public IOptimizationResults <T> Evolve( )
{
if (this.AlgebraProvider == null)
{
throw new ArgumentNullException("The Rosenbrock optimizer must have its AlgebraProvider property not set to 'null'");
}
currentPoint = startingPoint;
IBase b = createNewBase(currentPoint);
IVector stepsLength = b.CreateVector( );
for (int i = 0; i < stepsLength.Length; i++)
{
stepsLength[i] = initialStep;
}
List <IObjectiveScores <T> > scores = new List <IObjectiveScores <T> >();
while (!terminationCondition.IsFinished() && !isCancelled)
{
var endOfStage = performStage(b, currentPoint, stepsLength);
b = endOfStage.Item1;
currentPoint = endOfStage.Item2;
scores.AddRange(endOfStage.Item3);
}
//return new BasicOptimizationResults<T>( new IObjectiveScores<T>[] { currentPoint } );
return(new BasicOptimizationResults <T>(scores.ToArray()));
}
public BestOfSampling(ICandidateFactory <TSysConfig> innerSamplingFactory, int poolSize, int bestPoints,
IObjectiveEvaluator <TSysConfig> evaluator, IFitnessAssignment <double> fitAssignment = null)
{
if (fitAssignment == null)
{
fitAssignment = new DefaultFitnessAssignment();
}
if (poolSize < bestPoints)
{
throw new ArgumentOutOfRangeException("poolSize", poolSize, String.Format("poolSize must be >= bestPoints({0})", bestPoints));
}
this.HcFactory = (IHyperCubeOperationsFactory)innerSamplingFactory;
this.innerSamplingFactory = innerSamplingFactory;
var tmp = new IObjectiveScores[poolSize];
for (int i = 0; i < poolSize; i++)
{
tmp[i] = evaluator.EvaluateScore(innerSamplingFactory.CreateRandomCandidate());
}
var points = fitAssignment.AssignFitness(tmp);
Array.Sort(points);
candidates = new FitnessAssignedScores <double> [bestPoints];
for (int i = 0; i < bestPoints; i++)
{
candidates[i] = points[i];
}
}
public IOptimizationResults <StringConfiguration> Evolve()
{
IObjectiveEvaluator <StringSystemConfiguration> objectiveEvaluator = new StringObjEvaluator(this.target);
StringSystemConfiguration point = initializationFactory.CreateRandomCandidate();
IObjectiveScores score = objectiveEvaluator.EvaluateScore(point);
// TODO: Expand on the details of the algorithm. For now, the high-level interfaces is what we are teasing out.
return(null);
}
public IObjectiveScores <MpiSysConfig>[] EvaluateScore(MpiSysConfig systemConfiguration)
{
this.Execute(systemConfiguration);
var c = this.CatchmentScores;
var result = new IObjectiveScores <MpiSysConfig> [c.Length];
c.CopyTo(result, 0);
return(result);
}
public void CorrectObjectiveCount()
{
const int objectivesPerScore = 12;
const int numScores = 305;
var randomScores = CreateRandomScores(numScores, objectivesPerScore);
IObjectiveScores result = ObjectiveScoresHelper.Mean(randomScores, config);
Assert.That(result.ObjectiveCount, Is.EqualTo(objectivesPerScore));
}
private void assertPoint(IObjectiveScores iObjectiveScores, double x, double y, double z)
{
var s = (IObjectiveScores<TestHyperCube>)iObjectiveScores;
Assert.AreEqual(x, (double)s.GetObjective(0).ValueComparable, 1e-9);
Assert.AreEqual(y, (double)s.GetObjective(1).ValueComparable, 1e-9);
Assert.AreEqual(z, (double)s.GetObjective(2).ValueComparable, 1e-9);
Assert.AreEqual(x, s.SystemConfiguration.GetValue("0"), 1e-9);
Assert.AreEqual(y, s.SystemConfiguration.GetValue("1"), 1e-9);
Assert.AreEqual(z, s.SystemConfiguration.GetValue("2"), 1e-9);
}
public static string GetHumanReadable(IObjectiveScores scores)
{
var sb = new StringBuilder( );
for (int i = 0; i < scores.ObjectiveCount; i++)
{
sb.AppendLine(scores.GetObjective(i).GetText( ));
}
sb.AppendLine(scores.GetSystemConfiguration( ).GetConfigurationDescription( ));
