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);
}
}
}
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;
}
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;
}