/// <summary>
/// Creates a variable with evenly sized and spaced trapezoidal functions.
/// </summary>
/// <param name="varName">The name of this variable.</param>
/// <param name="min">Minimum value.</param>
/// <param name="max">Maxiumum value.</param>
/// <param name="termNames">The terms of this variable.</param>
/// <returns>Symmetric variable.</returns>
public static FuzzyVariable CreateSymmetricTrapezoidalVariable(string varName, double min, double max, params string[] termNames)
{
FuzzyVariable fuzzyVar = new FuzzyVariable(varName, min, max);
int count = 0;
double spread = (max - min) / ((termNames.Count() * 2) - 1);
foreach (string term in termNames)
{
if(count == 0)
{
fuzzyVar.Terms.Add(new FuzzyTerm(term, new TrapezoidMembershipFunction(spread * count, spread * count, spread * ++count, spread * ++count)));
}
else if(count == (termNames.Count() * 2) - 2)
{
fuzzyVar.Terms.Add(new FuzzyTerm(term, new TrapezoidMembershipFunction(spread * --count, spread * ++count, spread * ++count, spread * count)));
}
else
{
fuzzyVar.Terms.Add(new FuzzyTerm(term, new TrapezoidMembershipFunction(spread * --count, spread * ++count, spread * ++count, spread * ++count)));
}
}
return fuzzyVar;
}
/// <summary>
/// Get coefficient by fuzzy variable
/// </summary>
/// <param name="var">Fuzzy variable</param>
/// <returns>Coefficient's value</returns>
public double GetCoefficient(FuzzyVariable var)
{
if (var == null)
{
return(_constValue);
}
else
{
return(_coeffs[var]);
}
}
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);
}
}
}
/// <summary>
/// Set coefficient by fuzzy variable
/// </summary>
/// <param name="var">Fuzzy variable</param>
/// <param name="coeff">New value of the coefficient</param>
public void SetCoefficient(FuzzyVariable var, double coeff)
{
if (var == null)
{
_constValue = coeff;
}
else
{
_coeffs[var] = coeff;
}
}
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;
}
/// <summary>
/// Creates a variable with evenly sized and spaced triangular functions.
/// </summary>
/// <param name="varName">The name of this variable.</param>
/// <param name="min">Minimum value.</param>
/// <param name="max">Maxiumum value.</param>
/// <param name="termNames">The terms of this variable.</param>
/// <returns>Symmetric variable.</returns>
public static FuzzyVariable CreateSymmetricTriangularVariable(string varName, double min, double max, params string[] termNames)
{
FuzzyVariable fuzzyVar = new FuzzyVariable(varName, min, max);
int count = 0;
double spread = (max - min) / (termNames.Count() - 1);
foreach (string term in termNames)
{
fuzzyVar.Terms.Add(new FuzzyTerm(term, new TriangularMembershipFunction(spread * --count, spread * ++count, spread * ++count)));
}
return fuzzyVar;
}
public static FuzzyVariable GetOutputVariable()
{
XmlSerializer serializer = new XmlSerializer(typeof(ParamsList));
var fs = new FileStream(VARIABLES_DATA, FileMode.Open);
var parametersList = (ParamsList)serializer.Deserialize(fs);
var parameters =
(from item in parametersList.Items where item.Type == ParameterType.Output select item).ToList();
fs.Close();
if (parameters.Count > 0)
{
FuzzyVariable fsVariable = new FuzzyVariable(
parameters[0].ParamName,
parameters[0].Min,
parameters[0].Max);
foreach (Term term in parameters[0].Terms)
{
fsVariable.Terms.Add(new FuzzyTerm(term.Name, new NormalMembershipFunction(term.b, term.c)));
}
return fsVariable;
}
return null;
}
public static List<FuzzyVariable> GetInputVariables(int stage = 3)
{
List<FuzzyVariable> variables = new List<FuzzyVariable>();
XmlSerializer serializer = new XmlSerializer(typeof(ParamsList));
var fs = new FileStream(VARIABLES_DATA, FileMode.Open);
var parametersList = (ParamsList)serializer.Deserialize(fs);
var parameters = (from item in parametersList.Items
where item.Type == ParameterType.Input && item.Stage <= stage
select item)
.ToList();
fs.Close();
