/// <summary> /// Generates up to n valid binary combinations of all binary configuration options in the given model. /// In case n < 0 all valid binary combinations will be generated. /// </summary> /// <param name="m">The variability model containing the binary options and their constraints.</param> /// <param name="n">The maximum number of samples that will be generated.</param> /// <returns>Returns a list of configurations, in which a configuration is a list of SELECTED binary options (deselected options are not present)</returns> public List <List <BinaryOption> > GenerateUpToNFast(VariabilityModel m, int n) { List <List <BinaryOption> > configurations = new List <List <BinaryOption> >(); List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, m); ConstraintSolverSolution soln = S.Solve(); // TODO: Better solution than magic number? while (soln.HasFoundSolution && (configurations.Count < n || n < 0)) { List <BinaryOption> config = new List <BinaryOption>(); foreach (CspTerm cT in variables) { if (soln.GetIntegerValue(cT) == 1) { config.Add(termToElem[cT]); } } //THese should always be new configurations // if(!Configuration.containsBinaryConfiguration(configurations, config)) configurations.Add(config); soln.GetNext(); } return(configurations); }
private void AddBinaryConfigurationsToConstraintSystem(VariabilityModel vm, ConstraintSystem s, Configuration configurationToExclude, Dictionary <BinaryOption, CspTerm> elemToTerm) { List <BinaryOption> allBinaryOptions = vm.BinaryOptions; List <CspTerm> positiveTerms = new List <CspTerm>(); List <CspTerm> negativeTerms = new List <CspTerm>(); foreach (BinaryOption binOpt in allBinaryOptions) { if (configurationToExclude.BinaryOptions.ContainsKey(binOpt) && configurationToExclude.BinaryOptions[binOpt] == BinaryOption.BinaryValue.Selected) { positiveTerms.Add(elemToTerm[binOpt]); } else { negativeTerms.Add(elemToTerm[binOpt]); } } if (negativeTerms.Count > 0) { positiveTerms.Add(s.Not(s.And(negativeTerms.ToArray()))); } s.AddConstraints(s.Not(s.And(positiveTerms.ToArray()))); }
/// <summary> /// Generates all valid binary combinations of all binary configurations options in the given model /// </summary> /// <param name="vm">The variability model containing the binary options and their constraints.</param> /// <returns>Returns a list of configurations, in which a configuration is a list of SELECTED binary options (deselected options are not present)</returns> public List <List <BinaryOption> > generateAllVariantsFast(VariabilityModel vm) { List <List <BinaryOption> > configurations = new List <List <BinaryOption> >(); List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); ConstraintSolverSolution soln = S.Solve(); while (soln.HasFoundSolution) { List <BinaryOption> config = new List <BinaryOption>(); foreach (CspTerm cT in variables) { if (soln.GetIntegerValue(cT) == 1) { config.Add(termToElem[cT]); } } //THese should always be new configurations // if(!Configuration.containsBinaryConfiguration(configurations, config)) configurations.Add(config); soln.GetNext(); } return(configurations); }
/// <summary> /// Checks whether the boolean selection is valid w.r.t. the variability model. Does not check for numeric options' correctness. /// </summary> /// <param name="config">The list of binary options that are SELECTED (only selected options must occur in the list).</param> /// <param name="vm">The variability model that represents the context of the configuration.</param> /// <param name="partialConfiguration">Whether the given list of options represents only a partial configuration. This means that options not in config might be additionally select to obtain a valid configuration.</param> /// <returns>True if it is a valid selection w.r.t. the VM, false otherwise</returns> public bool checkConfigurationSAT(List <BinaryOption> config, VariabilityModel vm, bool partialConfiguration) { List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); //Feature Selection foreach (BinaryOption binayOpt in elemToTerm.Keys) { CspTerm term = elemToTerm[binayOpt]; if (config.Contains(binayOpt)) { S.AddConstraints(S.Implies(S.True, term)); } else if (!partialConfiguration) { S.AddConstraints(S.Implies(S.True, S.Not(term))); } } ConstraintSolverSolution sol = S.Solve(); if (sol.HasFoundSolution) { return(true); } else { return(false); } }
/// <summary> /// This constructor creates a new <see cref="ConstraintSystemCache"/> with the given parameters. /// </summary> /// <param name="cs">The <see cref="ConstraintSystem"/> to store.</param> /// <param name="variables">The variables used in the <see cref="ConstraintSystem"/>.</param> /// <param name="elemToTerm">The mapping from <see cref="BinaryOption"/> to <see cref="CspTerm"/>.</param> /// <param name="termToElem">The mapping from <see cref="CspTerm"/> to <see cref="BinaryOption"/>.</param> public ConstraintSystemCache(ConstraintSystem cs, List <CspTerm> variables, Dictionary <BinaryOption, CspTerm> elemToTerm, Dictionary <CspTerm, BinaryOption> termToElem) { this._cs = cs; this._variables = variables; this._elemToTerm = elemToTerm; this._termToElem = termToElem; }
/// <summary> /// The method aims at finding a configuration which is similar to the given configuration, but does not contain the optionToBeRemoved. If further options need to be removed from the given configuration, they are outputed in removedElements. /// Idea: Encode this as a CSP problem. We aim at finding a configuration that maximizes a goal. Each option of the given configuration gets a large value assigned. All other options of the variability model gets a negative value assigned. /// We will further create a boolean constraint that forbids selecting the optionToBeRemoved. Now, we find an optimal valid configuration. /// </summary> /// <param name="optionToBeRemoved">The binary configuration option that must not be part of the new configuration.</param> /// <param name="originalConfig">The configuration for which we want to find a similar one.</param> /// <param name="removedElements">If further options need to be removed from the given configuration to build a valid configuration, they are outputed in this list.</param> /// <param name="vm">The variability model containing all options and their constraints.</param> /// <returns>A configuration that is valid, similar to the original configuration and does not contain the optionToBeRemoved.