public void BayesianOptimizer_Optimize()
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(0.0, 100.0, Transform.Linear)
};
var sut = new BayesianOptimizer(parameters,
iterations: 120,
randomStartingPointCount: 5,
functionEvaluationsPerIterationCount: 1,
randomSearchPointCount: 1000,
seed: 42,
runParallel: false); // Note, since the returned results are not ordered on error,
// running with parallel computations will not return reproducible order of results,
// so runParallel must be false for this test.
var results = sut.Optimize(MinimizeWeightFromHeight);
var actual = new OptimizerResult[] { results.First(), results.Last() };
var expected = new OptimizerResult[]
{
new OptimizerResult(new double[] { 43.216748276360683 }, 1352.8306605984087),
new OptimizerResult(new double[] { 38.201425707992833 }, 119.1316225267316)
};
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 BayesianOptimizer_OptimizeNonDeterministicInParallel()
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(0, 1, Transform.Linear, ParameterType.Discrete)
};
var sut = new BayesianOptimizer(parameters, iterations: 240, randomStartingPointCount: 5, functionEvaluationsPerIteration: 5,
seed: Seed, maxDegreeOfParallelism: -1, allowMultipleEvaluations: true);
var results = sut.Optimize(p => MinimizeNonDeterministic(p, Random));
var actual = new OptimizerResult[] { results.First(), results.Last() }.OrderByDescending(o => o.Error);
Assert.AreEqual(1, actual.First().Error);
Assert.AreEqual(1, (int)actual.First().ParameterSet[0]);
}
public void BayesianOptimizer_Optimize()
{
var parameters = new double[][] { new double[] { 0.0, 100.0 } };
var sut = new BayesianOptimizer(parameters, 120, 5, 1);
var results = sut.Optimize(Minimize2);
var actual = new OptimizerResult[] { results.First(), results.Last() };
var expected = new OptimizerResult[]
{
new OptimizerResult(new double[] { 37.710969353891429 }, 109.34400835405613),
new OptimizerResult(new double[] { 99.646240426062718 }, 157577.44222424511)
};
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 BayesianOptimizer_Optimize()
{
var parameters = new ParameterBounds[]
{
new ParameterBounds(0.0, 100.0, Transform.Linear)
};
var sut = new BayesianOptimizer(parameters, 120, 5, 1);
var results = sut.Optimize(Minimize2);
var actual = new OptimizerResult[] { results.First(), results.Last() };
var expected = new OptimizerResult[]
{
new OptimizerResult(new double[] { 42.323589763754789 }, 981.97873691815118),
new OptimizerResult(new double[] { 99.110398813667885 }, 154864.41962974239)
};
Assert.AreEqual(expected.First().Error, actual.First().Error, m_delta);
Assert.AreEqual(expected.First().ParameterSet.First(), actual.First().ParameterSet.First(), m_delta);
Assert.AreEqual(expected.Last().Error, actual.Last().Error, m_delta);
Assert.AreEqual(expected.Last().ParameterSet.First(), actual.Last().ParameterSet.First(), m_delta);
}
public void BayesianOptimizer_Optimize()
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(0.0, 100.0, Transform.Linear)
};
var sut = new BayesianOptimizer(parameters, 120, 5, 1);
var results = sut.Optimize(Minimize2);
var actual = new OptimizerResult[] { results.First(), results.Last() };
var expected = new OptimizerResult[]
{
new OptimizerResult(new double[] { 90.513222660177 }, 114559.431919558),
new OptimizerResult(new double[] { 24.204380402436 }, 7601.008090362)
};
Assert.AreEqual(expected.First().Error, actual.First().Error, m_delta);
Assert.AreEqual(expected.First().ParameterSet.First(), actual.First().ParameterSet.First(), m_delta);
Assert.AreEqual(expected.Last().Error, actual.Last().Error, m_delta);
Assert.AreEqual(expected.Last().ParameterSet.First(), actual.Last().ParameterSet.First(), m_delta);
}
public void BayesianOptimizer_Optimize()
{
var parameters = new MinMaxParameterSpec[]
{
new MinMaxParameterSpec(0.0, 100.0, Transform.Linear)
};
var sut = new BayesianOptimizer(parameters, 120, 5, 1, maxDegreeOfParallelism: 1);
var results = sut.Optimize(MinimizeWeightFromHeight);
var actual = new OptimizerResult[] { results.First(), results.Last() }.OrderByDescending(o => o.Error);
var expected = new OptimizerResult[]
{
new OptimizerResult(new double[] { 90.513222660177 }, 114559.431919558),
new OptimizerResult(new double[] { 24.2043804024367 }, 7601.00809036235)
};
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);
}