public void TestMakeBins()
{
int[] population = new int[13];
for (int i = 0; i < population.Length; i++)
{
population[i] = i + 1;
}
var result = MetaheuristicsHelper.MakeBins(population, 2);
Assert.AreEqual(2, result.Length);
Assert.AreEqual(new[] { 1, 2, 3, 4, 5, 6, 7 }, result[0]);
Assert.AreEqual(new[] { 8, 9, 10, 11, 12, 13 }, result[1]);
}
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);
}