</returns> public List <BinaryOption> GenerateConfigWithoutOption(BinaryOption optionToBeRemoved, List <BinaryOption> originalConfig, out List <BinaryOption> removedElements, VariabilityModel vm) { List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); removedElements = new List <BinaryOption>(); //Forbid the selection of this configuration option CspTerm optionToRemove = elemToTerm[optionToBeRemoved]; S.AddConstraints(S.Implies(S.True, S.Not(optionToRemove))); //Defining Goals CspTerm[] finalGoals = new CspTerm[variables.Count]; int r = 0; foreach (var term in variables) { if (originalConfig.Contains(termToElem[term])) { finalGoals[r] = term * -1000; //Since we minimize, we put a large negative value of an option that is within the original configuration to increase chances that the option gets selected again } else { finalGoals[r] = variables[r] * 10000;//Positive number will lead to a small chance that an option gets selected when it is not in the original configuration } r++; } S.TryAddMinimizationGoals(S.Sum(finalGoals)); ConstraintSolverSolution soln = S.Solve(); List <BinaryOption> tempConfig = new List <BinaryOption>(); if (soln.HasFoundSolution && soln.Quality == ConstraintSolverSolution.SolutionQuality.Optimal) { tempConfig.Clear(); foreach (CspTerm cT in variables) { if (soln.GetIntegerValue(cT) == 1) { tempConfig.Add(termToElem[cT]); } } //Adding the options that have been removed from the original configuration foreach (var opt in originalConfig) { if (!tempConfig.Contains(opt)) { removedElements.Add(opt); } } return(tempConfig); } return(null); }
public ComprehensionData(Node node, ConstraintSystem owner, int depth) { Contract.Requires(node != null && owner != null && depth > 0); Contract.Requires(node.NodeKind == NodeKind.Compr); Node = node; Owner = owner; Depth = depth; }
public ZebraPuzzleConstraints(ConstraintSystem solver) { Solver = solver; HouseNumbers = ThereAreFiveHouses(); ColourMatrix = CreateConstrainSystemMatrix(Solver); DrinkMatrix = CreateConstrainSystemMatrix(Solver); NationalityMatrix = CreateConstrainSystemMatrix(Solver); SmokeMatrix = CreateConstrainSystemMatrix(Solver); PetMatrix = CreateConstrainSystemMatrix(Solver); }
static void Main() { var solver = ConstraintSystem.CreateSolver(); var constraints = new ZebraPuzzleConstraints(solver); solver = AddConstraintsToSolver(solver, constraints); var solution = solver.Solve(new ConstraintSolverParams()); var solutionTable = ConvertToSolutionTable(solution, constraints); WriteZebraSolutionToConsole(solutionTable); WriteSolutionForAllTheHousesToConsole(solutionTable); }
public void Solve(int?[,] field) { ConstraintSystem S = ConstraintSystem.CreateSolver(); CspDomain Z = S.CreateIntegerInterval(1, 9); CspTerm[][] sudoku = S.CreateVariableArray(Z, "cell", 9, 9); for (int row = 0; row < 9; row++) { for (int col = 0; col < 9; col++) { if (field[row, col] > 0) { S.AddConstraints(S.Equal(field[row, col] ?? 0, sudoku[row][col])); } } S.AddConstraints(S.Unequal(GetSlice(sudoku, row, row, 0, 8))); } for (int col = 0; col < 9; col++) { S.AddConstraints(S.Unequal(GetSlice(sudoku, 0, 8, col, col))); } for (int a = 0; a < 3; a++) { for (int b = 0; b < 3; b++) { S.AddConstraints(S.Unequal(GetSlice(sudoku, a * 3, a * 3 + 2, b * 3, b * 3 + 2))); } } ConstraintSolverSolution soln = S.Solve(); if (!soln.HasFoundSolution) { throw new NoSolutionException("Для поставленной цифры нет решение!"); } object[] h = new object[9]; for (int row = 0; row < 9; row++) { if ((row % 3) == 0) { System.Console.WriteLine(); } for (int col = 0; col < 9; col++) { soln.TryGetValue(sudoku[row][col], out h[col]); } System.Console.WriteLine("{0}{1}{2} {3}{4}{5} {6}{7}{8}", h[0], h[1], h[2], h[3], h[4], h[5], h[6], h[7], h[8]); } }
/// <summary> /// Generates all valid combinations of all configuration options in the given model. /// </summary> /// <param name="vm">the variability model containing the binary options and their constraints</param> /// <param name="optionsToConsider">the options that should be considered. All other options are ignored</param> /// <returns>Returns a list of <see cref="Configuration"/></returns> public List <Configuration> GenerateAllVariants(VariabilityModel vm, List <ConfigurationOption> optionsToConsider) { List <Configuration> allConfigurations = new List <Configuration>(); Dictionary <CspTerm, bool> variables; Dictionary <ConfigurationOption, CspTerm> optionToTerm; Dictionary <CspTerm, ConfigurationOption> termToOption; ConstraintSystem S = CSPsolver.GetGeneralConstraintSystem(out variables, out optionToTerm, out termToOption, vm); ConstraintSolverSolution soln = S.Solve(); while (soln.HasFoundSolution) { Dictionary <BinaryOption, BinaryOption.BinaryValue> binOpts = new Dictionary <BinaryOption, BinaryOption.BinaryValue>(); Dictionary <NumericOption, double> numOpts = new Dictionary <NumericOption, double>(); foreach (CspTerm ct in variables.Keys) { // Ignore all options that should not be considered. if (!optionsToConsider.Contains(termToOption[ct])) { continue; } // If it is a binary option if (variables[ct]) { BinaryOption.BinaryValue isSelected = soln.GetIntegerValue(ct) == 1 ? BinaryOption.BinaryValue.Selected : BinaryOption.BinaryValue.Deselected; if (isSelected == BinaryOption.BinaryValue.Selected) { binOpts.Add((BinaryOption)termToOption[ct], isSelected); } } else { numOpts.Add((NumericOption)termToOption[ct], soln.GetIntegerValue(ct)); } } Configuration c = new Configuration(binOpts, numOpts); // Check if the non-boolean constraints are satisfied if (vm.configurationIsValid(c) && !IsInConfigurationFile(c, allConfigurations) && FulfillsMixedConstraints(c, vm)) { allConfigurations.Add(c); } soln.GetNext(); } return(allConfigurations); }
/// <summary> /// Checks whether the boolean selection is valid w.r.t. the variability model. Does not check for numeric options' correctness. /// </summary> /// <param name="config">The list of binary options that are SELECTED (only selected options must occur in the list).</param> /// <param name="vm">The variability model that represents the context of the configuration.</param> /// <param name="exact">Checks also the number of selected options such that it returns only true if exactly the given configuration is valid /// (e.g., if we need to select more features to get a valid config, it returns false if exact is set to true). /// <returns>True if it is a valid selection w.r.t. the VM, false otherwise</returns> public bool checkConfigurationSAT(List <BinaryOption> config, VariabilityModel vm, bool exact) { List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); //Feature Selection foreach (BinaryOption binayOpt in elemToTerm.Keys) { CspTerm term = elemToTerm[binayOpt]; if (config.Contains(binayOpt)) { S.AddConstraints(S.Implies(S.True, term)); } else { if (exact) { S.AddConstraints(S.Implies(S.True, S.Not(term))); } } } ConstraintSolverSolution sol = S.Solve(); if (sol.HasFoundSolution) { int count = 0; foreach (CspTerm cT in variables) { if (sol.GetIntegerValue(cT) == 1) { count++; } } //Needs testing TODO if (count != config.Count && exact == true) { return(false); } return(true); } else { return(false); } }
