public void MyTestMethod()
{
var random = new RandomImplementation(23);
INeatConfiguration configuration = new DefaultNeatConfiguration(500);
var nodeInovator = new InnovationGenerator(1);
var ConnectionInovator = new InnovationGenerator(1);
var genomeProvider = new GenomeProvider(nodeInovator, ConnectionInovator, random);
var crossFunction = new NeatCrossFunction(random, configuration);
var weightMutation = new ApplyWeightMutation(random, configuration);
var addNodeMutation = new AddNodeMutation(nodeInovator, ConnectionInovator, random);
var addConnectionMutation = new AddConnectionMutation(ConnectionInovator, random, configuration);
var evaluator = new WeightOfAHundred(configuration, genomeProvider, nodeInovator, ConnectionInovator, addConnectionMutation, addNodeMutation, weightMutation, crossFunction, random);
for (int i = 0; i < 1000; i++)
{
evaluator.EvaluateGeneration();
Console.WriteLine($"Best Fitness: {evaluator.FittestGenome.Fitness}");
}
Assert.That(evaluator.FittestGenome.Fitness > 90);
}
public void test()
{
var random = new RandomImplementation(23);
INeatConfiguration configuration = new DefaultNeatConfiguration(500);
var nodeInovator = new InnovationGenerator(1);
var connectionInovator = new InnovationGenerator(1);
var problemDomain = new ProblemDomain(
inputs:
new double[, ]
{
{ 0, 0, 1 },
{ 0, 1, 1 },
{ 1, 0, 1 },
{ 1, 1, 1 }
},
outputs:
new double[, ]
{
{ 0 },
{ 1 },
{ 1 },
{ 0 }
});
var genome = CreateOriginalGenome(nodeInovator, connectionInovator, problemDomain);
var genomeProvider = new XORNNProvider(nodeInovator, connectionInovator, random, genome);
var crossFunction = new NeatCrossFunction(random, configuration);
var weightMutation = new ApplyWeightMutation(random, configuration);
var addNodeMutation = new AddNodeMutation(nodeInovator, connectionInovator, random);
var addConnectionMutation = new AddConnectionMutation(connectionInovator, random, configuration);
var evaluator = new XORNNEvaluator(configuration, genomeProvider, nodeInovator, connectionInovator, addConnectionMutation, addNodeMutation, weightMutation, crossFunction, random, problemDomain);
for (int i = 0; i < 1000; i++)
{
evaluator.EvaluateGeneration();
Console.WriteLine($"Best Fitness: {evaluator.FittestGenome.Fitness}");
}
Assert.That(evaluator.FittestGenome.Fitness > 90);
}
