public void TestInitialize()
{
mockModelInitializer = new Mock <IModelInitializer>(MockBehavior.Strict);
mockModelExecuter = new Mock <IModelExecuter>(MockBehavior.Strict);
mockPopulationBreeder = new Mock <IPopulationBreeder>(MockBehavior.Strict);
random = new ThreadSafeRandom();
sut = new GeneticLearner(populationSize, selectionSize, mockModelInitializer.Object, mockModelExecuter.Object, mockPopulationBreeder.Object, random);
}
public void TestInitialize()
{
random = new ThreadSafeRandom();
modelInitializer = new FullyConnectedNeuralNetworkInitializer(random);
sigmoidActivationFunction = new SigmoidActivationFunction();
modelExecuter = new FullyConnectedNeuralNetworkExecuter(sigmoidActivationFunction);
modelBreeder = new GeneticModelBreeder(modelInitializer, random);
populationBreeder = new PolygamousPopulationBreeder(modelBreeder, random);
sut = new GeneticLearner(populationSize, selectionSize, modelInitializer, modelExecuter, populationBreeder, random);
}
public void TestInitialize()
{
random = new ThreadSafeRandom();
mockModelInitializer = new Mock <IModelInitializer>(MockBehavior.Strict);
mockModelExecuter = new Mock <IModelExecuter>(MockBehavior.Strict);
mockPopulationBreeder = new Mock <IPopulationBreeder>(MockBehavior.Strict);
population = new List <FullyConnectedNeuralNetworkModel>();
sut = new GeneticLearner(populationSize, selectionSize, mockModelInitializer.Object, mockModelExecuter.Object, mockPopulationBreeder.Object, random);
mockModelInitializer.Setup(m => m.CreateModel(It.Is <int[]>(it => it != null && activationCountsPerLayer.SequenceEqual(it)), activationFunction))
.Returns(() =>
{
var model = CreateFullyConnectedNeuralNetworkModel();
population.Add(model);
return(model);
});
}