internal Action( Node head, ConstraintSystem body, TypeEnvironment typeEnv, ComprehensionData comprData = null, Node configurationContext = null) { Head = head; Body = body; myComprData = comprData; this.configurationContext = configurationContext; typeEnvironment = typeEnv; theUnnSymbol = body.Index.SymbolTable.GetOpSymbol(ReservedOpKind.TypeUnn); theRngSymbol = body.Index.SymbolTable.GetOpSymbol(ReservedOpKind.Range); }
public override void AddConstaint() { ConstraintSystem solver = ConstraintSystemSolver.Instance.Solver; CspTerm constraint = null; CspTerm inputTerm = Input1.CspTerm; CspTerm outputTerm = Output.CspTerm; Type consType = type; if (IsNotHealthy) { // In case the gate is Broken (Not Healthy) - we don't want to add any constraint!!! return; /* * switch (type) * { * case Type.buffer: * consType = Type.not; * break; * case Type.not: * consType = Type.buffer; * break; * } */ } lock (ConstraintSystemSolver.Instance.Locker) { //Debug.WriteLine("SAT IN!"); switch (consType) { case Type.buffer: constraint = solver.Equal(inputTerm, outputTerm); break; case Type.not: constraint = solver.Equal(inputTerm, solver.Not(outputTerm)); break; } solver.AddConstraints(constraint); //Debug.WriteLine("SAT OUT!"); } }
static void Main(string[] args) { ConstraintSystem s1 = ConstraintSystem.CreateSolver(); // () and () CspTerm p1 = s1.CreateBoolean("p1"); CspTerm p2 = s1.CreateBoolean("p2"); CspTerm p3 = s1.CreateBoolean("p3"); CspTerm p4 = s1.CreateBoolean("p4"); var x = new CspDomain(); CspTerm test = s1.And(s1.Or(p1, s1.And(s1.Neg(p3)), s1.Neg(p1)), s1.And(p2, s1.Neg(s1.Difference(p1, p2)))); CspTerm tOr12 = s1.Or(s1.Neg(t1), s1.Neg(t2)); CspTerm tOr13 = s1.Or(s1.Neg(t1), s1.Neg(t3)); CspTerm tOr14 = s1.Or(s1.Neg(t1), s1.Neg(t4)); CspTerm tOr23 = s1.Or(s1.Neg(t2), s1.Neg(t3)); CspTerm tOr24 = s1.Or(s1.Neg(t2), s1.Neg(t4)); CspTerm tOr34 = s1.Or(s1.Neg(t3), s1.Neg(t4)); CspTerm tOr = s1.Or(t1, t2, t3, t4); s1.AddConstraints(tOr12); s1.AddConstraints(tOr13); s1.AddConstraints(tOr14); s1.AddConstraints(tOr23); s1.AddConstraints(tOr24); s1.AddConstraints(tOr34); s1.AddConstraints(tOr); ConstraintSolverSolution solution1 = s1.Solve(); if (solution1.HasFoundSolution) { Console.WriteLine("Is Satisfiable"); } else { Console.WriteLine("Not satisfiable"); } Console.ReadKey(); }
private List <List <BinaryOption> > generateTilSize(int i1, int size, int timeout, VariabilityModel vm) { var foundSolutions = new List <List <BinaryOption> >(); List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); CspTerm t = S.ExactlyMofN(i1, variables.ToArray()); S.AddConstraints(new CspTerm[] { t }); var csp = new ConstraintSolverParams { TimeLimitMilliSec = timeout * 1000, }; ConstraintSolverSolution soln = S.Solve(csp); int counter = 0; while (soln.HasFoundSolution) { List <BinaryOption> tempConfig = ( from cT in variables where soln.GetIntegerValue(cT) == 1 select termToElem[cT]).ToList(); if (tempConfig.Contains(null)) { tempConfig.Remove(null); } foundSolutions.Add(tempConfig); counter++; if (counter == size) { break; } soln.GetNext(); } //Console.WriteLine(i1 + "\t" + foundSolutions.Count); return(foundSolutions); }
/// <summary> /// Creates a sample of configurations, by iteratively adding a configuration that has the maximal manhattan distance /// to the configurations that were previously selected. /// </summary> /// <param name="vm">The domain for sampling.</param> /// <param name="minimalConfiguration">A minimal configuration that will be used as starting point.</param> /// <param name="numberToSample">The number of configurations that should be sampled.</param> /// <param name="optionWeight">Weight assigned to optional binary options.</param> /// <returns>A list of distance maximized configurations.</returns> public List <List <BinaryOption> > DistanceMaximization(VariabilityModel vm, List <BinaryOption> minimalConfiguration, int numberToSample, int optionWeight) { List <Configuration> sample = new List <Configuration>(); List <List <BinaryOption> > convertedSample = new List <List <BinaryOption> >(); sample.Add(new Configuration(minimalConfiguration)); convertedSample.Add(minimalConfiguration); List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); while (sample.Count < numberToSample) { ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); addDistanceMaximiationGoal(sample, vm, elemToTerm, S, optionWeight); ConstraintSolverSolution sol = S.Solve(); if (sol.HasFoundSolution) { List <BinaryOption> solution = new List <BinaryOption>(); foreach (CspTerm cT in variables) { if (sol.GetIntegerValue(cT) == 1) { solution.Add(termToElem[cT]); } } S.ResetSolver(); convertedSample.Add(solution); sample.Add(new Configuration(solution)); } else { GlobalState.logInfo.logLine("No more solutions available."); return(convertedSample); } } return(convertedSample); }
CspTerm[][] CreateConstrainSystemMatrix(ConstraintSystem system) { var size = HouseNumbers.Count; var matrix = system.CreateBooleanArray(new object(), size, size); Enumerable.Range(0, size).ToList().ForEach(i => { var row = system.CreateBooleanVector(new object(), size); var column = system.CreateBooleanVector(new object(), size); Enumerable.Range(0, size).ToList().ForEach(j => { row[j] = matrix[i][j]; column[j] = matrix[j][i]; }); system.AddConstraints(system.Equal(1, system.Sum(row))); system.AddConstraints(system.Equal(1, system.Sum(column))); }); return(matrix); }
