public static MamdaniFuzzySystem CreateMamdaniSystem(List<FuzzyVariable> inputs, List<FuzzyVariable> outputs)
        {
            MamdaniFuzzySystem fuzzySystem = new MamdaniFuzzySystem();

            fuzzySystem.Input.AddRange(inputs);
            fuzzySystem.Output.AddRange(outputs);

            return fuzzySystem;
        }
        public DecisionMakerType1(
            List<FuzzyVariable> inputVariables,
            FuzzyVariable outputVariable,
            RulesList ruleDefinitions,
            List<string> includedVariables = null)
        {
            if (includedVariables == null)
            {
                includedVariables = (from v in inputVariables select v.Name).ToList();
            }

            this.fsWellEval = new MamdaniFuzzySystem();
            this.fsWellEval.Input.AddRange(from v in inputVariables where includedVariables.Contains(v.Name) select v);
            this.fsWellEval.Output.Add(outputVariable);

            this.RulesDefinitions = new RulesList();
            foreach (var rule in ruleDefinitions.Items)
            {
                string[] splitDef = rule.Definition.Split(
                    new[] { "if", "and", "(", ")" },
                    StringSplitOptions.RemoveEmptyEntries);

                string updatedDef = string.Empty;
                foreach (var condition in splitDef)
                {
                    if (condition == string.Empty)
                    {
                        continue;
                    }

                    var trimmedCondition = condition.Trim();
                    if (trimmedCondition.StartsWith("then"))
                    {
                        updatedDef += trimmedCondition;
                    }
                    else
                    {
                        string variable = trimmedCondition.Split(' ')[0];
                        if (includedVariables.Contains(variable))
                        {
                            string keyword = updatedDef == string.Empty ? "if" : "and";
                            updatedDef += string.Format("{0} ({1}) ", keyword, trimmedCondition);
                        }
                    }
                }

                if (!RulesDefinitions.Items.Exists(r => r.Definition.Equals(updatedDef)))
                {
                    this.RulesDefinitions.Items.Add(new RuleDef { Definition = updatedDef, Weight = rule.Weight });
                    MamdaniFuzzyRule newRule = this.fsWellEval.ParseRule(updatedDef);
                    this.fsWellEval.Rules.Add(newRule);
                }
            }
        }
Exemple #3
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        MamdaniFuzzySystem CreateSystem()
        {
            //
            // Create empty fuzzy system
            //
            MamdaniFuzzySystem fsTips = new MamdaniFuzzySystem();

            //
            // Create input variables for the system
            //
            FuzzyVariable fvService = new FuzzyVariable("service", 0.0, 10.0);
            fvService.Terms.Add(new FuzzyTerm("poor", new TriangularMembershipFunction(-5.0, 0.0, 5.0)));
            fvService.Terms.Add(new FuzzyTerm("good", new TriangularMembershipFunction(0.0, 5.0, 10.0)));
            fvService.Terms.Add(new FuzzyTerm("excellent", new TriangularMembershipFunction(5.0, 10.0, 15.0)));
            fsTips.Input.Add(fvService);

            FuzzyVariable fvFood = new FuzzyVariable("food", 0.0, 10.0);
            fvFood.Terms.Add(new FuzzyTerm("rancid", new TrapezoidMembershipFunction(0.0, 0.0, 1.0, 3.0)));
            fvFood.Terms.Add(new FuzzyTerm("delicious", new TrapezoidMembershipFunction(7.0, 9.0, 10.0, 10.0)));
            fsTips.Input.Add(fvFood);

            //
            // Create output variables for the system
            //
            FuzzyVariable fvTips = new FuzzyVariable("tips", 0.0, 30.0);
            fvTips.Terms.Add(new FuzzyTerm("cheap", new TriangularMembershipFunction(0.0, 5.0, 10.0)));
            fvTips.Terms.Add(new FuzzyTerm("average", new TriangularMembershipFunction(10.0, 15.0, 20.0)));
            fvTips.Terms.Add(new FuzzyTerm("generous", new TriangularMembershipFunction(20.0, 25.0, 30.0)));
            fsTips.Output.Add(fvTips);

            //
            // Create three fuzzy rules
            //
            try
            {
                MamdaniFuzzyRule rule1 = fsTips.ParseRule("if (service is poor )  or (food is rancid) then tips is cheap");
                MamdaniFuzzyRule rule2 = fsTips.ParseRule("if ((service is good)) then tips is average");
                MamdaniFuzzyRule rule3 = fsTips.ParseRule("if (service is excellent) or (food is delicious) then (tips is generous)");

                fsTips.Rules.Add(rule1);
                fsTips.Rules.Add(rule2);
                fsTips.Rules.Add(rule3);
            }
            catch (Exception ex)
            {
                MessageBox.Show(string.Format("Parsing exception: {0}", ex.Message));
                return null;
            }

            return fsTips;
        }
Exemple #4
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        private void btnRun_Click(object sender, EventArgs e)
        {
            //
            // Create new fuzzy system
            //
            if (_fsTips == null)
            {
                _fsTips = CreateSystem();
                if (_fsTips == null)
                {
                    return;
                }
            }

