public ParameterValue[] CreateIndividual(OriginData matlabOriginData, IEnumerable <MoleculeOntogeny> moleculeOntogenies)
{
var originData = originDataFrom(matlabOriginData);
var individual = _individualFactory.CreateAndOptimizeFor(originData);
var individualProperties = _individualPropertiesMapper.MapFrom(individual);
var allIndividualParameters = individualProperties.ParameterValues.ToList();
allIndividualParameters.AddRange(_ontogenyFactorsRetriever.FactorsFor(originData, moleculeOntogenies));
return(allIndividualParameters.ToArray());
}
public Task <RandomPopulation> CreateFor(RandomPopulationSettings populationSettings, CancellationToken cancellationToken)
{
Task <RandomPopulation> task = Task.Run(() =>
{
using (var progressUpdater = _progressManager.Create())
{
var randomPopulation = createPopulationFor(populationSettings);
fllUpGenderQueueBasedOn(populationSettings);
progressUpdater.Initialize(populationSettings.NumberOfIndividuals, PKSimConstants.UI.CreatingPopulation);
_randomGenerator = randomPopulation.RandomGenerator;
//the base indiviudal is used to retrieve the default values.
var baseIndividual = populationSettings.BaseIndividual;
//current individual defined as a clone of the based individual. The current individual will be the one varying
var currentIndividual = _cloner.Clone(populationSettings.BaseIndividual);
//cache containing all parameters changed by the create individual from the current individual. This will be used just as reference to the current parameters
var allChangedByCreatedIndividualParameters = getAllCreateIndividualParametersFrom(currentIndividual);
//all distributed parameters. This will be used to udpate the distribution for the current individual
var allDistributedParameters = getAllDistributedParametersFrom(currentIndividual);
var allBaseDistributedParamters = getAllDistributedParametersFrom(baseIndividual);
//all individual parameters. Just an optimiztion to avoid call GetAllChildren for each individual
var allIndividualParameters = currentIndividual.GetAllChildren <IParameter>().ToList();
int maxTotalIterations = populationSettings.NumberOfIndividuals * _maxIterations;
uint numberOfTry = 0;
var currentGender = _genderQueue.Dequeue();
do
{
cancellationToken.ThrowIfCancellationRequested();
numberOfTry++;
//could not create one item in max Iteration try=>exit
if (numberOfTry > _maxIterations && randomPopulation.NumberOfItems == 0)
{
throw new CannotCreatePopulationWithConstraintsException(_reportGenerator.StringReportFor(populationSettings));
}
//create a new individual based on population settings defined by the user
updateCurrentIndividualFromSettings(populationSettings, currentIndividual, allDistributedParameters, allBaseDistributedParamters, currentGender);
bool success = tryRandomize(currentIndividual, populationSettings, allIndividualParameters);
if (!success)
{
continue;
}
randomPopulation.AddIndividualProperties(_individualPropertiesMapper.MapFrom(currentIndividual, allChangedByCreatedIndividualParameters));
currentGender = _genderQueue.Dequeue();
progressUpdater.IncrementProgress(PKSimConstants.UI.CreatingIndividualInPopulation(randomPopulation.NumberOfItems, populationSettings.NumberOfIndividuals));
} while (randomPopulation.NumberOfItems < populationSettings.NumberOfIndividuals && numberOfTry < maxTotalIterations);
if (numberOfTry >= maxTotalIterations)
{
throw new CannotCreatePopulationWithConstraintsException(_reportGenerator.StringReportFor(populationSettings));
}
addUserDefinedVariabilityAndOntogenyForMolecules(randomPopulation);
randomPopulation.IsLoaded = true;
return(randomPopulation);
}
}, cancellationToken);
return(task);
}