/* public List<List<string>> generateDimacsPseudoRandom(string[] lines,int features, int randomsize, BackgroundWorker worker) * { * var cts = new CancellationTokenSource(); * var erglist = new List<List<string>>(); * * var tasks = new Task[features]; * var mylock = new object(); * * for (var i = 0; i < features; i++) * { * var i1 = i; * tasks[i] = Task.Factory.StartNew(() => * { * Console.WriteLine("Starting: " + i1); * var sw = new Stopwatch(); * sw.Start(); * var result = generateDimacsSize(lines, i1, randomsize); * sw.Stop(); * Console.WriteLine("Done: " + i1 + "\tDuration: " + sw.ElapsedMilliseconds + "\tResults: " + result.Count); * return result; * * }, cts.Token).ContinueWith(task => * { * lock (mylock) * { * * erglist.AddRange(task.Result); * * Console.WriteLine("Added results: " + i1 + "\tResults now: " + erglist.Count); * counter++; * //worker.ReportProgress((int)(counter * 100.0f / (double)features), erglist.Count); * } * }); * * if (Task.WaitAny(new[] { tasks[i] }, TimeSpan.FromMilliseconds(60 * 1000)) < 0) * { * cts.Cancel(); * } * } * * Task.WaitAll(tasks); * * return erglist; * } */ /// <summary> /// Simulates a simple method to get valid configurations of binary options of a variability model. The randomness is simulated by the modulu value. /// We take only the modulu'th configuration into the result set based on the CSP solvers output. If modulu is larger than the number of valid variants, the result set is empty. /// </summary> /// <param name="vm">The variability model containing the binary options and their constraints.</param> /// <param name="treshold">Maximum number of configurations</param> /// <param name="modulu">Each configuration that is % modulu == 0 is taken to the result set. Can be less than the maximal (i.e. threshold) specified number of configurations.</param> /// <returns>Returns a list of configurations, in which a configuration is a list of SELECTED binary options (deselected options are not present</returns> public List <List <BinaryOption> > generateRandomVariants(VariabilityModel vm, int treshold, int modulu) { List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); List <List <BinaryOption> > erglist = new List <List <BinaryOption> >(); ConstraintSolverSolution soln = S.Solve(); int mod = 0; while (soln.HasFoundSolution) { mod++; if (mod % modulu != 0) { soln.GetNext(); continue; } List <BinaryOption> tempConfig = new List <BinaryOption>(); foreach (CspTerm cT in variables) { if (soln.GetIntegerValue(cT) == 1) { tempConfig.Add(termToElem[cT]); } } if (tempConfig.Contains(null)) { tempConfig.Remove(null); } erglist.Add(tempConfig); if (erglist.Count == treshold) { break; } soln.GetNext(); } return(erglist); }
/// <summary> /// Simulates a simple method to get valid configurations of binary options of a variability model. The randomness is simulated by the modulu value. /// We take only the modulu'th configuration into the result set based on the CSP solvers output. If modulu is larger than the number of valid variants, the result set is empty. /// </summary> /// <param name="vm">The variability model containing the binary options and their constraints.</param> /// <param name="treshold">Maximum number of configurations</param> /// <param name="modulu">Each configuration that is % modulu == 0 is taken to the result set</param> /// <returns>Returns a list of configurations, in which a configuration is a list of SELECTED binary options (deselected options are not present</returns> public List <List <BinaryOption> > GenerateRandomVariants(VariabilityModel vm, int treshold, int modulu) { List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); List <List <BinaryOption> > erglist = new List <List <BinaryOption> >(); ConstraintSolverSolution soln = S.Solve(); List <List <BinaryOption> > allConfigs = new List <List <BinaryOption> >(); while (soln.HasFoundSolution) { soln.GetNext(); List <BinaryOption> tempConfig = new List <BinaryOption>(); foreach (CspTerm cT in variables) { if (soln.GetIntegerValue(cT) == 1) { tempConfig.Add(termToElem[cT]); } } if (tempConfig.Contains(null)) { tempConfig.Remove(null); } allConfigs.Add(tempConfig); } Random r = new Random(modulu); for (int i = 0; i < treshold; i++) { erglist.Add(allConfigs[r.Next(allConfigs.Count)]); } return(erglist); }
private static ConstraintSystem AddConstraintsToSolver( ConstraintSystem solver, ZebraPuzzleConstraints constraints) { solver.AddConstraints( constraints.TheEnglishManLivesInTheRedHouse()); solver.AddConstraints( constraints.TheSwedeHasADog()); solver.AddConstraints( constraints.TheDaneDrinksTea()); solver.AddConstraints( constraints.TheGreenHouseIsImmediatelyLeftOfTheWhiteHouse()); solver.AddConstraints( constraints.TheyDrinkCoffeeInTheGreenHouse()); solver.AddConstraints( constraints.TheManWhoSmokesPallMallHasBirds()); solver.AddConstraints( constraints.InTheYellowHouseTheySmokeDunhill()); solver.AddConstraints( constraints.InTheMiddleHouseTheyDrinkMilk()); solver.AddConstraints( constraints.TheNorwegianLivesInTheFirstHouse()); solver.AddConstraints( constraints.TheManWhoSmokesBlendInHouseNextToHouseWithCats()); solver.AddConstraints( constraints.InHouseNextToHouseWhereHaveAHorseSmokeDunhill()); solver.AddConstraints( constraints.TheManWhoSmokesBlueMasterDrinksBeer()); solver.AddConstraints( constraints.TheGermanSmokesPrince()); solver.AddConstraints( constraints.TheNorwegianLivesNextToTheBlueHouse()); solver.AddConstraints( constraints.TheyDrinkWaterInHouseNextToHouseWhereSmokeBlend()); return(solver); }
/// <summary> /// Generates a constraint system based on a variability model. The constraint system can be used to check for satisfiability of configurations as well as optimization. /// </summary> /// <param name="variables">Empty input, outputs a list of CSP terms that correspond to the configuration options of the variability model</param> /// <param name="optionToTerm">A map to get for a given configuration option the corresponding CSP term of the constraint system</param> /// <param name="termToOption">A map that gives for a given CSP term the corresponding configuration option of the variability model</param> /// <param name="vm">The variability model for which we generate a constraint system</param> /// <returns>The generated constraint system consisting of logical terms representing configuration options as well as their boolean constraints.