/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome population and their associated/parent genome factory.
/// </summary>
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new KMeansClusteringStrategy <NeatGenome>(distanceMetric);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
TestEvaluator evaluator = new TestEvaluator();
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeListEvaluator <NeatGenome> innerEvaluator = new SerialGenomeListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, evaluator);
// Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
// that were in the population in previous generations (elite genomes). This is determiend by examining each genome's evaluation info object.
IGenomeListEvaluator <NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator <NeatGenome>(
innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(ea);
}
private void evaluateChromosome(int index, TestEvaluator evaluator)
{
Chromosome chromosome = population.getChromosomes()[index];
evaluator.startEvaluation(chromosome.getWeights(), index);
++chromosomesInEvaluation;
}
void Start()
{
TestEvaluator simulator = Instantiate(simulatorPrefab);
simulator.evolutionator = this;
simulators.Add(simulator);
population = new Population(null, CreateNeuralNet(simulators[0]), populationSize, .03, .5, .7);
runEvaluation();
}
private string BuildHeader(Subject subject)
{
TestEvaluator evaluation = new TestEvaluator();
string header = $"<head><meta charset='UTF8'> " +
"<style>table{font-family: arial, sans-serif;border-collapse: collapse;width:100%;}td,th{border: 1px solid #dddddd;text-align: left;padding: 8px;}tr:nth-child(even){background-color: #dddddd;}</style></head>" +
"<h1 style=text-align:center;background-color:lightblue;font-size:xx-large;>Teszt eredmények</h1>" +
"<p>tesztvezető neve:</p>" +
$"<p>tesztalany neve: {subject.Nickname}</p>" +
$"<p><b>életkor: {subject.Age}</b></p>" +
$"<p><b>nem: {subject.Gender.ToString()}</b></p>" +
$"<p><b>kitöltés időpontja: {subject.SessionStartDate.ToString("yyyy.MM.dd HH:mm:ss")}</b></p>" +
$"<p><i>eredmény: {evaluation.Evaluate(subject).ToString()}</i><p >" +
"<hr>";
return(header);
}
public void Test1()
{
TestEvaluator evaluator = new TestEvaluator();
var expression = BindingExpression.ParseExpression("{eval path=Name}");
Assert.AreEqual(expression.Name, "eval", "测试解析绑定表达式失败");
object value;
Assert.IsTrue(expression.TryGetValue(evaluator, "path", out value), "测试解析绑定表达式失败");
Assert.AreEqual(value, "Name", "测试解析绑定表达式失败");
expression = BindingExpression.ParseExpression("{eval path=Name , key==@#$}");
Assert.AreEqual(expression.Name, "eval", "测试解析绑定表达式失败");
Assert.IsTrue(expression.TryGetValue(evaluator, "path", out value), "测试解析绑定表达式失败");
Assert.AreEqual(value, "Name ", "测试解析绑定表达式失败");
Assert.IsTrue(expression.TryGetValue(evaluator, "key", out value), "测试解析绑定表达式失败");
Assert.AreEqual(value, "=@#$", "测试解析绑定表达式失败");
expression = BindingExpression.ParseExpression("{eval path={{abc,,}},key=abc}");
Assert.AreEqual(expression.Name, "eval", "测试解析绑定表达式失败");
Assert.IsTrue(expression.TryGetValue(evaluator, "path", out value), "测试解析绑定表达式失败");
Assert.AreEqual(value, "{abc,}", "测试解析绑定表达式失败");
Assert.IsTrue(expression.TryGetValue(evaluator, "key", out value), "测试解析绑定表达式失败");
Assert.AreEqual(value, "abc", "测试解析绑定表达式失败");
expression = BindingExpression.ParseExpression("{eval path={},,key=abc}");
Assert.IsNull(expression, "错误的解析了错误的表达式");
expression = BindingExpression.ParseExpression("{eval path={eval a=b, c=d},key=abc}");
Assert.AreEqual(expression.Name, "eval", "测试解析绑定表达式失败");
Assert.IsTrue(expression.TryGetValue(evaluator, "path", out value), "测试解析绑定表达式失败");
Assert.AreEqual(value, "a:b,c:d", "测试解析绑定表达式失败");
Assert.IsTrue(expression.TryGetValue(evaluator, "key", out value), "测试解析绑定表达式失败");
Assert.AreEqual(value, "abc", "测试解析绑定表达式失败");
expression = BindingExpression.ParseExpression("{eval a, b=}");
Assert.AreEqual(expression.Name, "eval", "测试解析绑定表达式失败");
Assert.IsTrue(expression.TryGetValue(evaluator, "a", out value), "测试解析绑定表达式失败");
Assert.IsNull(value, "测试解析绑定表达式失败");
