public void Eval(String start, String expect)
{
EncogProgramContext context = new EncogProgramContext();
StandardExtensions.CreateNumericOperators(context);
PrgPopulation pop = new PrgPopulation(context, 1);
ICalculateScore score = new ZeroEvalScoreFunction();
TrainEA genetic = new TrainEA(pop, score);
genetic.ValidationMode = true;
genetic.CODEC = new PrgCODEC();
genetic.AddOperation(0.95, new SubtreeCrossover());
genetic.AddOperation(0.05, new SubtreeMutation(context, 4));
genetic.AddScoreAdjuster(new ComplexityAdjustedScore());
genetic.Rules.AddRewriteRule(new RewriteConstants());
genetic.Rules.AddRewriteRule(new RewriteAlgebraic());
EncogProgram expression = new EncogProgram(context);
expression.CompileExpression(start);
RenderCommonExpression render = new RenderCommonExpression();
genetic.Rules.Rewrite(expression);
Assert.AreEqual(expect, render.Render(expression));
}
/// <summary>
/// Create an EPL GA trainer.
/// </summary>
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
PrgPopulation pop = (PrgPopulation)method;
ICalculateScore score = new TrainingSetScore(training);
TrainEA train = new TrainEA(pop, score);
train.Rules.AddRewriteRule(new RewriteConstants());
train.Rules.AddRewriteRule(new RewriteAlgebraic());
train.CODEC = new PrgCODEC();
train.AddOperation(0.8, new SubtreeCrossover());
train.AddOperation(0.1, new SubtreeMutation(pop.Context, 4));
train.AddOperation(0.1, new ConstMutation(pop.Context, 0.5, 1.0));
train.AddScoreAdjuster(new ComplexityAdjustedScore());
train.Speciation = new PrgSpeciation();
return(train);
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
IMLDataSet trainingData = GenerationUtil.GenerateSingleDataRange(
(x) => (3 * Math.Pow(x, 2) + (12 * x) + 4)
, 0, 100, 1);
EncogProgramContext context = new EncogProgramContext();
context.DefineVariable("x");
StandardExtensions.CreateNumericOperators(context);
PrgPopulation pop = new PrgPopulation(context, 1000);
MultiObjectiveFitness score = new MultiObjectiveFitness();
score.AddObjective(1.0, new TrainingSetScore(trainingData));
TrainEA genetic = new TrainEA(pop, score);
genetic.ValidationMode = true;
genetic.CODEC = new PrgCODEC();
genetic.AddOperation(0.5, new SubtreeCrossover());
genetic.AddOperation(0.25, new ConstMutation(context, 0.5, 1.0));
genetic.AddOperation(0.25, new SubtreeMutation(context, 4));
genetic.AddScoreAdjuster(new ComplexityAdjustedScore(10, 20, 10, 20.0));
genetic.Rules.AddRewriteRule(new RewriteConstants());
genetic.Rules.AddRewriteRule(new RewriteAlgebraic());
genetic.Speciation = new PrgSpeciation();
(new RampedHalfAndHalf(context, 1, 6)).Generate(new EncogRandom(), pop);
genetic.ShouldIgnoreExceptions = false;
EncogProgram best = null;
genetic.ThreadCount = 1;
try
{
for (int i = 0; i < 1000; i++)
{
genetic.Iteration();
best = (EncogProgram)genetic.BestGenome;
Console.Out.WriteLine(genetic.IterationNumber + ", Error: "
+ best.Score + ",Best Genome Size:" + best.Size
+ ",Species Count:" + pop.Species.Count + ",best: " + best.DumpAsCommonExpression());
}
//EncogUtility.evaluate(best, trainingData);
Console.Out.WriteLine("Final score:" + best.Score
+ ", effective score:" + best.AdjustedScore);
Console.Out.WriteLine(best.DumpAsCommonExpression());
//pop.dumpMembers(Integer.MAX_VALUE);
//pop.dumpMembers(10);
}
catch (Exception t)
{
Console.Out.WriteLine(t.ToString());
}
finally
{
genetic.FinishTraining();
EncogFramework.Instance.Shutdown();
}
}