            //
            // Get variables from the system (for convinience only)
            //
            FuzzyVariable fvService = _fsTips.InputByName("service");
            FuzzyVariable fvFood = _fsTips.InputByName("food");
            FuzzyVariable fvTips = _fsTips.OutputByName("tips");

            //
            // Associate input values with input variables
            //
            Dictionary<FuzzyVariable, double> inputValues = new Dictionary<FuzzyVariable, double>();
            inputValues.Add(fvService, (double)nudInputService.Value);
            inputValues.Add(fvFood, (double)nudInputFood.Value);

            //
            // Calculate result: one output value for each output variable
            //
            Dictionary<FuzzyVariable, double> result = _fsTips.Calculate(inputValues);

            //
            // Get output value for the 'tips' variable
            //
            tbTips.Text = result[fvTips].ToString("f1");
        }
        public static Dictionary<NodeInstance, double> PrioritizeNodes(List<NodeInstance> nodes)
        {
            MamdaniFuzzySystem fsNodeSys = new MamdaniFuzzySystem();

            FuzzyVariable fvCPU = new FuzzyVariable("cpu", 0.0, 1);
            fvCPU.Terms.Add(new FuzzyTerm("low", new TriangularMembershipFunction(-.50, 0, .50)));
            fvCPU.Terms.Add(new FuzzyTerm("med", new TriangularMembershipFunction(0, .50, 1)));
            fvCPU.Terms.Add(new FuzzyTerm("high", new TriangularMembershipFunction(.50, 1, 1.5)));
            fsNodeSys.Input.Add(fvCPU);

            FuzzyVariable fvBandwidth = new FuzzyVariable("bandwidth", 0.0, 1);
            fvBandwidth.Terms.Add(new FuzzyTerm("low", new TriangularMembershipFunction(-.50, 0, .50)));
            fvBandwidth.Terms.Add(new FuzzyTerm("med", new TriangularMembershipFunction(0, .50, 1)));
            fvBandwidth.Terms.Add(new FuzzyTerm("high", new TriangularMembershipFunction(.50, 1, 1.5)));
            fsNodeSys.Input.Add(fvBandwidth);

            FuzzyVariable fvFreeSpace = new FuzzyVariable("freespace", 0.0, 1);
            fvFreeSpace.Terms.Add(new FuzzyTerm("low", new TriangularMembershipFunction(-.5, 0, .5)));
            fvFreeSpace.Terms.Add(new FuzzyTerm("moderate", new TriangularMembershipFunction(0, .5, 1)));
            fvFreeSpace.Terms.Add(new FuzzyTerm("ample", new TriangularMembershipFunction(.5, 1, 1.5)));
            fsNodeSys.Input.Add(fvFreeSpace);

            //
            // Create output variables for the system
            //
            FuzzyVariable fvRank = new FuzzyVariable("rank", 0, 1);
            fvRank.Terms.Add(new FuzzyTerm("low", new TriangularMembershipFunction(-0.25, 0, 0.25)));
            fvRank.Terms.Add(new FuzzyTerm("med_low", new TriangularMembershipFunction(0, 0.25, 0.50)));
            fvRank.Terms.Add(new FuzzyTerm("med", new TriangularMembershipFunction(0.25, 0.50, 0.75)));
            fvRank.Terms.Add(new FuzzyTerm("med_high", new TriangularMembershipFunction(0.50, 0.75, 1)));
            fvRank.Terms.Add(new FuzzyTerm("high", new TriangularMembershipFunction(0.75, 1, 1.25)));

            fsNodeSys.Output.Add(fvRank);

