public void BayesianOptimizer_OptimizeBest_MultipleParameters(bool runParallel)
{
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 BayesianOptimizer(parameters,
iterations: 100,
randomStartingPointCount: 5,
functionEvaluationsPerIterationCount: 1,
randomSearchPointCount: 1000,
seed: 42,
runParallel: runParallel);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(-0.76070603822760785, actual.Error, Delta);
Assert.AreEqual(3, actual.ParameterSet.Length);
Assert.AreEqual(1.6078245041928358, actual.ParameterSet[0], Delta);
Assert.AreEqual(-8.9735394990879769, actual.ParameterSet[1], Delta);
Assert.AreEqual(-0.18217921731163855, actual.ParameterSet[2], Delta);
}
public void BayesianOptimizer_OptimizeBest()
{
var parameters = new double[][]
{
new double[] { -10.0, 10.0 },
new double[] { -10.0, 10.0 },
new double[] { -10.0, 10.0 }
};
var sut = new BayesianOptimizer(parameters, 100, 5, 1);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(actual.Error, -0.92327107866106661, 0.0001);
Assert.AreEqual(actual.ParameterSet.Length, 3);
Assert.AreEqual(actual.ParameterSet[0], 8.1239613509382878, 0.0001);
Assert.AreEqual(actual.ParameterSet[1], -9.2896384835660637, 0.0001);
Assert.AreEqual(actual.ParameterSet[2], -0.03435398919245003, 0.0001);
}
public void BayesianOptimizer_OptimizeBest()
{
var parameters = new ParameterBounds[]
{
new ParameterBounds(-10.0, 10.0, Transform.Linear),
new ParameterBounds(-10.0, 10.0, Transform.Linear),
new ParameterBounds(-10.0, 10.0, Transform.Linear),
};
var sut = new BayesianOptimizer(parameters, 100, 5, 1);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(actual.Error, -0.74765422244251278, 0.0001);
Assert.AreEqual(actual.ParameterSet.Length, 3);
Assert.AreEqual(actual.ParameterSet[0], -5.0065683270835173, m_delta);
Assert.AreEqual(actual.ParameterSet[1], -9.67008227467075, m_delta);
Assert.AreEqual(actual.ParameterSet[2], -0.24173704452893574, m_delta);
}
public void BayesianOptimizer_OptimizeBest()
{
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 BayesianOptimizer(parameters, 100, 5, 1, maxDegreeOfParallelism: 1);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(-0.74765422244251278, actual.Error, 0.0001);
Assert.AreEqual(3, actual.ParameterSet.Length);
Assert.AreEqual(-5.00656832708352, actual.ParameterSet[0], Delta);
Assert.AreEqual(-9.67008227467075, actual.ParameterSet[1], Delta);
Assert.AreEqual(-0.241737044528936, actual.ParameterSet[2], Delta);
}
public void BayesianOptimizer_OptimizeBestInParallel()
{
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),
};
//high variance without fixed seed
var sut = new BayesianOptimizer(parameters, 100, 5, 2, seed: Seed);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(-0.736123479387088, actual.Error, Delta);
Assert.AreEqual(3, actual.ParameterSet.Length);
Assert.AreEqual(-4.37019084318084, actual.ParameterSet[0], Delta);
Assert.AreEqual(-3.05224638108734, actual.ParameterSet[1], Delta);
Assert.AreEqual(0.274598500819224, actual.ParameterSet[2], Delta);
}
public void BayesianOptimizer_OptimizeBest_SingleParameter(bool runParallel)
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(0.0, 100.0, Transform.Linear)
};
var sut = new BayesianOptimizer(parameters,
iterations: 80,
randomStartingPointCount: 20,
functionEvaluationsPerIterationCount: 1,
randomSearchPointCount: 1000,
seed: 42,
runParallel: runParallel);
var actual = sut.OptimizeBest(MinimizeWeightFromHeight);
Assert.AreEqual(126.50056735005998, actual.Error, Delta);
Assert.AreEqual(38.359608938153649, actual.ParameterSet.Single(), Delta);
}