return(sb.ToString( ));
}
private void assertPoint(IObjectiveScores iObjectiveScores, double x, double y, double z)
{
var s = (IObjectiveScores <TestHyperCube>)iObjectiveScores;
Assert.AreEqual(x, (double)s.GetObjective(0).ValueComparable, 1e-9);
Assert.AreEqual(y, (double)s.GetObjective(1).ValueComparable, 1e-9);
Assert.AreEqual(z, (double)s.GetObjective(2).ValueComparable, 1e-9);
Assert.AreEqual(x, s.SystemConfiguration.GetValue("0"), 1e-9);
Assert.AreEqual(y, s.SystemConfiguration.GetValue("1"), 1e-9);
Assert.AreEqual(z, s.SystemConfiguration.GetValue("2"), 1e-9);
}
public void CorrectObjectiveNames()
{
const int objectivesPerScore = 3;
const int numScores = 3;
var randomScores = CreateRandomScores(numScores, objectivesPerScore);
IObjectiveScores result = ObjectiveScoresHelper.Mean(randomScores, config);
for (int i = 0; i < objectivesPerScore; i++)
{
Assert.That(result.GetObjective(i).Name, Is.EqualTo(randomScores[0].GetObjective(i).Name));
}
}
public Stage(IBase b, IObjectiveScores <T> startingPoint, IVector stepsLength, double alpha, double beta, CountingEvaluator countingEvaluator, IAlgebraProvider algebraProvider, ITerminationCondition <T> terminationCondition, IDictionary <string, string> logTags = null)
{
this.b = b;
this.startingPoint = startingPoint;
this.currentPoint = startingPoint;
this.stepsLength = stepsLength;
this.alpha = alpha;
this.beta = beta;
this.countingEvaluator = countingEvaluator;
this.terminationCondition = terminationCondition;
this.algebraProvider = algebraProvider;
this.logTags = logTags;
}
private IObjectiveScores <T>[] makeAMove(IBase b, IObjectiveScores <T> startPoint)
{
IObjectiveScores <T> newPoint = startPoint;
int d = b.NumDimensions;
IObjectiveScores <T>[] points = new IObjectiveScores <T> [d];
for (int i = 0; i < d; i++)
{
newPoint = makeAMove(i, b, newPoint);
points[i] = newPoint;
}
return(points);
}
private IVector createVector(IObjectiveScores <T> startingPoint, IObjectiveScores <T> endPoint)
{
IVector result = algebraProvider.CreateVector(startingPoint.SystemConfiguration.Dimensions);
int count = result.Length;
var varNames = startingPoint.SystemConfiguration.GetVariableNames( );
for (int i = 0; i < count; i++)
{
result[i] =
endPoint.SystemConfiguration.GetValue(varNames[i]).ToDouble(null) -
startingPoint.SystemConfiguration.GetValue(varNames[i]).ToDouble(null);
}
return(result);
}
public void Execute( )
{
RiverSystemScenario testScenario;
RiverSystemProject testProject;
TestHelperRiverSystem.getAPreconfiguredTestScenarioWithAWBM(3, out testProject,
out testScenario);
IObjectiveEvaluator <MetaParameterSet> evaluator = buildNewEvaluator(testScenario);
ICandidateFactory <MetaParameterSet> candidateFactory = new LumpedAWBMFactory(testScenario, new BasicRngFactory(123));
int pSetNumber = 5;
IObjectiveScores[] scores = new IObjectiveScores[pSetNumber];
ISystemConfiguration[] pSets = new ISystemConfiguration[pSetNumber];
int k = 0;
foreach (Catchment c in testScenario.Network.catchmentList)
{
foreach (StandardFunctionalUnit fu in c.FunctionalUnits)
{
var list = AccessorMemberInfo.GetFieldsAndPropertiesList(fu.rainfallRunoffModel.GetType(), typeof(double), typeof(ParameterAttribute));
foreach (MemberInfo m in list)
{
string metaParameterName = "$tag" + k++;
UpdateFunctionBuilder.CreateMetaParameter(testScenario, metaParameterName);
UpdateFunctionBuilder.CreateUpdateFunctionWithLinkToMetaParameter(testScenario, metaParameterName, m, fu.rainfallRunoffModel);
}
}
}
for (int i = 0; i < scores.Length; i++)
{
pSets[i] = candidateFactory.CreateRandomCandidate();
//Do we need to reset the model states??
