public void SmacOptimizer_Optimize()
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(0.0, 100.0, Transform.Linear)
};
var sut = new SmacOptimizer(parameters,
iterations: 80,
randomStartingPointCount: 20,
functionEvaluationsPerIterationCount: 1,
localSearchPointCount: 10,
randomSearchPointCount: 1000,
epsilon: 0.00001,
seed: 42);
var actual = sut.Optimize(MinimizeWeightFromHeight);
var expected = new OptimizerResult[]
{
new OptimizerResult(new double[] { 90.513222660177 }, 114559.431919558),
new OptimizerResult(new double[] { 41.8333740634068 }, 806.274612132759),
};
Assert.AreEqual(expected.First().Error, actual.First().Error, Delta);
Assert.AreEqual(expected.First().ParameterSet.First(), actual.First().ParameterSet.First(), Delta);
Assert.AreEqual(expected.Last().Error, actual.Last().Error, Delta);
Assert.AreEqual(expected.Last().ParameterSet.First(), actual.Last().ParameterSet.First(), Delta);
}
public void SmacOptimizer_OptimizeBest_MultipleParameters()
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(-10.0, 10.0, Transform.Linear),
new MinMaxParameterSpec(-10.0, 10.0, Transform.Linear),
new MinMaxParameterSpec(-10.0, 10.0, Transform.Linear),
};
var sut = new SmacOptimizer(parameters,
iterations: 80,
randomStartingPointCount: 20,
functionEvaluationsPerIterationCount: 1,
localSearchPointCount: 10,
randomSearchPointCount: 1000,
epsilon: 0.00001,
seed: 42);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(-0.964878416222769, actual.Error, Delta);
Assert.AreEqual(actual.ParameterSet.Length, 3);
Assert.AreEqual(-7.8487638560350819, actual.ParameterSet[0], Delta);
Assert.AreEqual(6.2840940040927826, actual.ParameterSet[1], Delta);
Assert.AreEqual(0.036385473812179825, actual.ParameterSet[2], Delta);
}
public void SmacOptimizer_OptimizeBest_SingleParameter()
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(0.0, 100.0, Transform.Linear)
};
var sut = new SmacOptimizer(parameters,
iterations: 80,
randomStartingPointCount: 20,
functionEvaluationsPerIterationCount: 1,
localSearchPointCount: 10,
randomSearchPointCount: 1000,
epsilon: 0.00001,
seed: 42);
var actual = sut.OptimizeBest(MinimizeWeightFromHeight);
Assert.AreEqual(109.616853578648, actual.Error, Delta);
Assert.AreEqual(37.6315924979893, actual.ParameterSet.Single(), Delta);
}
OptimizerResult RunOpenLoopOptimizationTest(List <OptimizerResult> results)
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(-10.0, 10.0, Transform.Linear),
new MinMaxParameterSpec(-10.0, 10.0, Transform.Linear),
new MinMaxParameterSpec(-10.0, 10.0, Transform.Linear),
};
var iterations = 80;
var randomStartingPointsCount = 20;
var functionEvaluationsPerIterationCount = 1;
var sut = new SmacOptimizer(parameters,
iterations: iterations,
randomStartingPointCount: randomStartingPointsCount,
functionEvaluationsPerIterationCount: functionEvaluationsPerIterationCount,
localSearchPointCount: 10,
randomSearchPointCount: 1000,
epsilon: 0.00001,
seed: 42);
// Using SmacOptimizer in an open loop.
var initialParameterSets = sut.ProposeParameterSets(randomStartingPointsCount, results);
var initializationResults = sut.RunParameterSets(Minimize, initialParameterSets);
results.AddRange(initializationResults);
for (int i = 0; i < iterations; i++)
{
var parameterSets = sut.ProposeParameterSets(functionEvaluationsPerIterationCount, results);
var iterationResults = sut.RunParameterSets(Minimize, parameterSets);
results.AddRange(iterationResults);
}
return(results.Where(v => !double.IsNaN(v.Error)).OrderBy(r => r.Error).First());
}
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);
}
}
public void SmacOptimizer_ArgumentCheck_RandomSearchPointCount()
{
var sut = new SmacOptimizer(new[] { new GridParameterSpec(0, 1, 2) },
10, 20, 30, 40, 0);
}
public void SmacOptimizer_ArgumentCheck_FunctionEvaluationsPerIterationCount()
{
var sut = new SmacOptimizer(new[] { new GridParameterSpec(0, 1, 2) },
10, 20, 0);
}
public void SmacOptimizer_ArgumentCheck_Iterations()
{
var sut = new SmacOptimizer(new[] { new GridParameterSpec(0, 1, 2) },
0);
}
public void SmacOptimizer_ArgumentCheck_ParameterRanges()
{
var sut = new SmacOptimizer(null, 20);
}
