/// <summary>
/// Executes the treatment
/// </summary>
/// <param name="component"></param>
/// <returns></returns>
public override bool ApplyOn(ExecutableComponent component)
{
Component = Component;
return(ApplyOn());
}
public void Propagate(List <IFASTDistribution> inputDistributions, List <ProbabilityDistributionFromSamples> outputDistributions, WorkflowComponent innerWorkflow)
{
filer = (createFile) ? new CSVFiler(path) : null;
try
{
int Ninputs = inputDistributions.Count;
int Noutputs = outputDistributions.Count;
sampler = new FASTSampler(Ninputs);
int NSamples = sampler.Ns;
Matrix <double> samples = Matrix <double> .Build.Dense(NSamples, Ninputs + Noutputs);
for (int i = 0; i < inputDistributions.Count; i++)
{
samples.SetColumn(i, inputDistributions[i].GetSamples(NSamples, sampler.GetSamplesForVariable(i)));
}
for (int s = 0; s < NSamples; s++)
{
int v = 0;
foreach (IProbabilityDistribution input in inputDistributions)
{
input.Data.Value = samples[s, v];
v++;
}
// Execute workflow
bool statusToCheck = innerWorkflow.Execute();
foreach (IProbabilityDistribution output in outputDistributions)
{
samples[s, v] = Convert.ToDouble(output.Data.Value);
v++;
}
if (createFile && statusToCheck)
{
// Execute database insert command
filer.NewRow();
//filer.AddToRow(i);
foreach (Data input in innerWorkflow.ModelDataInputs)
{
filer.AddToRow(input);
}
foreach (Data output in innerWorkflow.ModelDataInputs)
{
filer.AddToRow(output);
}
filer.WriteRow();
}
}
int o = Ninputs;
foreach (IProbabilityDistribution output in outputDistributions)
{
output.Update(samples.Column(o).AsArray());
o++;
}
Samples = samples;
}
finally
{
filer?.Dispose();
}
}
public (int[] variableIn, int[] constantIn, int[] variableOut, int[] constantOut, List <MissionParameter> missionParameters) GetComponentCalssificationAndIndices(WorkflowComponent component)
{
var variableInputs = new List <string>();
var constantInputs = new List <string>();
var variableOutputs = new List <string>();
var constantOutputs = new List <string>();
var modelDataDict = new Dictionary <string, int>();
var missionParameters = new List <MissionParameter>(component.ModelDataInputs.Count + component.ModelDataOutputs.Count);
for (int i = 0; i < component.ModelDataInputs.Count; i++)
{
Data data = component.ModelDataInputs[i];
string name = data.Name;
try
{
MissionParameter parameter = parameters[name];
missionParameters.Add(parameter);
if (parameter is VariableMissionParameter vmp)
{
variableInputs.Add(name);
}
else if (parameter is ConstantMissionParameter cmp)
{
constantInputs.Add(name);
}
}
catch (KeyNotFoundException knfe)
{
throw new KeyNotFoundException($"No parameter for variable '{data}' was found in segment {Name}", knfe);
}
modelDataDict[name] = i;
}
for (int i = 0; i < component.ModelDataOutputs.Count; i++)
{
Data data = component.ModelDataInputs[i];
string name = data.Name;
try
{
MissionParameter parameter = parameters[name];
missionParameters.Add(parameter);
if (parameter is VariableMissionParameter vmp)
{
variableOutputs.Add(name);
}
else if (parameter is ConstantMissionParameter cmp)
{
constantOutputs.Add(name);
}
}
catch (KeyNotFoundException knfe)
{
throw new KeyNotFoundException($"No parameter for variable '{data}' was found in segment {Name}", knfe);
}
modelDataDict[name] = i;
}
int[] variableIn = variableInputs.Select(v => modelDataDict[v]).ToArray();
int[] constantIn = constantInputs.Select(v => modelDataDict[v]).ToArray();
int[] variableOut = variableOutputs.Select(v => modelDataDict[v]).ToArray();
int[] constantOut = constantOutputs.Select(v => modelDataDict[v]).ToArray();
return(variableIn, constantIn, variableOut, constantOut, missionParameters);
}
public Data Child(WorkflowComponent component) => component.ModelDataOutputs.First();
public void Propagate(List <IProbabilityDistribution> inputDistributions, List <IProbabilityDistribution> outputDistributions, WorkflowComponent innerWorkflow)
{
List <IFASTDistribution> inputs = null;
List <ProbabilityDistributionFromSamples> outputs = null;
try
{
inputs = inputDistributions.Cast <IFASTDistribution>().ToList();
}
catch (InvalidCastException)
{
Console.WriteLine("At least one of the input distributions used for this Monte Carlo Simulation are not of the right kind. Samples cannot be generated");
throw;
}
try
{
outputs = inputDistributions.Cast <ProbabilityDistributionFromSamples>().ToList();
}
catch (InvalidCastException)
{
Console.WriteLine("At least one of the output distributions used for this Monte Carlo Simulation are not of the right kind. Distribution annot be infered from samples");