scores[i] = evaluator.EvaluateScore((MetaParameterSet)pSets[i]);
//print Params after each iteration
LumpedAWBMFactory.printPrams(testScenario, pSets[i]);
}
IObjectiveScores[] paretoScores = ParetoRanking <IObjectiveScores> .GetParetoFront(scores);
SourceUtility.PrintObjScores(scores);
Console.WriteLine("----------------------------------------");
SourceUtility.PrintObjScores(paretoScores);
}
private static List<ObjectiveScore> Convert(IObjectiveScores scores)
{
var scoreList = new List<ObjectiveScore>();
for (int i = 0; i < scores.ObjectiveCount; i++)
{
var singleObjective = scores.GetObjective(i);
scoreList.Add(
new ObjectiveScore
{
Name = singleObjective.Name,
Maximize = singleObjective.Maximise,
Value = singleObjective.ValueComparable.ToString()
});
}
return scoreList;
}
static IObjectiveScores[] EvaluateScoresSerial <T>(T[] population, Func <bool> isCancelled,
IClonableObjectiveEvaluator <T> evaluator) where T : ISystemConfiguration
{
IObjectiveScores[] result = new IObjectiveScores[population.Length];
for (int j = 0; j < population.Length; j++)
{
if (!isCancelled())
{
result [j] = evaluator.EvaluateScore(population [j]);
}
else
{
result [j] = null;
}
}
return(result);
}
public MpiObjectiveScores(IObjectiveScores <MpiSysConfig> scores, string catchmentId = "")
{
this.config = scores.SystemConfiguration;
this.CatchmentId = catchmentId;
this.scores = new MpiObjectiveScore[scores.ObjectiveCount];
for (int i = 0; i < this.scores.Length; i++)
{
var score = scores.GetObjective(i);
this.scores[i] = new MpiObjectiveScore
{
name = score.Name,
maximise = score.Maximise,
text = score.GetText(),
value = (double)score.ValueComparable
};
}
}
private static List <ObjectiveScore> Convert(IObjectiveScores scores)
{
var scoreList = new List <ObjectiveScore>();
for (int i = 0; i < scores.ObjectiveCount; i++)
{
var singleObjective = scores.GetObjective(i);
scoreList.Add(
new ObjectiveScore
{
Name = singleObjective.Name,
Maximize = singleObjective.Maximise,
Value = singleObjective.ValueComparable.ToString()
});
}
return(scoreList);
}
public static IObjectiveScores[] CreateTestScores(int objectivesPerScore, MpiSysConfig[] configs)
{
int numScores = configs.Length;
IObjectiveScores[] scores = new IObjectiveScores[numScores];
for (int i = 0; i < numScores; i++)
{
IObjectiveScore[] objectives = new IObjectiveScore[objectivesPerScore];
for (int j = 0; j < objectivesPerScore; j++)
{
objectives[j] = new DoubleObjectiveScore(String.Format("Score:{0}-{1}", i, j), FakeScore(i + j), true);
}
scores[i] = new MpiObjectiveScores(objectives, configs[i]);
}
return(scores);
}
public void Execute(string modelRunDefnFile, string modelOutputName, string runoffToMatchFile)
{
setRandSeed( );
var evaluator = ProblemDefinitionHelper.BuildEvaluator(modelRunDefnFile, modelOutputName, runoffToMatchFile);
var candidateFactory = buildCandidateFactory(ProblemDefinitionHelper.BuildSystem(modelRunDefnFile, "runoff").Model);
var scores = new IObjectiveScores[1000];
var pSets = new ICloneableSystemConfiguration[1000];
for (int i = 0; i < scores.Length; i++)
{
pSets[i] = candidateFactory.CreateRandomCandidate( );
scores[i] = evaluator.EvaluateScore(pSets[i]);
}
IObjectiveScores[] paretoScores = ParetoRanking <IObjectiveScores> .GetParetoFront(scores);
printSummary(scores);
Console.ReadLine( );
}
public void Execute()
{
SCH1ObjectiveEvaluator evaluator = new SCH1ObjectiveEvaluator();
UnivariateRealUniformRandomSampler rand = new UnivariateRealUniformRandomSampler(-5, 5, 0);
IObjectiveScores[] scores = new IObjectiveScores[1000];