// Create input and output variables for the system
foreach (var inputVariable in parameters)
{
FuzzyVariable fsVariable = new FuzzyVariable(
inputVariable.ParamName,
inputVariable.Min,
inputVariable.Max);
foreach (Term term in inputVariable.Terms)
{
fsVariable.Terms.Add(new FuzzyTerm(term.Name, new NormalMembershipFunction(term.b, term.c)));
}
variables.Add(fsVariable);
}
return variables;
}
void AddSugenoFuzzyRule(
SugenoFuzzySystem fs,
FuzzyVariable fv1,
FuzzyVariable fv2,
SugenoVariable sv,
string value1,
string value2,
string result)
{
SugenoFuzzyRule rule = fs.EmptyRule();
rule.Condition.Op = OperatorType.And;
rule.Condition.ConditionsList.Add(rule.CreateCondition(fv1, fv1.GetTermByName(value1)));
rule.Condition.ConditionsList.Add(rule.CreateCondition(fv2, fv2.GetTermByName(value2)));
rule.Conclusion.Var = sv;
rule.Conclusion.Term = sv.GetFuncByName(result);
fs.Rules.Add(rule);
}
SugenoFuzzySystem CreateSystem()
{
//
// Create empty Sugeno Fuzzy System
//
SugenoFuzzySystem fsCruiseControl = new SugenoFuzzySystem();
//
// Create input variables for the system
//
FuzzyVariable fvSpeedError = new FuzzyVariable("SpeedError", -20.0, 20.0);
fvSpeedError.Terms.Add(new FuzzyTerm("slower", new TriangularMembershipFunction(-35.0, -20.0, -5.0)));
fvSpeedError.Terms.Add(new FuzzyTerm("zero", new TriangularMembershipFunction(-15.0, -0.0, 15.0)));
fvSpeedError.Terms.Add(new FuzzyTerm("faster", new TriangularMembershipFunction(5.0, 20.0, 35.0)));
fsCruiseControl.Input.Add(fvSpeedError);
FuzzyVariable fvSpeedErrorDot = new FuzzyVariable("SpeedErrorDot", -5.0, 5.0);
fvSpeedErrorDot.Terms.Add(new FuzzyTerm("slower", new TriangularMembershipFunction(-9.0, -5.0, -1.0)));
fvSpeedErrorDot.Terms.Add(new FuzzyTerm("zero", new TriangularMembershipFunction(-4.0, -0.0, 4.0)));
fvSpeedErrorDot.Terms.Add(new FuzzyTerm("faster", new TriangularMembershipFunction(1.0, 5.0, 9.0)));
fsCruiseControl.Input.Add(fvSpeedErrorDot);
//
// Create output variables for the system
//
SugenoVariable svAccelerate = new SugenoVariable("Accelerate");
svAccelerate.Functions.Add(fsCruiseControl.CreateSugenoFunction("zero", new double[] { 0.0, 0.0, 0.0 }));
svAccelerate.Functions.Add(fsCruiseControl.CreateSugenoFunction("faster", new double[] { 0.0, 0.0, 1.0 }));
svAccelerate.Functions.Add(fsCruiseControl.CreateSugenoFunction("slower", new double[] { 0.0, 0.0, -1.0 }));
svAccelerate.Functions.Add(fsCruiseControl.CreateSugenoFunction("func", new double[] { -0.04, -0.1, 0.0 }));
fsCruiseControl.Output.Add(svAccelerate);
//
// Create fuzzy rules
//
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "slower", "slower", "faster");
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "slower", "zero", "faster");
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "slower", "faster", "zero");
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "zero", "slower", "faster");
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "zero", "zero", "func");
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "zero", "faster", "slower");
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "faster", "slower", "zero");
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "faster", "zero", "slower");
AddSugenoFuzzyRule(fsCruiseControl, fvSpeedError, fvSpeedErrorDot, svAccelerate, "faster", "faster", "slower");
//
// Adding the same rules in text form
//
///////////////////////////////////////////////////////////////////
//SugenoFuzzyRule rule1 = fsCruiseControl.ParseRule("if (SpeedError is slower) and (SpeedErrorDot is slower) then (Accelerate is faster)");
//SugenoFuzzyRule rule2 = fsCruiseControl.ParseRule("if (SpeedError is slower) and (SpeedErrorDot is zero) then (Accelerate is faster)");
//SugenoFuzzyRule rule3 = fsCruiseControl.ParseRule("if (SpeedError is slower) and (SpeedErrorDot is faster) then (Accelerate is zero)");