            MamdaniFuzzyRule rule1 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is high) and (freespace is ample) then rank is med");
            MamdaniFuzzyRule rule2 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is high) and (freespace is moderate) then rank is med_low");
            MamdaniFuzzyRule rule3 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is high) and (freespace is low) then rank is low");
            MamdaniFuzzyRule rule4 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is med) and (freespace is ample) then rank is med_high");
            MamdaniFuzzyRule rule5 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is med) and (freespace is moderate) then rank is med_high");
            MamdaniFuzzyRule rule6 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is med) and (freespace is low) then rank is med_low");
            MamdaniFuzzyRule rule7 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is low) and (freespace is ample) then rank is high");
            MamdaniFuzzyRule rule8 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is low) and (freespace is moderate) then rank is med_high");
            MamdaniFuzzyRule rule9 = fsNodeSys.ParseRule("if (cpu is low) and (bandwidth is low) and (freespace is low) then rank is med_low");
            MamdaniFuzzyRule rule10 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is high) and (freespace is ample) then rank is med");
            MamdaniFuzzyRule rule11 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is high) and (freespace is moderate) then rank is med_low");
            MamdaniFuzzyRule rule12 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is high) and (freespace is low) then rank is med_low");
            MamdaniFuzzyRule rule13 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is med) and (freespace is ample) then rank is med");
            MamdaniFuzzyRule rule14 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is med) and (freespace is moderate) then rank is med");
            MamdaniFuzzyRule rule15 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is med) and (freespace is low) then rank is low");
            MamdaniFuzzyRule rule16 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is low) and (freespace is ample) then rank is med_high");
            MamdaniFuzzyRule rule17 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is low) and (freespace is moderate) then rank is med_high");
            MamdaniFuzzyRule rule18 = fsNodeSys.ParseRule("if (cpu is med) and (bandwidth is low) and (freespace is low) then rank is low");
            MamdaniFuzzyRule rule19 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is high) and (freespace is ample) then rank is med");
            MamdaniFuzzyRule rule20 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is high) and (freespace is moderate) then rank is med_low");
            MamdaniFuzzyRule rule21 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is high) and (freespace is low) then rank is low");
            MamdaniFuzzyRule rule22 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is med) and (freespace is ample) then rank is med");
            MamdaniFuzzyRule rule23 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is med) and (freespace is moderate) then rank is med_low");
            MamdaniFuzzyRule rule24 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is med) and (freespace is low) then rank is low");
            MamdaniFuzzyRule rule25 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is low) and (freespace is ample) then rank is med_low");
            MamdaniFuzzyRule rule26 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is low) and (freespace is moderate) then rank is med");
            MamdaniFuzzyRule rule27 = fsNodeSys.ParseRule("if (cpu is high) and (bandwidth is low) and (freespace is low) then rank is low");

            fsNodeSys.Rules.Add(rule1);
            fsNodeSys.Rules.Add(rule2);
            fsNodeSys.Rules.Add(rule3);
            fsNodeSys.Rules.Add(rule4);
            fsNodeSys.Rules.Add(rule5);
            fsNodeSys.Rules.Add(rule6);
            fsNodeSys.Rules.Add(rule7);
            fsNodeSys.Rules.Add(rule8);
            fsNodeSys.Rules.Add(rule9);
            fsNodeSys.Rules.Add(rule10);
            fsNodeSys.Rules.Add(rule11);
            fsNodeSys.Rules.Add(rule12);
            fsNodeSys.Rules.Add(rule13);
            fsNodeSys.Rules.Add(rule14);
            fsNodeSys.Rules.Add(rule15);
            fsNodeSys.Rules.Add(rule16);
            fsNodeSys.Rules.Add(rule17);
            fsNodeSys.Rules.Add(rule18);
            fsNodeSys.Rules.Add(rule19);
            fsNodeSys.Rules.Add(rule20);
            fsNodeSys.Rules.Add(rule21);
            fsNodeSys.Rules.Add(rule22);
            fsNodeSys.Rules.Add(rule23);
            fsNodeSys.Rules.Add(rule24);
            fsNodeSys.Rules.Add(rule25);
            fsNodeSys.Rules.Add(rule26);
            fsNodeSys.Rules.Add(rule27);

            var rankedNodes = new Dictionary<NodeInstance, double>();

            for (int i = 0; i < nodes.Count; i++)
            {
                //
                // Fuzzify input values
                //
                Dictionary<FuzzyVariable, double> inputValues = new Dictionary<FuzzyVariable, double>();
                inputValues.Add(fvCPU, nodes[i].CPU_Utilization);
                inputValues.Add(fvBandwidth, nodes[i].UsedBandwidth / nodes[i].MaxBandwidth);
                inputValues.Add(fvFreeSpace, nodes[i].FreeSpace / nodes[i].MaxBackupSpace);

                //
                // Calculate the result
                //
                Dictionary<FuzzyVariable, double> result = fsNodeSys.Calculate(inputValues);

                double rank = result[fvRank];
                rankedNodes.Add(nodes[i], rank);

                //Console.WriteLine(nodes[i].ToString());
                //Console.WriteLine("Rank: " + Math.Round(rank, 2).ToString());
                //Console.WriteLine();
            }
            var sortedNodes
                = (from entry in rankedNodes orderby entry.Value descending select entry)
                .ToDictionary(pair => pair.Key, pair => pair.Value);
            return sortedNodes;
        }
        public static double GetResult(MamdaniFuzzySystem fuzzySystem, string outputVariable, params KeyValuePair<string, double>[] inputVariables)
        {
            SerializableDictionary<FuzzyVariable, double> inputValues = new SerializableDictionary<FuzzyVariable, double>();

            foreach(KeyValuePair<string, double> entry in inputVariables)
            {
                inputValues.Add(fuzzySystem.InputByName(entry.Key), entry.Value);
            }
            