for (int i = 0; i < scores.Length; i++)
{
scores[i] = evaluator.EvaluateScore(new UnivariateReal(rand.GetNext()));
}
IObjectiveScores[] paretoScores = getParetoFront(scores);
foreach (var score in paretoScores)
{
Console.Write(score.GetSystemConfiguration().GetConfigurationDescription());
Console.Write(", ");
}
Console.WriteLine();
Console.WriteLine("Press enter to quit");
Console.ReadLine();
}
public IOptimizationResults <T> Evolve()
{
Process p = new Process();
p.StartInfo.UseShellExecute = false;
p.StartInfo.CreateNoWindow = true;
p.StartInfo.FileName = "pest";
p.StartInfo.Arguments = this.pestFileName;
p.StartInfo.RedirectStandardOutput = true;
p.Start();
StreamReader outputReader = p.StandardOutput;
MessageQueueModelRunner mqmr = new MessageQueueModelRunner(this.resultFileName, this.mr, this.modelOutputName);
ParameterSet pSet;
var scores = new List <IObjectiveScores>();
while (mqmr.RunIteration(out pSet))
{
currentGeneration++;
}
p.WaitForExit();
if (0 == currentGeneration)
{
throw new PestExecutionFailedException("Pest executed 0 times.", outputReader.ReadToEnd());
//throw new Exception(String.Format("PEST ran 0 times. Pest Output: \n {0}",outputReader.ReadToEnd()));
}
T sysConfig = (T) new TIMEModelParameterSet(pSet);
IObjectiveScores <T> score = this.evaluator.EvaluateScore(sysConfig);
IOptimizationResults <T> result = new CSIRO.Metaheuristics.Optimization.BasicOptimizationResults <T>(new IObjectiveScores[] { score });
this.pestProcessOutput = outputReader.ReadToEnd();
return(result);
}
private IObjectiveScores <T> makeAMove(int baseVectorIndex, IBase b, IObjectiveScores <T> startingPoint)
{
double stepSize = stepsLength[baseVectorIndex];
bool success = false;
bool moveMetConstraint = false;
bool bumped = false;
var sysConfig = (T)startingPoint.SystemConfiguration.Clone( );
var varnames = sysConfig.GetVariableNames( );
int count = sysConfig.Dimensions;
for (int variableIndex = 0; variableIndex < count; variableIndex++)
{
var minMax = Tuple.Create(sysConfig.GetMinValue(varnames[variableIndex]), sysConfig.GetMaxValue(varnames[variableIndex]));
bumped = !(tryAdd(varnames[baseVectorIndex], sysConfig, b[baseVectorIndex][variableIndex] * step(stepSize, minMax.Item1.ToDouble(null), minMax.Item2.ToDouble(null))));
if (bumped)
{
moveMetConstraint = true;
}
}
IObjectiveScores <T> candidatePoint = evaluate(sysConfig);
var compareResult = compareObjScores(candidatePoint, startingPoint);
success = compareResult <= 0;
var result = success ? candidatePoint : startingPoint;
if (success && !moveMetConstraint)
{
stepsLength[baseVectorIndex] *= (alpha);
moveSuccededOnce[baseVectorIndex] = true;
}
else
{
stepsLength[baseVectorIndex] *= (-beta);
moveFailedOnce[baseVectorIndex] = true;
}
return(result);
}
private IBase createNewBase(IBase b, IObjectiveScores <T> startingPoint, IObjectiveScores <T> endPoint)
{
IVector pathOfStage = createVector(startingPoint, endPoint);
if (pathOfStage.IsNullVector)
{
pathOfStage.SetAllComponents(1.0);
}
IBase result = b.CreateNew(pathOfStage.Length);
int colinearTo = isColinearToBase(pathOfStage);
if (colinearTo >= 0)
{
return(result);
}
if (result.GetBaseVector(0).IsOrthogonal(pathOfStage))
{
if (result.NumDimensions > 1)
{
if (!result.GetBaseVector(1).IsOrthogonal(pathOfStage))
{
result.SetBaseVector(1, pathOfStage);
result.Orthonormalize(1);
}
}
else
{
return(result);
}
}
else
{
result.SetBaseVector(0, pathOfStage);
result.Orthonormalize(0);
}
return(result);
}
/// <summary>
/// Calculates the per-objective means of the specified scores.
