/// <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>
/// 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);
}
/* 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>
/// 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);
}
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>
/// 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);
}
public void GetSolutionsAll(ConstraintSolverSolution solution, int maxSolutions)
{
if (solution == null)
{
return;
}
if (maxSolutions < 1)
{
maxSolutions = 1;
}
if (solution.HasFoundSolution)
{
int no = 1;
ShiftsForce = new Dictionary <int, List <Models.ShiftForce> >();
while (solution.HasFoundSolution)
{
if (no > maxSolutions)
{
break;
}
var shiftsForce = GetSolutionResults(solution: solution);
if (shiftsForce != null)
{
ShiftsForce.Add(no, shiftsForce);
}
solution.GetNext();
no++;
}
}
else
{
ShiftsForce = null;
}
}
/// <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);
}
/// <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);
}
/// <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 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);
}
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 static void Run()
{
ConstraintSystem S = ConstraintSystem.CreateSolver();
List <KeyValuePair <CspTerm, string> > termList = new List <KeyValuePair <CspTerm, string> >();
// create a Term between [1..5], associate it with a name for later ease of display
NamedTerm namedTerm = delegate(string name) {
CspTerm x = S.CreateVariable(S.CreateIntegerInterval(1, 5), name);
termList.Add(new KeyValuePair <CspTerm, string>(x, name));
return(x);
};
// the people and attributes will all be matched via the house they reside in.
CspTerm English = namedTerm("English"), Spanish = namedTerm("Spanish"), Japanese = namedTerm("Japanese"), Italian = namedTerm("Italian"), Norwegian = namedTerm("Norwegian");
CspTerm red = namedTerm("red"), green = namedTerm("green"), white = namedTerm("white"), blue = namedTerm("blue"), yellow = namedTerm("yellow");
CspTerm dog = namedTerm("dog"), snails = namedTerm("snails"), fox = namedTerm("fox"), horse = namedTerm("horse"), zebra = namedTerm("zebra");
CspTerm painter = namedTerm("painter"), sculptor = namedTerm("sculptor"), diplomat = namedTerm("diplomat"), violinist = namedTerm("violinist"), doctor = namedTerm("doctor");
CspTerm tea = namedTerm("tea"), coffee = namedTerm("coffee"), milk = namedTerm("milk"), juice = namedTerm("juice"), water = namedTerm("water");
S.AddConstraints(
S.Unequal(English, Spanish, Japanese, Italian, Norwegian),
S.Unequal(red, green, white, blue, yellow),
S.Unequal(dog, snails, fox, horse, zebra),
S.Unequal(painter, sculptor, diplomat, violinist, doctor),
S.Unequal(tea, coffee, milk, juice, water),
S.Equal(English, red),
S.Equal(Spanish, dog),
S.Equal(Japanese, painter),
S.Equal(Italian, tea),
S.Equal(1, Norwegian),
S.Equal(green, coffee),
S.Equal(1, green - white),
S.Equal(sculptor, snails),
S.Equal(diplomat, yellow),
S.Equal(3, milk),
S.Equal(1, S.Abs(Norwegian - blue)),
S.Equal(violinist, juice),
S.Equal(1, S.Abs(fox - doctor)),
S.Equal(1, S.Abs(horse - diplomat))
);
bool unsolved = true;
ConstraintSolverSolution soln = S.Solve();
while (soln.HasFoundSolution)
{
unsolved = false;
System.Console.WriteLine("solved.");
StringBuilder[] houses = new StringBuilder[5];
for (int i = 0; i < 5; i++)
{
houses[i] = new StringBuilder(i.ToString());
}
foreach (KeyValuePair <CspTerm, string> kvp in termList)
{
string item = kvp.Value;
object house;
if (!soln.TryGetValue(kvp.Key, out house))
{
throw new InvalidProgramException("can't find a Term in the solution: " + item);
}
houses[(int)house - 1].Append(", ");
houses[(int)house - 1].Append(item);
}
foreach (StringBuilder house in houses)
{
System.Console.WriteLine(house);
}
soln.GetNext();
}
if (unsolved)
{
System.Console.WriteLine("No solution found.");
}
else
{
System.Console.WriteLine("Solution should have the Norwegian drinking water and the Japanese with the zebra.");
}
}
