public IPredictionResult Predict <TDistribution>(IHiddenMarkovModel <TDistribution> model, IPredictionRequest request) where TDistribution : IDistribution
{
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var evaluator = new HmmEvaluator <TDistribution>(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters
{
CrossOverProbability = CrossoverProbability,
MutationProbability = MutationProbability,
NumberOfGenerations = NumberOfGenerations,
PopulationSize = request.NumberOfDays * 10,
TournamentSize = TournamentSize
};
var predictions = new PredictionResult {
Predicted = new double[request.NumberOfDays][]
};
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
var populationInitializer = new FromTimeSeriesRandomInitializer(request.TrainingSet);
var population = populationInitializer.Initialize <decimal>(parameters.PopulationSize, request.NumberOfDays, MutationProbability);
for (int i = 0; i < population.Count; i++)
{
var chromosome = population[i];
population[i].FintnessValue = evaluator.Evaluate(chromosome);
}
var result = solver.Solve(population);
// Get best fitted chromosome
var maximumFitness = result[0].FintnessValue;
var solution = result[0];
foreach (var chromosome in result)
{
if (maximumFitness <= chromosome.FintnessValue)
{
solution = chromosome;
maximumFitness = chromosome.FintnessValue;
}
}
// Convert it to array
for (int i = 0; i < solution.Representation.Length; i++)
{
predictions.Predicted[i] = Array.ConvertAll(solution.Representation[i].Representation, x => (double)Convert.ChangeType(x, typeof(double)));
}
return(predictions);
}
public void Solve_PopulationSizeZero_ZeroSizePopulationReturned()
{
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var model = (HiddenMarkovModelMixtureDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <Mixture <IMultivariateDistribution> > {
Pi = new double[3], TransitionProbabilityMatrix = new double[3][], Emissions = new Mixture <IMultivariateDistribution> [3]
});
var evaluator = new HmmEvaluator <Mixture <IMultivariateDistribution> >(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters();
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
var result = solver.Solve(new List <IChromosome <decimal> >());
Assert.AreEqual(result.Count, 0);
}
public void GeneticSolver_AllDependencies_SolverCreatedWithAllDependecnies()
{
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var model = (HiddenMarkovModelMixtureDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <Mixture <IMultivariateDistribution> > {
Pi = new double[3], TransitionProbabilityMatrix = new double[3][], Emissions = new Mixture <IMultivariateDistribution> [3]
});
var evaluator = new HmmEvaluator <Mixture <IMultivariateDistribution> >(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters();
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
Assert.IsNotNull(solver.MutationAlgorithm);
Assert.IsNotNull(solver.CrossoverAlgorithm);
Assert.IsNotNull(solver.EvaluationMethod);
Assert.IsNotNull(solver.SelectionMethod);
}
public void Solve_InitialPopulationFromTimeSeriesAndTrainedHmmEvaluator_SolvedPopulation()
{
var util = new TestDataUtils();
var series = util.GetSvcData(util.GOOGFilePath, new DateTime(2010, 12, 18), new DateTime(2011, 12, 18));
var test = util.GetSvcData(util.GOOGFilePath, new DateTime(2011, 12, 18), new DateTime(2012, 01, 18));
var model = (HiddenMarkovModelMixtureDistribution)HiddenMarkovModelFactory.GetModel(new ModelCreationParameters <Mixture <IMultivariateDistribution> >()
{
NumberOfComponents = NumberOfComponents, NumberOfStates = NumberOfStates
});
model.Normalized = true;
model.Train(series, NumberOfIterations, LikelihoodTolerance);
var selectionMethod = new TournamentSelection(TournamentSize);
var crossoverAlgorithm = new Crossover(CrossoverProbability);
var mutationAlgorithm = new Mutator(MutationProbability);
var evaluator = new HmmEvaluator <Mixture <IMultivariateDistribution> >(model, new ForwardBackward(true));
var parameters = new GeneticSolverParameters
{
CrossOverProbability = CrossoverProbability,
MutationProbability = MutationProbability,
NumberOfGenerations = 10,
PopulationSize = PopulationSize,
TournamentSize = TournamentSize
};
var populationInitializer = new FromTimeSeriesRandomInitializer(series);
var population = populationInitializer.Initialize <decimal>(parameters.PopulationSize, test.Length, MutationProbability);
for (int i = 0; i < population.Count; i++)
{
var chromosome = population[i];
population[i].FintnessValue = evaluator.Evaluate(chromosome);
}
var solver = new GeneticSolver(parameters, mutationAlgorithm, crossoverAlgorithm, evaluator, selectionMethod);
var result = solver.Solve(population);
Assert.AreEqual(result.Count, parameters.PopulationSize);
}
