public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new KMeansClusteringStrategy <NeatGenome>(distanceMetric);
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create black box evaluator
SimpleEvaluator evaluator = new SimpleEvaluator(_optimizer);
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
IGenomeListEvaluator <NeatGenome> innerEvaluator = new UnityParallelListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, evaluator, _optimizer);
IGenomeListEvaluator <NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator <NeatGenome>(innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
//ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
ea.Initialize(innerEvaluator, genomeFactory, genomeList);
return(ea);
}
public void WillReturnHigherScoreForBetterBoard()
{
int[,] data1 = new int[,] {
{9,6,2,7,1,5,8,3,4},
{4,7,8,9,6,3,5,1,2},
{3,1,5,4,2,8,6,9,7},
{1,2,7,6,5,9,3,4,8},
{8,9,4,2,3,1,7,5,6},
{5,3,6,8,7,4,9,2,1},
{6,5,3,1,4,7,2,8,9},
{7,8,1,3,9,2,4,0,5},
{2,4,9,5,8,6,1,7,0}
};
int[,] data2 = new int[,] {
{9,6,2,7,1,5,8,3,4},
{4,7,8,9,6,3,5,1,2},
{3,1,5,4,2,8,6,9,7},
{1,2,7,6,5,9,3,4,8},
{8,9,4,2,3,1,7,5,6},
{5,3,6,8,7,4,9,2,1},
{6,5,3,1,4,7,0,8,9},
{7,8,1,3,9,2,4,0,5},
{2,4,9,5,8,6,1,7,0}
};
var eval = new SimpleEvaluator(new SimpleMoveFinder());
var score1 = eval.GetScore(new Board(data1));
var score2 = eval.GetScore(new Board(data2));
Assert.True(score1.CompareTo(score2) > 0);
}
static void Main(string[] args)
{
//TODO: akolyada: introduce DI
var se = new SimpleEvaluator();
IExpressionsReader rdr = new FileExpressionsReader("../../input_numbers.txt");
List <Tuple <string, decimal> > results = new List <Tuple <string, decimal> >();
foreach (string expression in rdr)
{
Console.Write($"{expression} = ");
string expr = expression;
var res = se.Evaluate(expr);
results.Add(new Tuple <string, decimal>(expr, res));
Console.WriteLine(res);
}
File.WriteAllLines($"../../calculationResults_{DateTime.Now.Ticks}.txt", results.Select(n => $"{n.Item1} = {n.Item2}").ToArray());
Console.WriteLine();
Console.Write("Press any key...");
Console.ReadKey();
}
public NeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory<NeatGenome> genomeFactory, List<NeatGenome> genomeList)
{
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy<NeatGenome> speciationStrategy = new KMeansClusteringStrategy<NeatGenome>(distanceMetric);
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
NeatEvolutionAlgorithm<NeatGenome> ea = new NeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create black box evaluator
SimpleEvaluator evaluator = new SimpleEvaluator(_optimizer);
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
IGenomeListEvaluator<NeatGenome> innerEvaluator = new UnityParallelListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _optimizer);
IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(innerEvaluator,
SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
//ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
ea.Initialize(innerEvaluator, genomeFactory, genomeList);
return ea;
}
public void Setup()
{
var se = new SimpleEvaluator(new SilentExpressionEvaluator());
}