/// </summary>
/// <param name="scoresArray">The scores.</param>
/// <param name="sysConfig">The sys config.</param>
/// <returns>A single <see cref="IObjectiveScores"/> instance where the i'th score is the
/// mean of the i'th score in each element of the scores array.
/// The result score names are taken from the first input element.</returns>
/// <remarks>
/// This method makes a few assumptions which are not checked for performance reasons:
/// - each element in the scores array has the same objective count. More specifically,
/// if an element has more scores than element 0, the additional scores will be ignored.
/// If an element has less scores, then an out of bounds array access error will occur.
/// - score names are ignored. It is assumed that every element in scores has named scores
/// occuring in the same order.
/// </remarks>
public static MpiObjectiveScores Mean(MpiObjectiveScores[] scoresArray, MpiSysConfig sysConfig)
{
if (scoresArray == null)
{
throw new ArgumentNullException("scoresArray");
}
if (sysConfig == null)
{
throw new ArgumentNullException("sysConfig");
}
if (scoresArray.Length < 1)
{
throw new ArgumentException("Scores array is empty", "scoresArray");
}
IObjectiveScores referenceScore = scoresArray[0];
int objectiveCount = referenceScore.ObjectiveCount;
double[] means = new double[objectiveCount];
for (int objectiveIdx = 0; objectiveIdx < objectiveCount; objectiveIdx++)
{
foreach (MpiObjectiveScores objectiveScores in scoresArray)
{
means[objectiveIdx] += (double)objectiveScores.GetObjective(objectiveIdx).ValueComparable;
}
means[objectiveIdx] /= scoresArray.Length;
}
IObjectiveScore[] meanScores = new IObjectiveScore[objectiveCount];
for (int i = 0; i < means.Length; i++)
{
meanScores[i] = new DoubleObjectiveScore(referenceScore.GetObjective(i).Name, means[i], referenceScore.GetObjective(i).Maximise);
}
return(new MpiObjectiveScores(meanScores, sysConfig));
}
public IObjectiveScores <MpiSysConfig>[] EvaluateScore(MpiSysConfig systemConfiguration)
{
int rank = comm.Rank;
if (rank == 0)
{
for (int i = 1; i < comm.Size; i++)
{
comm.Send(systemConfiguration, i, Convert.ToInt32(MpiMessageTags.SystemConfigurationMsgTag));
}
if (log.IsDebugEnabled)
{
log.Info("Process " + comm.Rank + " has sent the sys configs to evaluate");
}
IObjectiveScores <MpiSysConfig>[] allscores = new IObjectiveScores <MpiSysConfig> [comm.Size - 1];
IObjectiveScores <MpiSysConfig> objscore = null;
if (log.IsDebugEnabled)
{
log.Info("Process " + comm.Rank + " waiting to receive results from all slave processes");
}
for (int i = 1; i < comm.Size; i++)
{
comm.Receive(i, Convert.ToInt32(MpiMessageTags.EvalSlaveResultMsgTag), out objscore);
allscores[i - 1] = objscore;
}
if (log.IsDebugEnabled)
{
log.Info("Process " + comm.Rank + " has received all the scores evaluated ");
}
return(allscores);
}
else
{
throw new NotSupportedException("MpiObjectiveEvaluator is designed to work with MPI process rank 0 only");
}
}
public Tuple <IBase, IObjectiveScores <T>, IObjectiveScores <T>[]> Evolve()
{
moveFailedOnce = createNegBoolArray(b.NumDimensions);
moveSuccededOnce = createNegBoolArray(b.NumDimensions);
List <IObjectiveScores <T> > scores = new List <IObjectiveScores <T> >();
while (stageIsComplete( ) == false)
{
var triedPoints = makeAMove(b, currentPoint);
currentPoint = triedPoints.Last();
scores.AddRange(triedPoints);
LoggerMhHelper.Write(new IObjectiveScores[] { currentPoint },
createTag(),
Logger);
}
endPoint = currentPoint;
b = createNewBase(b, startingPoint, endPoint);
