/// <summary>
/// Computes the influence of all configuration options based on the measurements of the given result db. It uses linear programming (simplex) and is an exact algorithm.
/// </summary>
/// <param name="nfp">The non-funcitonal property for which the influences of configuration options are to be computed. If null, we use the property of the global model.</param>
/// <param name="infModel">The influence model containing options and interactions. The state of the model will be changed by the result of the process</param>
/// <param name="db">The result database containing the measurements.</param>
/// <returns>A map of binary options to their computed influences.</returns>
public Dictionary<BinaryOption, double> computeOptionInfluences(NFProperty nfp, InfluenceModel infModel, ResultDB db)
{
List<BinaryOption> variables = infModel.Vm.BinaryOptions;
List<double> results = new List<double>();
List<List<BinaryOption>> configurations = new List<List<BinaryOption>>();
foreach (Configuration c in db.Configurations)
{
configurations.Add(c.getBinaryOptions(BinaryOption.BinaryValue.Selected));
if (nfp != null)
results.Add(c.GetNFPValue(nfp));
else
results.Add(c.GetNFPValue());
}
List<String> errorEqs = new List<string>();
Dictionary<String, double> faultRates = new Dictionary<string, double>();
List<int> indexOfErrorMeasurements = new List<int>();
Dictionary<String, double> featureValuedAsStrings = solve(variables, results, configurations, infModel.InteractionInfluence.Keys.ToList());
foreach (String current in featureValuedAsStrings.Keys)
{
BinaryOption temp = infModel.Vm.getBinaryOption(current);
this.featureValues[temp] = featureValuedAsStrings[current];
InfluenceFunction influence = new InfluenceFunction(temp.Name + " + " + featureValuedAsStrings[current].ToString(),infModel.Vm);
if (infModel.BinaryOptionsInfluence.Keys.Contains(temp))
infModel.BinaryOptionsInfluence[temp] = influence;
else
infModel.BinaryOptionsInfluence.Add(temp, influence);
}
return this.featureValues;
}
/// <summary>
/// Clears the global state. This mehtod should be used after performing all experiments of one case study.
/// </summary>
public static void clear()
{
varModel = null;
currentNFP = null;
allMeasurements = new ResultDB();
evalutionSet = new ResultDB();
infModel = null;
nfProperties = new Dictionary <string, NFProperty>();
}
/// <summary>
/// Clears the global state. This mehtod should be used after performing all experiments of one case study.
/// </summary>
public static void clear()
{
varModel = null;
currentNFP = null;
allMeasurements = new ResultDB();
evaluationSet = new ResultDB();
nfProperties = new Dictionary <string, NFProperty>();
optionOrder = new List <ConfigurationOption>();
}
/// <summary>
/// Clears the global state. This mehtod should be used after performing all experiments of one case study.
/// </summary>
public static void clear()
{
varModel = null;
currentNFP = null;
allMeasurements = new ResultDB();
evalutionSet = new ResultDB();
infModel = null;
nfProperties = new Dictionary<string,NFProperty>();
optionOrder = new List<ConfigurationOption>();
}
/// <summary>
/// This method searches for a corresponding methods in the dynamically loadeda assemblies and calls it if found. It prefers due to performance reasons the Microsoft Solver Foundation implementation.
/// </summary>
/// <param name="nfp">The non-funcitonal property for which the influences of configuration options are to be computed. If null, we use the property of the global model.</param>
/// <param name="infModel">The influence model containing options and interactions. The state of the model will be changed by the result of the process</param>
/// <param name="db">The result database containing the measurements.</param>
/// <returns>A map of binary options to their computed influences.</returns>
public Dictionary<BinaryOption, double> computeOptionInfluences(NFProperty nfp, InfluenceModel infModel, ResultDB db)
{
foreach (Lazy<ISolverLP, ISolverType> solver in solvers)
{
if (solver.Metadata.SolverType.Equals("MSSolverFoundation")) return solver.Value.computeOptionInfluences(nfp, infModel, db);
}
//If not MS Solver, take any solver. Should be changed when supporting more than 2 solvers here
foreach (Lazy<ISolverLP, ISolverType> solver in solvers)
{
return solver.Value.computeOptionInfluences(nfp, infModel, db);
}
return null;
}
/// <summary>
/// This method searches for a corresponding methods in the dynamically loadeda assemblies and calls it if found. It prefers due to performance reasons the Microsoft Solver Foundation implementation.