</returns> internal static ConstraintSystem getConstraintSystem(out List<CspTerm> variables, out Dictionary<BinaryOption, CspTerm> optionToTerm, out Dictionary<CspTerm, BinaryOption> termToOption, VariabilityModel vm) { //Reusing seems to not work correctely. The problem: configurations are realized as additional constraints for the system. //however, when checking for the next config, the old config's constraints remain in the solver such that we have a wrong result. /* if (csystem != null && variables_global != null && optionToTerm_global != null && termToOption_global != null && vm != null) {//For optimization purpose if (vm.BinaryOptions.Count == vm_global.BinaryOptions.Count && vm.Name.Equals(vm_global.Name)) { variables = variables_global; optionToTerm = optionToTerm_global; termToOption = termToOption_global; return csystem; } }*/ ConstraintSystem S = ConstraintSystem.CreateSolver(); optionToTerm = new Dictionary<BinaryOption, CspTerm>(); termToOption = new Dictionary<CspTerm, BinaryOption>(); variables = new List<CspTerm>(); foreach (BinaryOption binOpt in vm.BinaryOptions) { CspDomain domain = S.DefaultBoolean; CspTerm temp = S.CreateVariable(domain, binOpt); optionToTerm.Add(binOpt, temp); termToOption.Add(temp, binOpt); variables.Add(temp); } List<List<ConfigurationOption>> alreadyHandledAlternativeOptions = new List<List<ConfigurationOption>>(); //Constraints of a single configuration option foreach (BinaryOption current in vm.BinaryOptions) { CspTerm cT = optionToTerm[current]; if (current.Parent == null || current.Parent == vm.Root) { if (current.Optional == false && current.Excluded_Options.Count == 0) S.AddConstraints(S.Implies(S.True, cT)); else S.AddConstraints(S.Implies(cT, optionToTerm[vm.Root])); } if (current.Parent != null && current.Parent != vm.Root) { CspTerm parent = optionToTerm[(BinaryOption)current.Parent]; S.AddConstraints(S.Implies(cT, parent)); if (current.Optional == false && current.Excluded_Options.Count == 0) S.AddConstraints(S.Implies(parent, cT));//mandatory child relationship } //Alternative or other exclusion constraints if (current.Excluded_Options.Count > 0) { List<ConfigurationOption> alternativeOptions = current.collectAlternativeOptions(); if (alternativeOptions.Count > 0) { //Check whether we handled this group of alternatives already foreach (var alternativeGroup in alreadyHandledAlternativeOptions) foreach (var alternative in alternativeGroup) if (current == alternative) goto handledAlternative; //It is not allowed that an alternative group has no parent element CspTerm parent = null; if (current.Parent == null) parent = S.True; else parent = optionToTerm[(BinaryOption)current.Parent]; CspTerm[] terms = new CspTerm[alternativeOptions.Count + 1]; terms[0] = cT; int i = 1; foreach (BinaryOption altEle in alternativeOptions) { CspTerm temp = optionToTerm[altEle]; terms[i] = temp; i++; } S.AddConstraints(S.Implies(parent, S.ExactlyMofN(1, terms))); alreadyHandledAlternativeOptions.Add(alternativeOptions); handledAlternative: { } } //Excluded option(s) as cross-tree constraint(s) List<List<ConfigurationOption>> nonAlternative = current.getNonAlternativeExlcudedOptions(); if(nonAlternative.Count > 0) { foreach(var excludedOption in nonAlternative){ CspTerm[] orTerm = new CspTerm[excludedOption.Count]; int i = 0; foreach (var opt in excludedOption) { CspTerm target = optionToTerm[(BinaryOption)opt]; orTerm[i] = target; i++; } S.AddConstraints(S.Implies(cT, S.Not(S.Or(orTerm)))); } } } //Handle implies if (current.Implied_Options.Count > 0) { foreach (List<ConfigurationOption> impliedOr in current.Implied_Options) { CspTerm[] orTerms = new CspTerm[impliedOr.Count]; //Possible error: if a binary option impies a numeric option for (int i = 0; i < impliedOr.Count; i++) orTerms[i] = optionToTerm[(BinaryOption)impliedOr.ElementAt(i)]; S.AddConstraints(S.Implies(optionToTerm[current], S.Or(orTerms))); } } } //Handle global cross-tree constraints involving multiple options at a time // the constraints should be in conjunctive normal form foreach (string constraint in vm.BooleanConstraints) { bool and = false; string[] terms; if (constraint.Contains("&")) { and = true; terms = constraint.Split('&'); } else terms = constraint.Split('|'); CspTerm[] cspTerms = new CspTerm[terms.Count()]; int i = 0; foreach (string t in terms) { string optName = t.Trim(); if (optName.StartsWith("-") || optName.StartsWith("!")) { optName = optName.Substring(1); BinaryOption binOpt = vm.getBinaryOption(optName); CspTerm cspElem = optionToTerm[binOpt]; CspTerm notCspElem = S.Not(cspElem); cspTerms[i] = notCspElem; } else { BinaryOption binOpt = vm.getBinaryOption(optName); CspTerm cspElem = optionToTerm[binOpt]; cspTerms[i] = cspElem; } i++; } if (and) S.AddConstraints(S.And(cspTerms)); else S.AddConstraints(S.Or(cspTerms)); } csystem = S; optionToTerm_global = optionToTerm; vm_global = vm; termToOption_global = termToOption; variables_global = variables; return S; }
/// <summary> /// Based on a given (partial) configuration and a variability, we aim at finding all optimally maximal or minimal (in terms of selected binary options) configurations. /// </summary> /// <param name="config">The (partial) configuration which needs to be expaned to be valid.</param> /// <param name="vm">Variability model containing all options and their constraints.</param> /// <param name="minimize">If true, we search for the smallest (in terms of selected options) valid configuration. If false, we search for the largest one.</param> /// <param name="unwantedOptions">Binary options that we do not want to become part of the configuration. Might be part if there is no other valid configuration without them</param> /// <returns>A list of configurations that satisfies the VM and the goal (or null if there is none).</returns> public List <List <BinaryOption> > MaximizeConfig(List <BinaryOption> config, VariabilityModel vm, bool minimize, List <BinaryOption> unwantedOptions) { List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); //Feature Selection if (config != null) { foreach (BinaryOption binOpt in config) { CspTerm term = elemToTerm[binOpt]; S.AddConstraints(S.Implies(S.True, term)); } } //Defining Goals CspTerm[] finalGoals = new CspTerm[variables.Count]; for (int r = 0; r < variables.Count; r++) { if (minimize == true) { BinaryOption binOpt = termToElem[variables[r]]; if (unwantedOptions != null && (unwantedOptions.Contains(binOpt) && !config.Contains(binOpt))) { finalGoals[r] = variables[r] * 10000; } else { // Element is part of an altnerative Group ... we want to select always the same option of the group, so we give different weights to the member of the group //Functionality deactivated... todo needs further handling /*if (binOpt.getAlternatives().Count != 0) * { * finalGoals[r] = variables[r] * (binOpt.getID() * 10); * } * else * {*/ finalGoals[r] = variables[r] * 1; //} // wenn in einer alternative, dann bekommt es einen wert nach seiner reihenfolge // id mal 10 } } else { finalGoals[r] = variables[r] * -1; // dynamic cost map } } S.TryAddMinimizationGoals(S.Sum(finalGoals)); ConstraintSolverSolution soln = S.Solve(); List <string> erg2 = new List <string>(); List <BinaryOption> tempConfig = new List <BinaryOption>(); List <List <BinaryOption> > resultConfigs = new List <List <BinaryOption> >(); while (soln.HasFoundSolution && soln.Quality == ConstraintSolverSolution.SolutionQuality.Optimal) { tempConfig.Clear(); foreach (CspTerm cT in variables) { if (soln.GetIntegerValue(cT) == 1) { tempConfig.Add(termToElem[cT]); } } if (minimize && tempConfig != null) { resultConfigs.Add(tempConfig); break; } if (!Configuration.containsBinaryConfiguration(resultConfigs, tempConfig)) { resultConfigs.Add(tempConfig); } soln.GetNext(); } return(resultConfigs); }