var paretoRanking = new ParetoRanking <IObjectiveScores <T> >(scores, new ParetoComparer <IObjectiveScores <T> >());
IObjectiveScores <T>[] paretoScores = paretoRanking.GetDominatedByParetoRank(1);
return(Tuple.Create(b, currentPoint, paretoScores));
}
public void Write(IObjectiveScores[] scores, IDictionary<string, string> tags)
{
if (log.IsInfoEnabled)
log.Info(new SysConfigLogInfo(scores, tags));
}
private IBase createNewBase(IObjectiveScores <T> currentPoint)
{
return(AlgebraProvider.CreateBase(currentPoint.SystemConfiguration.Dimensions));
}
private Tuple <IBase, IObjectiveScores <T>, IObjectiveScores <T>[]> performStage(IBase b, IObjectiveScores <T> currentPoint, IVector stepsLength)
{
stage = new Stage(b, currentPoint, stepsLength, alpha, beta, this.countingEvaluator, this.AlgebraProvider, this.terminationCondition, this.logTags);
stage.Logger = Logger;
return(stage.Evolve());
}
private int compareObjScores(IObjectiveScores <T> candidatePoint, IObjectiveScores <T> startingPoint)
{
return(this.comparer.Compare(candidatePoint, startingPoint));
}
public StringFinderResults(IObjectiveScores <StringSpecification> res)
{
this.Results = new List <IObjectiveScores <StringSpecification> >();
Results.Add(res);
}
public static IObjectiveScores[] EvaluateScores <T>(IClonableObjectiveEvaluator <T> evaluator, T[] population, Func <bool> isCancelled, ParallelOptions parallelOptions = null) where T : ISystemConfiguration
{
if (population.Length == 0)
{
return(new IObjectiveScores[0]);
}
if (parallelOptions == null)
{
parallelOptions = new ParallelOptions()
{
MaxDegreeOfParallelism = -1
}
}
;
var procCount = System.Environment.ProcessorCount;
IObjectiveScores[] result;
if (evaluator.SupportsThreadSafeCloning)
{
// There is presumably no point cloning
// the system more times than the max level of parallelism
int nParallel = procCount;
if (parallelOptions.MaxDegreeOfParallelism > 0)
{
nParallel = Math.Min(nParallel, parallelOptions.MaxDegreeOfParallelism);
}
T[][] subPop = MetaheuristicsHelper.MakeBins(population, nParallel);
var taskPkgs = new List <Tuple <T[], IClonableObjectiveEvaluator <T> > >();
taskPkgs.Add(Tuple.Create(subPop[0], evaluator));
for (int i = 1; i < subPop.Length; i++)
{
taskPkgs.Add(Tuple.Create(subPop[i], evaluator.Clone()));
}
// Need to use Parallel.ForEach rather than Parallel.For to work around a Parallel.For
// oddity in Mono 3.12.1. Need an identity to iterate over...
var ramp = new int[subPop.Length];
// Map of index of subpopulations to indices in the variable result:
//var offsets = new int[subPop.Length];
var resultBins = new IObjectiveScores[ramp.Length][];
for (int i = 1; i < ramp.Length; i++)
{
ramp[i] = i;
}
Parallel.ForEach(ramp, parallelOptions,
(i => {
resultBins[i] = EvaluateScoresSerial(taskPkgs[i], isCancelled);
})
);
result = Gather(resultBins);
}
else
{
result = new IObjectiveScores[population.Length];
for (int i = 0; i < population.Length; i++)
{
if (!isCancelled())
{
result [i] = evaluator.EvaluateScore(population [i]);
}
else
{
result [i] = null;
}
}
}
return(result);
}
/// <summary>
/// Creates a FitnessAssignedScores, a union of a candidate system configuration and its objective scores, and an overall fitness score.
/// </summary>
/// <param name="scores">Objective scores</param>
/// <param name="fitnessValue">Fitness value, derived from the scores and context information such as a candidate population.</param>
public FitnessAssignedScores(IObjectiveScores scores, T fitnessValue)
{
this.Scores = scores;
this.FitnessValue = fitnessValue;
}