throw;
}
Propagate(inputs, outputs, innerWorkflow);
}
public List <Data> DependencyData(WorkflowComponent component, bool onlyIndependent = true) => DependencyData(Child(component), onlyIndependent);
public List <WorkflowComponent> DependencyComponents(WorkflowComponent component) => DependencyComponentsHash(component, true);
public List <Data> UnnasignedData(string segmentName, WorkflowComponent component, bool onlyIndependent = true) => UnnasignedData(segmentName, Child(component), onlyIndependent);
//public DOEplusURQStudy(string name, string description, Model worflow, List<Data> factors, List<Data> responses)
public DOEplusURQStudy(string name, string description, List <string> names, Treatment treatment, FullFactorial fullFactorialDOE, WorkflowComponent worflow)
: base(name, description)
{
TreatmentURQ = treatment;
this.names = names; //DOE factors
Treatment = treatment;
StudiedComponent = worflow;
FullFactorialDOE = fullFactorialDOE;
var DesignVariables = new List <RobOptDesignVariable>();
for (int i = 0; i < ((string[, ])(Treatment.input_options.setuplist[0])).GetLength(0); i++)
{
var designVariable = new RobOptDesignVariable
{
Name = ((string[, ])(Treatment.input_options.setuplist[0]))[i, 0]
};
//designVariable.LowerBound = ;
//designVariable.UpperBound = ;
DesignVariables.Add(designVariable);
}
var Objectives = new List <RobOptObjective>();
for (int i = 0; i < ((string[, ])(Treatment.input_options.setuplist[1])).GetLength(0); i++)
{
var objective = new RobOptObjective
{
Name = ((string[, ])(Treatment.input_options.setuplist[1]))[i, 0]
};
if (((string[, ])(Treatment.input_options.setuplist[1]))[i, 2] == "minimise")
{
objective.Type = ObjectiveType.Minimise;
}
else if (((string[, ])(Treatment.input_options.setuplist[1]))[i, 2] == "maximise")
{
objective.Type = ObjectiveType.Maximise;
}
Objectives.Add(objective);
}
var Constraints = new List <RobOptConstraint>();
for (int i = 0; i < ((string[, ])(Treatment.input_options.setuplist[2])).GetLength(0); i++)
{
var constraint = new RobOptConstraint
{
Name = ((string[, ])(Treatment.input_options.setuplist[2]))[i, 0]
};
if (((string[, ])(Treatment.input_options.setuplist[2]))[i, 2] == "<=")
{
constraint.Type = ConstraintType.LessThanOrEqual;
}
else if (((string[, ])(Treatment.input_options.setuplist[2]))[i, 2] == ">=")
{
constraint.Type = ConstraintType.GreatorThanOrEqual;
}
constraint.Value = Convert.ToDouble(((string[, ])(Treatment.input_options.setuplist[2]))[i, 3]);
Constraints.Add(constraint);
}
robOptTemplate = new RobOptTemplate()
{
DesignVariables = DesignVariables, Objectives = Objectives, Constraints = Constraints
};
ColumnNames.Add("ID");
ColumnTypes.Add(DataTypes.INTEGER);
ColumnFormats.Add(0);
ColumnUnits.Add("");
for (int i = 0; i < this.names.Count; i++)
{
string columnHeader = this.names[i];
ColumnNames.Add(columnHeader);
ColumnTypes.Add(DataTypes.DOUBLE);
ColumnFormats.Add(4);
ColumnUnits.Add("");
}
for (int i = 0; i < robOptTemplate.DesignVariables.Count(); i++)
{
if (!this.names.Contains(robOptTemplate.DesignVariables[i].Name))
{
string columnHeader = robOptTemplate.DesignVariables[i].Name;
ColumnNames.Add(columnHeader);
ColumnTypes.Add(DataTypes.DOUBLE);
ColumnFormats.Add(4);
ColumnUnits.Add("");
}
}
for (int i = 0; i < robOptTemplate.Objectives.Count(); i++)
{
string columnHeader = robOptTemplate.Objectives[i].Name;
// Loss Function
ColumnNames.Add(columnHeader + "LF");
ColumnTypes.Add(DataTypes.DOUBLE);
ColumnFormats.Add(4);
ColumnUnits.Add("");
// Mean
ColumnNames.Add(columnHeader + "mean");
ColumnTypes.Add(DataTypes.DOUBLE);
ColumnFormats.Add(4);
ColumnUnits.Add("");
// Variance
ColumnNames.Add(columnHeader + "var");
ColumnTypes.Add(DataTypes.DOUBLE);
ColumnFormats.Add(4);
ColumnUnits.Add("");
}
for (int i = 0; i < robOptTemplate.Constraints.Count(); i++)
{
string columnHeader = robOptTemplate.Constraints[i].Name;
// Loss Function
ColumnNames.Add(columnHeader + "LF");
ColumnTypes.Add(DataTypes.DOUBLE);
ColumnFormats.Add(4);
ColumnUnits.Add("");
// Mean
ColumnNames.Add(columnHeader + "mean");
ColumnTypes.Add(DataTypes.DOUBLE);
ColumnFormats.Add(4);
ColumnUnits.Add("");
// Variance
ColumnNames.Add(columnHeader + "var");
ColumnTypes.Add(DataTypes.DOUBLE);
ColumnFormats.Add(4);
ColumnUnits.Add("");
}
TableNames.Add("DOE");
}
public override bool IsAllowed(WorkflowComponent source)
{
return((source is BasicTrigger || source is BasicAction || source is MagicOutput) && source != this);
}