private void addDistanceMaximiationGoal(List <Configuration> sample, VariabilityModel vm, Dictionary <BinaryOption, CspTerm> elemToTerm, ConstraintSystem cs, int weight) { List <CspTerm> goals = new List <CspTerm>(); foreach (Configuration config in sample) { List <CspTerm> sum = new List <CspTerm>(); List <BinaryOption> configInSample = convertToBinaryOptionList(config); foreach (BinaryOption binOpt in vm.BinaryOptions) { if (!configInSample.Contains(binOpt)) { if (binOpt.Optional) { sum.Add(weight * elemToTerm[binOpt]); } else { sum.Add(elemToTerm[binOpt]); } } else { if (binOpt.Optional) { sum.Add(weight * (cs.Abs(elemToTerm[binOpt] - cs.Constant(1)))); } else { sum.Add(cs.Abs(elemToTerm[binOpt] - cs.Constant(1))); } } } // negate term because we search for the biggest distance goals.Add(-1 * cs.Sum(sum.ToArray())); } cs.TryAddMinimizationGoals(cs.Sum(goals.ToArray())); }
/// <summary> /// This method searches for a corresponding methods in the dynamically loaded assemblies and calls it if found. It prefers due to performance reasons the Microsoft Solver Foundation implementation. /// </summary> /// <param name="config">The (partial) configuration which needs to be expaned to be valid.</param> /// <param name="vm">Variability model containing all options and their constraints.</param> /// <param name="minimize">If true, we search for the smallest (in terms of selected options) valid configuration. If false, we search for the largest one.</param> /// <param name="unWantedOptions">Binary options that we do not want to become part of the configuration. Might be part if there is no other valid configuration without them.</param> /// <returns>The valid configuration (or null if there is none) that satisfies the VM and the goal.</returns> public List <BinaryOption> MinimizeConfig(List <BinaryOption> config, VariabilityModel vm, bool minimize, List <BinaryOption> unWantedOptions) { List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); //Feature Selection foreach (BinaryOption binOpt in config) { CspTerm term = elemToTerm[binOpt]; S.AddConstraints(S.Implies(S.True, term)); } //Defining Goals CspTerm[] finalGoals = new CspTerm[variables.Count]; for (int r = 0; r < variables.Count; r++) { if (minimize == true) { if (unWantedOptions != null && (unWantedOptions.Contains(termToElem[variables[r]]) && !config.Contains(termToElem[variables[r]]))) { finalGoals[r] = variables[r] * 100; } else { finalGoals[r] = variables[r] * 1; } } else { finalGoals[r] = variables[r] * -1; // dynamic cost map } } S.TryAddMinimizationGoals(S.Sum(finalGoals)); ConstraintSolverSolution soln = S.Solve(); List <string> erg2 = new List <string>(); List <BinaryOption> tempConfig = new List <BinaryOption>(); while (soln.HasFoundSolution) { tempConfig.Clear(); foreach (CspTerm cT in variables) { if (soln.GetIntegerValue(cT) == 1) { tempConfig.Add(termToElem[cT]); } } if (minimize && tempConfig != null) { break; } soln.GetNext(); } return(tempConfig); }
static void Main(string[] args) { try { Console.WriteLine("Seleccione el método por el que desea resolver el problema:\n1 Programación por restricciones\n2 Programación Lineal"); switch (int.Parse(Console.ReadLine())) { case 1: SolverContext context = new SolverContext(); Model model = context.CreateModel(); // Creación de variables Decision x1 = new Decision(Domain.Integer, "x1"); model.AddDecision(x1); Decision x2 = new Decision(Domain.Integer, "x2"); model.AddDecision(x2); Decision x3 = new Decision(Domain.Integer, "x3"); model.AddDecision(x3); // Creación de limites model.AddConstraint("limitX1", 50 <= x1 <= 300); model.AddConstraint("limitX2", 100 <= x2 <= 200); model.AddConstraint("limitX3", 20 <= x3 <= 1000); // Creación de restricciones model.AddConstraint("restriccion1", 200 <= (x1 + x2 + x3) <= 280); model.AddConstraint("restriccion2", 100 <= (x1 + (3 * x3)) <= 2000); model.AddConstraint("restriccion3", 50 <= ((2 + x1) + (4 * x3)) <= 1000); // Función objetivo model.AddGoal("maximo", GoalKind.Maximize, -(4 * x1) - (2 * x2) + x3); // Solucion Solution solucion = context.Solve(new SimplexDirective()); Report reporte = solucion.GetReport(); // Imprimir Console.WriteLine(reporte); Console.ReadLine(); break; case 2: //Solucionador específico ConstraintSystem csp = ConstraintSystem.CreateSolver(); // Creacíón de variables CspTerm sx1 = csp.CreateVariable(csp.CreateIntegerInterval(50, 300), "x1"); CspTerm sx2 = csp.CreateVariable(csp.CreateIntegerInterval(100, 200), "x2"); CspTerm sx3 = csp.CreateVariable(csp.CreateIntegerInterval(20, 1000), "x3"); // Creación de restricciones csp.AddConstraints(200 <= (sx1 + sx2 + sx3) <= 280, 100 <= sx1 + (3 * sx2) <= 2000, 50 <= (2 * sx1) + (4 * sx3) <= 1000); // Solución ConstraintSolverSolution cspSolucion = csp.Solve(); int numero = 1; while (cspSolucion.HasFoundSolution) { object rx1, rx2, rx3; if (!cspSolucion.TryGetValue(sx1, out rx1) || !cspSolucion.TryGetValue(sx2, out rx2) || !cspSolucion.TryGetValue(sx3, out rx3)) { throw new InvalidProgramException("No se encontro solución"); } Console.WriteLine(String.Format("Solución {0} :\nx1={1}\nx2={2}\nx3={3}", numero, rx1, rx2, rx3)); numero += 1; cspSolucion.GetNext(); } /* * //Solucionador específico * SimplexSolver sSolver = new SimplexSolver(); * //Creación de variables * int sx1, sx2, sx3; * sSolver.AddVariable("x1", out sx1); * sSolver.SetBounds(sx1, 50, 300); * sSolver.AddVariable("x2", out sx2); * sSolver.SetBounds(sx2, 100, 200); * sSolver.AddVariable("x3", out sx3); * sSolver.SetBounds(sx3,20,1000); * //Creación de restricciones * int r1, r2, r3, goal; * sSolver.AddRow("restriccion1", out r1); * sSolver.SetCoefficient(r1, sx1, 1); * sSolver.SetCoefficient(r1, sx2, 1); * sSolver.SetCoefficient(r1, sx3, 1); * sSolver.SetBounds(r1, 200, 280); * sSolver.AddRow("restriccion2", out r2); * sSolver.SetCoefficient(r2, sx1, 1); * sSolver.SetCoefficient(r2, sx2, 3); * sSolver.SetBounds(r2, 100, 2000); * sSolver.AddRow("restriccion3", out r3); * sSolver.SetCoefficient(r3, sx1, 2); * sSolver.SetCoefficient(r3, sx3, 4); * sSolver.SetBounds(r3, 50, 1000); * //Función objetivo * sSolver.AddRow("objetivo", out goal); * sSolver.SetCoefficient(goal, sx1, -4); * sSolver.SetCoefficient(goal, sx2, -2); * sSolver.SetCoefficient(goal, sx3, 1); * sSolver.SetBounds(goal, Rational.NegativeInfinity,Rational.PositiveInfinity); * sSolver.AddGoal(goal, 1, false); * //Solución * sSolver.Solve(new SimplexSolverParams()); * sSolver.GetReport(); */ break; } } catch (Exception ex) { Console.WriteLine(ex.Message); Console.WriteLine(ex.StackTrace); Console.ReadLine(); } }