//SugenoFuzzyRule rule4 = fsCruiseControl.ParseRule("if (SpeedError is zero) and (SpeedErrorDot is slower) then (Accelerate is faster)");
//SugenoFuzzyRule rule5 = fsCruiseControl.ParseRule("if (SpeedError is zero) and (SpeedErrorDot is zero) then (Accelerate is func)");
//SugenoFuzzyRule rule6 = fsCruiseControl.ParseRule("if (SpeedError is zero) and (SpeedErrorDot is faster) then (Accelerate is slower)");
//SugenoFuzzyRule rule7 = fsCruiseControl.ParseRule("if (SpeedError is faster) and (SpeedErrorDot is slower) then (Accelerate is faster)");
//SugenoFuzzyRule rule8 = fsCruiseControl.ParseRule("if (SpeedError is faster) and (SpeedErrorDot is zero) then (Accelerate is slower)");
//SugenoFuzzyRule rule9 = fsCruiseControl.ParseRule("if (SpeedError is faster) and (SpeedErrorDot is faster) then (Accelerate is slower)");
//fsCruiseControl.Rules.Add(rule1);
//fsCruiseControl.Rules.Add(rule2);
//fsCruiseControl.Rules.Add(rule3);
//fsCruiseControl.Rules.Add(rule4);
//fsCruiseControl.Rules.Add(rule5);
//fsCruiseControl.Rules.Add(rule6);
//fsCruiseControl.Rules.Add(rule7);
//fsCruiseControl.Rules.Add(rule8);
//fsCruiseControl.Rules.Add(rule9);
///////////////////////////////////////////////////////////////////
return fsCruiseControl;
}
/// <summary>
/// Create a single condition
/// </summary>
/// <param name="var">A linguistic variable to which the condition is related</param>
/// <param name="term">A term in expression 'var is term'</param>
/// <param name="not">Does condition contain 'not'</param>
/// <param name="hedge">Hedge modifier</param>
/// <returns>Generated condition</returns>
public FuzzyCondition CreateCondition(FuzzyVariable var, FuzzyTerm term, bool not, HedgeType hedge)
{
return(new FuzzyCondition(var, term, not, hedge));
}
/// <summary>
/// Create a single condition
/// </summary>
/// <param name="var">A linguistic variable to which the condition is related</param>
/// <param name="term">A term in expression 'var is term'</param>
/// <param name="not">Does condition contain 'not'</param>
/// <returns>Generated condition</returns>
public FuzzyCondition CreateCondition(FuzzyVariable var, FuzzyTerm term, bool not)
{
return(new FuzzyCondition(var, term, not));
}
private List<FuzzyVariable> GetInputVariables(FuzzyCheckDBContext context)
{
List<FuzzyVariable> variables = new List<FuzzyVariable>();
foreach (var inputVariable in context.Parameters)
{
if (inputVariable.IsInput)
{
FuzzyVariable fsVariable = new FuzzyVariable(
inputVariable.Name,
inputVariable.Min,
inputVariable.Max);
variables.Add(fsVariable);
}
}
foreach (var variable in variables)
{
var dbVariable = context.Parameters.Single(p => p.Name == variable.Name);
foreach (var term in dbVariable.Terms)
{
variable.Terms.Add(new FuzzyTerm(term.Name, new NormalMembershipFunction(term.b, term.c)));
}
}
return variables;
}
private double PreprocessInputValue(FuzzyVariable variable, double value)
{
if (value <= variable.Min)
{
return variable.Min;
}
if (value >= variable.Max)
{
return variable.Max;
}
return value;
}
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;
}
private static Bitmap GetTermImg(FuzzyVariable fv)
{
Bitmap bmp = new Bitmap(400, 200);
if (fv != null)
{
RelationImage img = new RelationImage(fv);
img.DrawImage(Graphics.FromImage(bmp));
}
return bmp;
}
private FuzzyVariable GetOutputVariable()
{
var parameters = (from item in db.Parameters where !item.IsInput select item).ToList();
List<FuzzyVariable> variables = new List<FuzzyVariable>();
foreach (var variable in parameters)
{
FuzzyVariable fsVariable = new FuzzyVariable(
variable.Name,
variable.Min,
variable.Max);
foreach (var term in variable.Terms)
{
fsVariable.Terms.Add(new FuzzyTerm(term.Name, new NormalMembershipFunction(term.b, term.c)));
}
return fsVariable;
}
return null;
}
private List<FuzzyVariable> GetInputVariables()
{
List<FuzzyVariable> variables = new List<FuzzyVariable>();