            Dictionary<FuzzyVariable, double> result = fuzzySystem.Calculate(inputValues);
            
            return result[fuzzySystem.OutputByName(outputVariable)];
        }
        public static MamdaniFuzzySystem AddRules(MamdaniFuzzySystem fuzzySystem, params string[] rules)
        {
            try
            {
                foreach(string rule in rules)
                {
                    fuzzySystem.Rules.Add(FuzzyLogicUtilities.CreateRule(fuzzySystem, rule));
                }

                return fuzzySystem;
            }
            catch
            {
                return null;
            }
        }
 public static MamdaniFuzzyRule CreateRule(MamdaniFuzzySystem fuzzySystem, string rule)
 {
     try
     {
         return fuzzySystem.ParseRule(rule);
     }
     catch
     {
         return null;
     }
 }
 public DecisionMakerType1(MamdaniFuzzySystem fuzzySystem, RulesList rulesDefinitions)
 {
     this.fsWellEval = fuzzySystem;
     this.RulesDefinitions = rulesDefinitions;
 }
        private static MamdaniFuzzySystem UpdateParamsByMultiplier(
            DecisionMakerType1 originalDecisionMaker,
            double multiplier,
            VariableMFParam paramToUpdate,
            out bool canStretchMore)
        {
            canStretchMore = false;

            // Create empty fuzzy system
            MamdaniFuzzySystem newFs = new MamdaniFuzzySystem();

            // Create input and output variables for the system
            foreach (var inputVariable in originalDecisionMaker.fsWellEval.Input)
            {
                FuzzyVariable fsVariable = new FuzzyVariable(inputVariable.Name, inputVariable.Min, inputVariable.Max);
                foreach (var term in inputVariable.Terms)
                {
                    var mf = term.MembershipFunction as NormalMembershipFunction;
                    if (paramToUpdate == VariableMFParam.Mean)
                    {
                        if (mf != null && (mf.B * multiplier >= fsVariable.Min && mf.B * multiplier <= fsVariable.Max))
                        {
                            fsVariable.Terms.Add(
                                new FuzzyTerm(term.Name, new NormalMembershipFunction(mf.B * multiplier, mf.Sigma)));
                            canStretchMore = true;
                        }
                        else
                        {
                            if (mf != null)
                            {
                                fsVariable.Terms.Add(new FuzzyTerm(term.Name, new NormalMembershipFunction(mf.B, mf.Sigma)));
                                canStretchMore = false;
                            }
                        }
                    }

                    if (paramToUpdate == VariableMFParam.Deviation)
                    {
                        if (mf != null && (mf.Sigma * multiplier >= 0.001 && mf.Sigma * multiplier <= 10))
                        {
                            fsVariable.Terms.Add(
                                new FuzzyTerm(term.Name, new NormalMembershipFunction(mf.B, mf.Sigma * multiplier)));

                        }
                        else
                        {
                            if (mf != null)
                            {
                                fsVariable.Terms.Add(new FuzzyTerm(term.Name, new NormalMembershipFunction(mf.B, mf.Sigma)));

                            }
                        }
                    }

                    if (paramToUpdate == VariableMFParam.RuleWeight)
                    {
                        if (mf != null)
                        {
                            fsVariable.Terms.Add(new FuzzyTerm(term.Name, new NormalMembershipFunction(mf.B, mf.Sigma)));
                        }
                    }
                }

                newFs.Input.Add(fsVariable);
            }

            foreach (var outputVariable in originalDecisionMaker.fsWellEval.Output)
            {
                var newVariable = new FuzzyVariable(
                    outputVariable.Name,
                    outputVariable.Min,
                    outputVariable.Max,
                    outputVariable.Unit);
                newVariable.Terms.Clear();
                newVariable.Terms.AddRange(outputVariable.Terms);
                newFs.Output.Add(newVariable);
            }

            for (int i = 0; i < originalDecisionMaker.RulesDefinitions.Items.Count; i++)
            {
                MamdaniFuzzyRule newRule = newFs.ParseRule(originalDecisionMaker.RulesDefinitions.Items[i].Definition);
                double oldWeight = originalDecisionMaker.fsWellEval.Rules[i].Weight;
                if (paramToUpdate == VariableMFParam.RuleWeight)
                {
                    if (oldWeight * multiplier >= 0.001 && oldWeight * multiplier <= 1)
                    {
                        newRule.Weight = oldWeight * multiplier;
                    }
                }
                else
                {
                    newRule.Weight = oldWeight;
                }

                newFs.Rules.Add(newRule);
            }

            return newFs;
        }