public List <List <BinaryOption> > generateRandomVariantsUntilSeconds(VariabilityModel vm, int seconds, int treshold, int modulu) { List <List <BinaryOption> > erglist = new List <List <BinaryOption> >(); var cts = new CancellationTokenSource(); var task = Task.Factory.StartNew(() => { #region task List <CspTerm> variables = new List <CspTerm>(); Dictionary <BinaryOption, CspTerm> elemToTerm = new Dictionary <BinaryOption, CspTerm>(); Dictionary <CspTerm, BinaryOption> termToElem = new Dictionary <CspTerm, BinaryOption>(); ConstraintSystem S = CSPsolver.getConstraintSystem(out variables, out elemToTerm, out termToElem, vm); ConstraintSolverSolution soln = S.Solve(); int mod = 0; while (soln.HasFoundSolution) { if (cts.IsCancellationRequested) { return(erglist); } mod++; if (mod % modulu != 0) { soln.GetNext(); continue; } List <BinaryOption> tempConfig = new List <BinaryOption>(); foreach (CspTerm cT in variables) { if (soln.GetIntegerValue(cT) == 1) { tempConfig.Add(termToElem[cT]); } } if (tempConfig.Contains(null)) { tempConfig.Remove(null); } erglist.Add(tempConfig); if (erglist.Count == treshold) { break; } soln.GetNext(); } return(erglist); #endregion }, cts.Token); if (Task.WaitAny(new[] { task }, TimeSpan.FromMilliseconds(seconds * 1000)) < 0) { Console.WriteLine("configsize: " + erglist.Count); cts.Cancel(); return(erglist); } return(erglist); }
public ConstraintSystemSolver() { Solver = ConstraintSystem.CreateSolver(); }
/// <summary> /// Knapsack enumerator -- enumerate up to "numAnswers" combinations of "weights" such that the sum of the weights is less than the weight limit. /// It places the patterns of items inside the list of patterns. The efficiency parameter ensures that we don't output any which use less than "efficiency" percent /// off the weightlimit. /// </summary> /// <param name="maxAnswers">maximum number of combinations to get out. Limits runtime. If zero return all.</param> /// <param name="weights">weight of each item to go into the knapsack</param> /// <param name="weightLimit">knapsack weight limit</param> /// <param name="efficiency">limit patterns to use at least this % of the weight limit (between 0.0 and 1.0) </param> /// <param name="patterns">output list of patterns of inclusion of the weights.</param> public static void SolveKnapsack(int maxAnswers, int[] weights, int weightLimit, double efficiency, out List <int[]> patterns) { // convenience value. int NumItems = weights.Length; ConstraintSystem solver = ConstraintSystem.CreateSolver(); CspDomain dom = solver.CreateIntegerInterval(0, weightLimit); CspTerm knapsackSize = solver.Constant(weightLimit); // these represent the quantity of each item. CspTerm[] itemQty = solver.CreateVariableVector(dom, "Quantity", NumItems); CspTerm[] itemWeights = new CspTerm[NumItems]; for (int cnt = 0; cnt < NumItems; cnt++) { itemWeights[cnt] = solver.Constant(weights[cnt]); } // contributors to the weight (weight * variable value) CspTerm[] contributors = new CspTerm[NumItems]; for (int cnt = 0; cnt < NumItems; cnt++) { contributors[cnt] = itemWeights[cnt] * itemQty[cnt]; } // single constraint CspTerm knapSackCapacity = solver.GreaterEqual(knapsackSize, solver.Sum(contributors)); solver.AddConstraints(knapSackCapacity); // must be efficient CspTerm knapSackAtLeast = solver.LessEqual(knapsackSize * efficiency, solver.Sum(contributors)); solver.AddConstraints(knapSackAtLeast); // start counter and allocate a list for the results. int nanswers = 0; patterns = new List <int[]>(); ConstraintSolverSolution sol = solver.Solve(); while (sol.HasFoundSolution) { int[] pattern = new int[NumItems]; // extract this pattern from the enumeration. for (int cnt = 0; cnt < NumItems; cnt++) { object val; sol.TryGetValue(itemQty[cnt], out val); pattern[cnt] = (int)val; } // add it to the output. patterns.Add(pattern); nanswers++; // stop if we reach the limit of results. if (maxAnswers > 0 && nanswers >= maxAnswers) { break; } sol.GetNext(); } }
public override void AddConstaint() { ConstraintSystem solver = ConstraintSystemSolver.Instance.Solver; CspTerm constraint = null; CspTerm[] inputTerms = new CspTerm[Input.Count]; for (int i = 0; i < Input.Count; i++) { inputTerms[i] = Input[i].CspTerm; } CspTerm outputTerm = Output.CspTerm; Type consType = type; if (IsNotHealthy) { // In case the gate is Broken (Not Healthy) - we don't want to add any constraint!!! return; /* * switch (type) * { * case Type.and: * consType = Type.nand; * break; * case Type.nand: * consType = Type.and; * break; * case Type.or: * consType = Type.nor; * break; * case Type.nor: * consType = Type.or; * break; * case Type.xor: * consType = Type.nxor; * break; * case Type.nxor: * consType = Type.xor; * break; * } */ } lock (ConstraintSystemSolver.Instance.Locker) { //Debug.WriteLine("SAT IN!"); switch (consType) { case Type.and: CspTerm allAndInputs = solver.And(inputTerms); constraint = solver.Equal(allAndInputs, outputTerm); break; case Type.nand: CspTerm allNandInputs = solver.And(inputTerms); constraint = solver.Equal(allNandInputs, solver.Not(outputTerm)); break; case Type.nor: CspTerm allNorInputs = solver.Or(inputTerms); constraint = solver.Equal(allNorInputs, solver.Not(outputTerm)); break; case Type.or: CspTerm allOrInputs = solver.Or(inputTerms); constraint = solver.Equal(allOrInputs, outputTerm); break; case Type.xor: //XOR is also: //http://en.wikipedia.org/wiki/XOR_gate#/media/File:254px_3gate_XOR.jpg CspTerm firstNand = solver.Not(solver.And(inputTerms)); CspTerm firstOr = solver.Or(inputTerms); CspTerm secendAnd = solver.And(firstNand, firstOr); constraint = solver.Equal(secendAnd, outputTerm); break; case Type.nxor: //XOR is also: //http://en.wikipedia.org/wiki/XOR_gate#/media/File:254px_3gate_XOR.jpg CspTerm firstNand2 = solver.Not(solver.And(inputTerms)); CspTerm firstOr2 = solver.Or(inputTerms); CspTerm secendAnd2 = solver.And(firstNand2, firstOr2); constraint = solver.Equal(secendAnd2, solver.Not(outputTerm)); break; } solver.AddConstraints(constraint); //Debug.WriteLine("SAT OUT!"); } }