foreach (var inputVariable in db.Parameters)
{
if (inputVariable.IsInput /* &¶meterValues.Any(pv => pv.ID == inputVariable.Id)*/)
{
FuzzyVariable fsVariable = new FuzzyVariable(
inputVariable.Name,
inputVariable.Min,
inputVariable.Max);
variables.Add(fsVariable);
}
}
foreach (var variable in variables)
{
var dbVariable = db.Parameters.Single(p => p.Name == variable.Name);
foreach (var term in dbVariable.Terms)
{
variable.Terms.Add(new FuzzyTerm(term.Name, new NormalMembershipFunction(term.b, term.c)));
}
}
return variables;
}
string DefuzzifyToClosestTerm(FuzzyVariable outputVariable, IMembershipFunction compositeMF, double min, double max)
{
int k = 50;
double step = (max - min) / k;
double maxArea = 0;
string matchingTerm = outputVariable.Terms[0].Name;
foreach (var term in outputVariable.Terms)
{
double coverageArea = 0;
double leftX = min;
double rightX = leftX + step;
while (rightX <= max)
{
coverageArea += step * (Math.Min(term.MembershipFunction.GetValue(leftX), compositeMF.GetValue(leftX)));
leftX += step;
rightX += step;
}
if (coverageArea > maxArea)
{
maxArea = coverageArea;
matchingTerm = term.Name;
}
}
return matchingTerm;
}
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;
}
/// <summary>
/// Creates new instance of image just to display a single-dimensional fuzzy set without any values specified.
/// </summary>
/// <param name="singleDimensionalSet">Single-dimensional set to display</param>
public RelationImage(FuzzyVariable singleDimensionalSet)
{
_variable = singleDimensionalSet;
_fullySpecified = false;
_specifiedMembership = null;
}
public DecisionMakerType2(string stretchedParametersXml, FuzzyVariable outputVariable, RulesList ruleDefinitions, List<FuzzyVariable> includedVariables)
{
XmlSerializer serializer = new XmlSerializer(typeof(Type2ParamsList));
XmlReaderSettings settings = new XmlReaderSettings();
using (StringReader textReader = new StringReader(stretchedParametersXml))
{
using (XmlReader xmlReader = XmlReader.Create(textReader, settings))
{
Type2ParamsList deserializedT2Params = (Type2ParamsList)serializer.Deserialize(xmlReader);
inputVariables = new List<Type2FuzzyVariable>();
foreach (var variable in deserializedT2Params.Items)
{
if (includedVariables.Exists(v => v.Name == variable.ParamName))
{
Type2FuzzyVariable newVariable = new Type2FuzzyVariable(
variable.ParamName,
variable.Min,
variable.Max,
variable.Unit);
foreach (var term in variable.Terms)
{
Type2FuzzyTerm newTerm = new Type2FuzzyTerm(
term.name,
new NormalMembershipFunction(term.b.Upper, term.c.Upper),
new NormalMembershipFunction(term.b.Lower, term.c.Lower));
newVariable.Terms.Add(newTerm);
}
inputVariables.Add(newVariable);
}
}
// output variable
this.outputVariable = new Type2FuzzyVariable(
outputVariable.Name,
outputVariable.Min,
outputVariable.Max,
outputVariable.Unit);
foreach (var term in outputVariable.Terms)
{
Type2FuzzyTerm newTerm = new Type2FuzzyTerm(
term.Name,
new NormalMembershipFunction(
(term.MembershipFunction as NormalMembershipFunction).B,
(term.MembershipFunction as NormalMembershipFunction).Sigma),
new NormalMembershipFunction(
(term.MembershipFunction as NormalMembershipFunction).B,
(term.MembershipFunction as NormalMembershipFunction).Sigma));
this.outputVariable.Terms.Add(newTerm);
}
// rules
rules = new List<Type2MamdaniFuzzyRule>();
var updatedDefinitions = new List<RuleDef>();
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.Exists(v => v.Name == variable))
{
string keyword = updatedDef == string.Empty ? "if" : "and";
updatedDef += string.Format("{0} ({1}) ", keyword, trimmedCondition);
}
}
}
if (!updatedDefinitions.Exists(r => r.Definition.Equals(updatedDef)))
{
updatedDefinitions.Add(new RuleDef { Definition = updatedDef, Weight = rule.Weight });
Type2MamdaniFuzzyRule newRule = ParseRule(updatedDef);
newRule.Weight = rule.Weight;
rules.Add(newRule);
}
}
isInitialized = true;
}
}
}
