(genome);
}
result.GenomeFactory = new TreeGenomeFactory(eval);
result.Species.Add(defaultSpecies);
return(result);
}
/// <summary>
/// Process the specified file.
/// </summary>
/// <param name="filename">The filename to process.</param>
public void Process(String filename)
{
// read the data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
Stream res = assembly.GetManifestResourceStream("AIFH_Vol2.Resources.simple-poly.csv");
// did we fail to read the resouce
if (res == null)
{
Console.WriteLine("Can't read iris data from embedded resources.");
return;
}
// load the data
var istream = new StreamReader(res);
DataSet ds = DataSet.Load(istream);
istream.Close();
// Extract supervised training.
IList <BasicData> training = ds.ExtractSupervised(0, 1, 1, 1);
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
var eval = new EvaluateExpression(rnd);
IPopulation pop = InitPopulation(rnd, eval);
IScoreFunction score = new ScoreSmallExpression(training, 30);
IEvolutionaryAlgorithm genetic = new BasicEA(pop, score);
genetic.AddOperation(0.3, new MutateTree(3));
genetic.AddOperation(0.7, new CrossoverTree());
genetic.ShouldIgnoreExceptions = false;
int sameSolutionCount = 0;
int iteration = 1;
double lastSolution = double.MaxValue;
var builder = new StringBuilder();
while (sameSolutionCount < MaxSameSolution && iteration < 1000)
{
genetic.Iteration();
double thisSolution = genetic.LastError;
builder.Length = 0;
builder.Append("Iteration: ");
builder.Append(iteration++);
builder.Append(", Current error = ");
builder.Append(thisSolution);
builder.Append(", Best Solution Length = ");
builder.Append(genetic.BestGenome.Count);
Console.WriteLine(builder.ToString());
if (Math.Abs(lastSolution - thisSolution) < 1.0)
{
sameSolutionCount++;
}
else
{
sameSolutionCount = 0;
}
lastSolution = thisSolution;
}
Console.WriteLine("Good solution found:");
var best = (TreeGenome)genetic.BestGenome;
Console.WriteLine(eval.DisplayExpressionNormal(best.Root));
genetic.FinishTraining();
}
/// <summary>
/// Create an initial random population.
/// </summary>
/// <param name="rnd">A random number generator.</param>
/// <param name="eval">The expression evaluator.</param>
/// <returns>The new population.</returns>
private IPopulation InitPopulation(IGenerateRandom rnd, EvaluateExpression eval)
{
IPopulation result = new BasicPopulation(PopulationSize, null);
var defaultSpecies = new BasicSpecies();
defaultSpecies.Population = result;
for (int i = 0; i < PopulationSize; i++)
{
TreeGenome genome = RandomGenome(rnd, eval);
defaultSpecies.Add(genome);
}
result.GenomeFactory = new TreeGenomeFactory(eval);
result.Species.Add(defaultSpecies);
return result;
}
/// <summary>
/// Generate a random genome.
/// </summary>
/// <param name="rnd">Random number generator.</param>
/// <param name="eval">The evaluator.</param>
/// <returns>The random genome.</returns>
private TreeGenome RandomGenome(IGenerateRandom rnd, EvaluateExpression eval)
{
var result = new TreeGenome(eval);
result.Root = eval.Grow(rnd, 5);
return result;
}
/// <summary>
/// Process the specified file.
/// </summary>
/// <param name="filename">The filename to process.</param>
public void Process(String filename)
{
// read the data from the resources
Assembly assembly = Assembly.GetExecutingAssembly();
Stream res = assembly.GetManifestResourceStream("AIFH_Vol2.Resources.simple-poly.csv");
// did we fail to read the resouce
if (res == null)
{
Console.WriteLine("Can't read iris data from embedded resources.");
return;
}
// load the data
var istream = new StreamReader(res);
DataSet ds = DataSet.Load(istream);
istream.Close();
// Extract supervised training.
IList<BasicData> training = ds.ExtractSupervised(0, 1, 1, 1);
IGenerateRandom rnd = new MersenneTwisterGenerateRandom();
var eval = new EvaluateExpression(rnd);
IPopulation pop = InitPopulation(rnd, eval);
IScoreFunction score = new ScoreSmallExpression(training, 30);
IEvolutionaryAlgorithm genetic = new BasicEA(pop, score);
genetic.AddOperation(0.3, new MutateTree(3));
genetic.AddOperation(0.7, new CrossoverTree());
genetic.ShouldIgnoreExceptions = false;
int sameSolutionCount = 0;
int iteration = 1;
double lastSolution = double.MaxValue;
var builder = new StringBuilder();
while (sameSolutionCount < MaxSameSolution && iteration < 1000)
{
genetic.Iteration();
double thisSolution = genetic.LastError;
builder.Length = 0;
builder.Append("Iteration: ");
builder.Append(iteration++);
builder.Append(", Current error = ");
builder.Append(thisSolution);
builder.Append(", Best Solution Length = ");
builder.Append(genetic.BestGenome.Count);
Console.WriteLine(builder.ToString());
if (Math.Abs(lastSolution - thisSolution) < 1.0)
{
sameSolutionCount++;
}
else
{
sameSolutionCount = 0;
}
lastSolution = thisSolution;
}
Console.WriteLine("Good solution found:");
var best = (TreeGenome) genetic.BestGenome;
Console.WriteLine(eval.DisplayExpressionNormal(best.Root));
genetic.FinishTraining();
}