/// </summary>
/// <param name="nfp">The non-funcitonal property for which the influences of configuration options are to be computed. If null, we use the property of the global model.</param>
/// <param name="infModel">The influence model containing the variability model, all configuration options and interactions.</param>
/// <param name="db">The result database containing the measurements.</param>
/// <param name="evaluateFeatureInteractionsOnly">Only interactions are learned.</param>
/// <param name="withDeviation">(Not used) We can specifiy whether learned influences must be greater than a certain value (e.g., greater than measurement bias).</param>
/// <param name="deviation">(Not used) We can specifiy whether learned influences must be greater than a certain value (e.g., greater than measurement bias).</param>
/// <returns>Returns the learned infleunces of each option in a map whereas the String (Key) is the name of the option / interaction.</returns>
public Dictionary<string, double> computeOptionInfluences(NFProperty nfp, InfluenceModel infModel, ResultDB db, bool evaluateFeatureInteractionsOnly, bool withDeviation, double deviation)
{
foreach (Lazy<ISolverLP, ISolverType> solver in solvers)
{
if (solver.Metadata.SolverType.Equals("MSSolverFoundation")) return solver.Value.computeOptionInfluences(nfp, infModel, db, evaluateFeatureInteractionsOnly, withDeviation, deviation);
}
//If not MS Solver, take any solver. Should be changed when supporting more than 2 solvers here
foreach (Lazy<ISolverLP, ISolverType> solver in solvers)
{
return solver.Value.computeOptionInfluences(nfp, infModel, db, evaluateFeatureInteractionsOnly, withDeviation, deviation);
}
return null;
}
/// <summary>
/// Computes the influence of all configuration options and interactions based on the measurements of the given result db. It uses linear programming (simplex) and is an exact algorithm.
/// </summary>
/// <param name="nfp">The non-funcitonal property for which the influences of configuration options are to be computed. If null, we use the property of the global model.</param>
/// <param name="infModel">The influence model containing the variability model, all configuration options and interactions.</param>
/// <param name="db">The result database containing the measurements.</param>
/// <param name="evaluateFeatureInteractionsOnly">Only interactions are learned.</param>
/// <param name="withDeviation">(Not used) We can specifiy whether learned influences must be greater than a certain value (e.g., greater than measurement bias).</param>
/// <param name="deviation">(Not used) We can specifiy whether learned influences must be greater than a certain value (e.g., greater than measurement bias).</param>
/// <returns>Returns the learned infleunces of each option in a map whereas the String (Key) is the name of the option / interaction.</returns>
public Dictionary<String, double> computeOptionInfluences(NFProperty nfp, InfluenceModel infModel, ResultDB db, bool evaluateFeatureInteractionsOnly, bool withDeviation, double deviation)
{
//Initialization
List<List<BinaryOption>> configurations = new List<List<BinaryOption>>();
this.evaluateInteractionsOnly = evaluateFeatureInteractionsOnly;
this.withStandardDeviation = withDeviation;
this.standardDeviation = deviation;
List<double> results = new List<double>();
foreach (Configuration c in db.Configurations)
{
configurations.Add(c.getBinaryOptions(BinaryOption.BinaryValue.Selected));
if (nfp != null)
results.Add(c.GetNFPValue(nfp));
else
results.Add(c.GetNFPValue());
}
List<BinaryOption> variables = new List<BinaryOption>();
Dictionary<String, double> featureValues = new Dictionary<string, double>();
Dictionary<String, double> faultRates = new Dictionary<string, double>();
List<int> indexOfErrorMeasurements = new List<int>();
if (configurations.Count == 0)
return null;
//For the case there is an empty base
if (configurations.Count != 0)
{
if (configurations[0].Count == 0)
{//Should never occur that we get a configuration with no option selected... at least the root must be there
BinaryOption root = infModel.Vm.Root;
//Element baseElement = new Element("base_gen", infModel.getID(), infModel);
//variables.Add(baseElement);
// featureValues.Add(baseElement.getName(), 0);
foreach (List<BinaryOption> config in configurations)
if(!config.Contains(root))
config.Insert(0, root);
}
}
//Building the variable list
foreach (var elem in infModel.Vm.BinaryOptions)
{
variables.Add(elem);
featureValues.Add(elem.Name, 0);
}
//First run
featureValues = solve(variables, results, configurations, null);
//if (evaluateFeatureInteractionsOnly == false)
return featureValues;
/*
//We might have some interactions here and cannot compute all values
//1. identify options that are only present in these equations
Dictionary<Element, int> featureCounter = new Dictionary<Element, int>();
for (int i = 0; i < indexOfErrorMeasurements.Count; i++)
{
}
*/
/*Todo: get compute interactins from deviations / errors of the LP results
if (errorEqs != null)
{
foreach (string eq in errorEqs)
{
double value = Double.Parse(eq.Substring(eq.IndexOf("==") + 2));
StringBuilder sb = new StringBuilder();
List<Element> derivativeParents = new List<Element>();
sb.Append("derivate_");
string[] splittedEQ = eq.Split('+');
foreach (string element in splittedEQ)
{
string name = element;
if (name.Contains("=="))
name = name.Substring(0, name.IndexOf("=="));
if (name.Contains("yp") && name.Contains("-yn"))
continue;
// string featureName = name.Substring(0, name.IndexOf("_p-"));
Element elem = infModel.getElementByNameUnsafe(name);
if (elem == null)
continue;
sb.Append("_" + name);
derivativeParents.Add(elem);
}
Element interaction = new Element(sb.ToString(), infModel.getID(), infModel);
interaction.setType("derivative");
interaction.addDerivativeParents(derivativeParents);
infModel.addElement(interaction);
this.featureValues.Add(interaction, value);
}
}
return featureValues;*/
}
