public void GridSearchOptimizer_OptimizeBest() { var parameters = new double[][] { new double[] { 10.0, 20.0, 30.0, 35.0, 37.5, 40.0, 50.0, 60.0 } }; var sut = new GridSearchOptimizer(parameters); var actual = sut.OptimizeBest(Minimize); Assert.AreEqual(111.20889999999987, actual.Error, 0.00001); CollectionAssert.AreEqual(new double[] { 37.5 }, actual.ParameterSet); }
public async Task <IterationResult> Start(IOptimizerConfiguration config, CancellationToken cancellationToken) { CancellationToken = cancellationToken; var parameters = config.Genes.Select(s => new MinMaxParameterSpec(min: s.Min ?? s.Actual.Value, max: s.Max ?? s.Actual.Value, transform: Transform.Linear, parameterType: s.Precision > 0 ? ParameterType.Continuous : ParameterType.Discrete) ).ToArray(); Keys = config.Genes.Where(g => g.Key != "id").Select(s => s.Key); IOptimizer optimizerMethod = null; if (config.Fitness != null) { if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.RandomSearch.ToString()) { optimizerMethod = new RandomSearchOptimizer(parameters, iterations: config.Generations, seed: 42, runParallel: false); } else if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.ParticleSwarm.ToString()) { optimizerMethod = new ParticleSwarmOptimizer(parameters, maxIterations: config.Generations, numberOfParticles: config.PopulationSize, seed: 42, maxDegreeOfParallelism: 1); } else if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Bayesian.ToString()) { optimizerMethod = new BayesianOptimizer(parameters: parameters, iterations: config.Generations, randomStartingPointCount: config.PopulationSize, functionEvaluationsPerIterationCount: config.PopulationSize, seed: 42, runParallel: false); } else if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GlobalizedBoundedNelderMead.ToString()) { optimizerMethod = new GlobalizedBoundedNelderMeadOptimizer(parameters, maxRestarts: config.Generations, maxIterationsPrRestart: config.PopulationSize, seed: 42, maxDegreeOfParallelism: 1); } else if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GridSearch.ToString()) { optimizerMethod = new GridSearchOptimizer(config.Genes.Select(s => new GridParameterSpec(RangeWithPrecision.Range(s.Min.Value, s.Max.Value, s.Precision.Value).ToArray())).ToArray(), runParallel: false); } } else { throw new ArgumentException("No optimizer was configured."); } var result = await optimizerMethod.OptimizeBest(Minimize); return(new IterationResult { ParameterSet = result.ParameterSet, Cost = IsMaximizing ? result.Error * -1 : result.Error }); }
public void GridSearchOptimizer_Optimize() { var parameters = new double[][] { new double[] { 10.0, 37.5 } }; var sut = new GridSearchOptimizer(parameters); var actual = sut.Optimize(Minimize); var expected = new OptimizerResult[] { new OptimizerResult(new double[] { 37.5 }, 111.20889999999987), new OptimizerResult(new double[] { 10 }, 31638.9579) }; Assert.AreEqual(expected.First().Error, actual.First().Error, 0.0001); Assert.AreEqual(expected.First().ParameterSet.First(), actual.First().ParameterSet.First(), 0.0001); Assert.AreEqual(expected.Last().Error, actual.Last().Error, 0.0001); Assert.AreEqual(expected.Last().ParameterSet.First(), actual.Last().ParameterSet.First(), 0.0001); }
public void GridSearchOptimizer_ArgumentCheck_ParameterRanges() { var sut = new GridSearchOptimizer(null, false); }
public override double Evaluate(IChromosome chromosome) { try { var parameters = Config.Genes.Select(s => new MinMaxParameterSpec(min: (double)(s.MinDecimal ?? s.MinInt.Value), max: (double)(s.MaxDecimal ?? s.MaxInt.Value), transform: Transform.Linear, parameterType: s.Precision > 0 ? ParameterType.Continuous : ParameterType.Discrete) ).ToArray(); IOptimizer optimizer = null; if (Config.Fitness != null) { if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.RandomSearch.ToString()) { optimizer = new RandomSearchOptimizer(parameters, iterations: Config.Generations, seed: 42, maxDegreeOfParallelism: Config.MaxThreads); } else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.ParticleSwarm.ToString()) { optimizer = new ParticleSwarmOptimizer(parameters, maxIterations: Config.Generations, numberOfParticles: Config.PopulationSize, seed: 42, maxDegreeOfParallelism: Config.MaxThreads); } else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Bayesian.ToString()) { optimizer = new BayesianOptimizer(parameters: parameters, iterations: Config.Generations, randomStartingPointCount: Config.PopulationSize, functionEvaluationsPerIterationCount: Config.PopulationSize, seed: 42); //optimizer = new BayesianOptimizer(parameters, iterations: Config.Generations, randomStartingPointCount: Config.PopulationSize, // functionEvaluationsPerIteration: Config.MaxThreads, seed: 42, maxDegreeOfParallelism: Config.MaxThreads, allowMultipleEvaluations: true); } else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GlobalizedBoundedNelderMead.ToString()) { optimizer = new GlobalizedBoundedNelderMeadOptimizer(parameters, maxRestarts: Config.Generations, maxIterationsPrRestart: Config.PopulationSize, seed: 42, maxDegreeOfParallelism: Config.MaxThreads); } else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Smac.ToString()) { optimizer = new SmacOptimizer(parameters, iterations: Config.Generations, randomSearchPointCount: Config.PopulationSize, seed: 42); } else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GridSearch.ToString()) { optimizer = new GridSearchOptimizer(parameters); } else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Genetic.ToString()) { throw new Exception("Genetic optimizer cannot be used with Sharpe Maximizer"); } } Func <double[], OptimizerResult> minimize = p => Minimize(p, (Chromosome)chromosome); // run optimizer _hasActualValues = true; var result = optimizer.OptimizeBest(minimize); Best = ToChromosome(result, chromosome); return(result.Error); } catch (Exception ex) { LogProvider.ErrorLogger.Error(ex); return(ErrorFitness); } }