Assert.IsTrue(expression.TryGetValue(evaluator, "b", out value), "测试解析绑定表达式失败");
Assert.AreEqual(value, "", "测试解析绑定表达式失败");
}
public void Test_SynchronizerConstruct_Steps()
{
Model model = new Model("SFC Test 1");
ProcedureFunctionChart pfc = new ProcedureFunctionChart(model, "SFC 1", "", Guid.NewGuid());
IPfcStepNode t0 = pfc.CreateStep("S_Alice", "", Guid.Empty);
IPfcStepNode t1 = pfc.CreateStep("S_Bob", "", Guid.Empty);
IPfcStepNode t2 = pfc.CreateStep("S_Charley", "", Guid.Empty);
IPfcStepNode t3 = pfc.CreateStep("S_David", "", Guid.Empty);
IPfcStepNode t4 = pfc.CreateStep("S_Edna", "", Guid.Empty);
IPfcStepNode t5 = pfc.CreateStep("S_Frank", "", Guid.Empty);
IPfcStepNode t6 = pfc.CreateStep("S_Gary", "", Guid.Empty);
IPfcStepNode t7 = pfc.CreateStep("S_Hailey", "", Guid.Empty);
pfc.Synchronize(new IPfcStepNode[] { t0, t1, t2, t3 }, new IPfcStepNode[] { t4, t5, t6, t7 });
string structureString = PfcDiagnostics.GetStructure(pfc);
structureString = structureString.Replace("SFC 1.Root", "SFC 1.Root");
string shouldBe = "{S_Alice-->[L_000(SFC 1.Root)]-->T_000}\r\n{S_Bob-->[L_001(SFC 1.Root)]-->T_000}\r\n{S_Charley-->[L_002(SFC 1.Root)]-->T_000}\r\n{S_David-->[L_003(SFC 1.Root)]-->T_000}\r\n{T_000-->[L_004(SFC 1.Root)]-->S_Edna}\r\n{T_000-->[L_005(SFC 1.Root)]-->S_Frank}\r\n{T_000-->[L_006(SFC 1.Root)]-->S_Gary}\r\n{T_000-->[L_007(SFC 1.Root)]-->S_Hailey}\r\n";
Console.WriteLine("After a synchronization of steps, structure is \r\n" + structureString);
Assert.AreEqual(structureString, shouldBe, "Structure should have been\r\n" + shouldBe + "\r\nbut it was\r\n" + structureString + "\r\ninstead.");
if (m_runSFCs)
{
TestEvaluator testEvaluator = new TestEvaluator(new IPfcStepNode[] { t0, t1, t2, t3, t4, t5, t6, t7 });
pfc.Synchronize(new IPfcStepNode[] { t0, t1, t2, t3 }, new IPfcStepNode[] { t4, t5, t6, t7 });
testEvaluator.NextExpectedActivations = new IPfcStepNode[] { t0, t1, t2, t3, t4, t5, t6, t7 };
foreach (IPfcNode ilinkable in new IPfcStepNode[] { t0, t1, t2, t3 })
{
Console.WriteLine("Incrementing " + ilinkable.Name + ".");
//ilinkable.Increment();
throw new ApplicationException("PFCs are not currently executable.");
}
testEvaluator.NextExpectedActivations = new IPfcTransitionNode[] { }; // Ensure it's empty and all have fired.
}
}
public void reportFitness(TestEvaluator evaluator, double fitness, int index)
{
totalFitness += fitness;
--chromosomesInEvaluation;
Chromosome chromosome = population.getChromosomes()[index];
chromosome.setFitness(fitness);
print("current index: " + currentChromosomeIndex);
if (currentChromosomeIndex < populationSize - 1)
{
if (currentChromosomeIndex + chromosomesInEvaluation < populationSize - 1)
{
evaluateChromosome(currentChromosomeIndex + chromosomesInEvaluation + 1, evaluator);
}
++currentChromosomeIndex;
}
else
{
print("restart generation");
startNextGeneration();
}
}
public static NeuralNet CreateNeuralNet(TestEvaluator evaluator)
{
return(new NeuralNet(NeuronMode.NEURON, true, 2, evaluator.getOutputsRequired(), 7, 2));
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome population and their associated/parent genome factory.
/// </summary>
public NeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory<NeatGenome> genomeFactory, List<NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy<NeatGenome> speciationStrategy = new KMeansClusteringStrategy<NeatGenome>(distanceMetric);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm<NeatGenome> ea = new NeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
TestEvaluator evaluator = new TestEvaluator();
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeListEvaluator<NeatGenome> innerEvaluator = new SerialGenomeListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator);
// Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
// that were in the population in previous generations (elite genomes). This is determiend by examining each genome's evaluation info object.
IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(
innerEvaluator,
SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return ea;
}