/// <summary> /// Generates a constraint system based on a variability model. The constraint system can be used to check for satisfiability of configurations as well as optimization. /// </summary> /// <param name="variables">Empty input, outputs a list of CSP terms that correspond to the configuration options of the variability model</param> /// <param name="optionToTerm">A map to get for a given configuration option the corresponding CSP term of the constraint system</param> /// <param name="termToOption">A map that gives for a given CSP term the corresponding configuration option of the variability model</param> /// <param name="vm">The variability model for which we generate a constraint system</param> /// <returns>The generated constraint system consisting of logical terms representing configuration options as well as their boolean constraints.</returns> internal static ConstraintSystem getConstraintSystem(out List <CspTerm> variables, out Dictionary <BinaryOption, CspTerm> optionToTerm, out Dictionary <CspTerm, BinaryOption> termToOption, VariabilityModel vm) { //Reusing seems to not work correctely. The problem: configurations are realized as additional constraints for the system. //however, when checking for the next config, the old config's constraints remain in the solver such that we have a wrong result. /* * if (csystem != null && variables_global != null && optionToTerm_global != null && termToOption_global != null && vm != null) * {//For optimization purpose * if (vm.BinaryOptions.Count == vm_global.BinaryOptions.Count && vm.Name.Equals(vm_global.Name)) * { * variables = variables_global; * optionToTerm = optionToTerm_global; * termToOption = termToOption_global; * return csystem; * } * }*/ ConstraintSystem S = ConstraintSystem.CreateSolver(); optionToTerm = new Dictionary <BinaryOption, CspTerm>(); termToOption = new Dictionary <CspTerm, BinaryOption>(); variables = new List <CspTerm>(); foreach (BinaryOption binOpt in vm.BinaryOptions) { CspDomain domain = S.DefaultBoolean; CspTerm temp = S.CreateVariable(domain, binOpt); optionToTerm.Add(binOpt, temp); termToOption.Add(temp, binOpt); variables.Add(temp); } List <List <ConfigurationOption> > alreadyHandledAlternativeOptions = new List <List <ConfigurationOption> >(); //Constraints of a single configuration option foreach (BinaryOption current in vm.BinaryOptions) { CspTerm cT = optionToTerm[current]; if (current.Parent == null || current.Parent == vm.Root) { if (current.Optional == false && current.Excluded_Options.Count == 0) { S.AddConstraints(S.Implies(S.True, cT)); } else { S.AddConstraints(S.Implies(cT, optionToTerm[vm.Root])); } } if (current.Parent != null && current.Parent != vm.Root) { CspTerm parent = optionToTerm[(BinaryOption)current.Parent]; S.AddConstraints(S.Implies(cT, parent)); if (current.Optional == false && current.Excluded_Options.Count == 0) { S.AddConstraints(S.Implies(parent, cT));//mandatory child relationship } } //Alternative or other exclusion constraints if (current.Excluded_Options.Count > 0) { List <ConfigurationOption> alternativeOptions = current.collectAlternativeOptions(); if (alternativeOptions.Count > 0) { //Check whether we handled this group of alternatives already foreach (var alternativeGroup in alreadyHandledAlternativeOptions) { foreach (var alternative in alternativeGroup) { if (current == alternative) { goto handledAlternative; } } } //It is not allowed that an alternative group has no parent element CspTerm parent = null; if (current.Parent == null) { parent = S.True; } else { parent = optionToTerm[(BinaryOption)current.Parent]; } CspTerm[] terms = new CspTerm[alternativeOptions.Count + 1]; terms[0] = cT; int i = 1; foreach (BinaryOption altEle in alternativeOptions) { CspTerm temp = optionToTerm[altEle]; terms[i] = temp; i++; } S.AddConstraints(S.Implies(parent, S.ExactlyMofN(1, terms))); alreadyHandledAlternativeOptions.Add(alternativeOptions); handledAlternative : { } } //Excluded option(s) as cross-tree constraint(s) List <List <ConfigurationOption> > nonAlternative = current.getNonAlternativeExlcudedOptions(); if (nonAlternative.Count > 0) { foreach (var excludedOption in nonAlternative) { CspTerm[] orTerm = new CspTerm[excludedOption.Count]; int i = 0; foreach (var opt in excludedOption) { CspTerm target = optionToTerm[(BinaryOption)opt]; orTerm[i] = target; i++; } S.AddConstraints(S.Implies(cT, S.Not(S.Or(orTerm)))); } } } //Handle implies if (current.Implied_Options.Count > 0) { foreach (List <ConfigurationOption> impliedOr in current.Implied_Options) { CspTerm[] orTerms = new CspTerm[impliedOr.Count]; //Possible error: if a binary option impies a numeric option for (int i = 0; i < impliedOr.Count; i++) { orTerms[i] = optionToTerm[(BinaryOption)impliedOr.ElementAt(i)]; } S.AddConstraints(S.Implies(optionToTerm[current], S.Or(orTerms))); } } } //Handle global cross-tree constraints involving multiple options at a time // the constraints should be in conjunctive normal form foreach (string constraint in vm.BinaryConstraints) { bool and = false; string[] terms; if (constraint.Contains("&")) { and = true; terms = constraint.Split('&'); } else { terms = constraint.Split('|'); } CspTerm[] cspTerms = new CspTerm[terms.Count()]; int i = 0; foreach (string t in terms) { string optName = t.Trim(); if (optName.StartsWith("-") || optName.StartsWith("!")) { optName = optName.Substring(1); BinaryOption binOpt = vm.getBinaryOption(optName); CspTerm cspElem = optionToTerm[binOpt]; CspTerm notCspElem = S.Not(cspElem); cspTerms[i] = notCspElem; } else { BinaryOption binOpt = vm.getBinaryOption(optName); CspTerm cspElem = optionToTerm[binOpt]; cspTerms[i] = cspElem; } i++; } if (and) { S.AddConstraints(S.And(cspTerms)); } else { S.AddConstraints(S.Or(cspTerms)); } } csystem = S; optionToTerm_global = optionToTerm; vm_global = vm; termToOption_global = termToOption; variables_global = variables; return(S); }