// Testing basic funcionality of fuzzy sets
private void runFuzzySetTestButton_Click( object sender, EventArgs e )
{
ClearDataSeries( );
// create 2 fuzzy sets to represent the Cool and Warm temperatures
TrapezoidalFunction function1 = new TrapezoidalFunction( 13, 18, 23, 28 );
FuzzySet fsCool = new FuzzySet( "Cool", function1 );
TrapezoidalFunction function2 = new TrapezoidalFunction( 23, 28, 33, 38 );
FuzzySet fsWarm = new FuzzySet( "Warm", function2 );
// get membership of some points to the cool fuzzy set
double[,] coolValues = new double[20, 2];
for ( int i = 10; i < 30; i++ )
{
coolValues[i - 10, 0] = i;
coolValues[i - 10, 1] = fsCool.GetMembership( i );
}
// getting memberships of some points to the warm fuzzy set
double[,] warmValues = new double[20, 2];
for ( int i = 20; i < 40; i++ )
{
warmValues[i - 20, 0] = i;
warmValues[i - 20, 1] = fsWarm.GetMembership( i );
}
// plot membership to a chart
chart.UpdateDataSeries( "COOL", coolValues );
chart.UpdateDataSeries( "WARM", warmValues );
}
public InferenceSystem SetupInferenceSystem(int width)
{
var mp = new TrapezoidalFunction(centerPoint - width/2, centerPoint, centerPoint + width/2);
var mn = new TrapezoidalFunction(-centerPoint - width/2, -centerPoint, -centerPoint + width/2);
var sp = new TrapezoidalFunction(-centerPoint/2 + width/4, (double) centerPoint/2, centerPoint/2 + width/3);
var sn = new TrapezoidalFunction(-centerPoint/2 - width/3, (double) -centerPoint/2, centerPoint/2 - width/4);
var ze = new TrapezoidalFunction(-(double)centerPoint / 4, 0, (double)centerPoint / 4);
var mpSet = new FuzzySet("MP", mp);
var mnSet = new FuzzySet("MN", mn);
var spSet = new FuzzySet("SP", sp);
var snSet = new FuzzySet("SN", sn);
var zeSet = new FuzzySet("ZE", ze);
var ruleDatabase = new Database();
for (int i = 0; i < windowSize*windowSize - 1; i++)
{
var variable = new LinguisticVariable(String.Format("IN{0}", i), -255, 255);
variable.AddLabel(mpSet);
variable.AddLabel(mnSet);
ruleDatabase.AddVariable(variable);
}
var outVariable = new LinguisticVariable("OUT", -centerPoint - width/2, centerPoint + width/2);
outVariable.AddLabel(spSet);
outVariable.AddLabel(snSet);
outVariable.AddLabel(zeSet);
ruleDatabase.AddVariable(outVariable);
var inferenceSystem = new InferenceSystem(ruleDatabase, new CentroidDefuzzifier(100));
string rule1 = "IF ";
string rule2 = "IF ";
string rule3 = "IF ";
for (int i = 0; i < windowSize*windowSize - 1; i++)
{
rule1 += String.Format("IN{0} is MP and ", i);
rule2 += String.Format("IN{0} is MN and ", i);
rule3 += String.Format("IN{0} is not MP and IN{0} is not MN AND ", i);
}
rule1 = rule1.Remove(rule1.Length - 4, 4);
rule2 = rule2.Remove(rule2.Length - 4, 4);
rule3 = "IF NOT (" + rule1.Replace("IF", "") + ") AND NOT(" + rule2.Replace("IF", "") + ")";
rule1 += " then OUT is SN";
rule2 += " then OUT is SP";
rule3 += " then OUT is ZE";
inferenceSystem.NewRule("Rule1", rule1);
inferenceSystem.NewRule("Rule2", rule2);
inferenceSystem.NewRule("Rule3", rule3);
return inferenceSystem;
}
// Testing basic funcionality of linguistic variables
private void runLingVarTestButton_Click( object sender, EventArgs e )
{
ClearDataSeries( );
// create a linguistic variable to represent temperature
LinguisticVariable lvTemperature = new LinguisticVariable( "Temperature", 0, 80 );
// create the linguistic labels (fuzzy sets) that compose the temperature
TrapezoidalFunction function1 = new TrapezoidalFunction( 10, 15, TrapezoidalFunction.EdgeType.Right );
FuzzySet fsCold = new FuzzySet( "Cold", function1 );
TrapezoidalFunction function2 = new TrapezoidalFunction( 10, 15, 20, 25 );
FuzzySet fsCool = new FuzzySet( "Cool", function2 );
TrapezoidalFunction function3 = new TrapezoidalFunction( 20, 25, 30, 35 );
FuzzySet fsWarm = new FuzzySet( "Warm", function3 );
TrapezoidalFunction function4 = new TrapezoidalFunction( 30, 35, TrapezoidalFunction.EdgeType.Left );
FuzzySet fsHot = new FuzzySet( "Hot", function4 );
// adding labels to the variable
lvTemperature.AddLabel( fsCold );
lvTemperature.AddLabel( fsCool );
lvTemperature.AddLabel( fsWarm );
lvTemperature.AddLabel( fsHot );
// get membership of some points to the cool fuzzy set
double[][,] chartValues = new double[4][,];
for ( int i = 0; i < 4; i++ )
chartValues[i] = new double[160, 2];
// showing the shape of the linguistic variable - the shape of its labels memberships from start to end
int j = 0;
for ( float x = 0; x < 80; x += 0.5f, j++ )
{
double y1 = lvTemperature.GetLabelMembership( "Cold", x );
double y2 = lvTemperature.GetLabelMembership( "Cool", x );
double y3 = lvTemperature.GetLabelMembership( "Warm", x );
double y4 = lvTemperature.GetLabelMembership( "Hot", x );
chartValues[0][j, 0] = x;
chartValues[0][j, 1] = y1;
chartValues[1][j, 0] = x;
chartValues[1][j, 1] = y2;
chartValues[2][j, 0] = x;
chartValues[2][j, 1] = y3;
chartValues[3][j, 0] = x;
chartValues[3][j, 1] = y4;
}
// plot membership to a chart
chart.UpdateDataSeries( "COLD", chartValues[0] );
chart.UpdateDataSeries( "COOL", chartValues[1] );
chart.UpdateDataSeries( "WARM", chartValues[2] );
chart.UpdateDataSeries( "HOT", chartValues[3] );
}
public void SetInput2()
{
this.Power = new LinguisticVariable("Moc_samochodu", 20, 180);
TrapezoidalFunction function1 = new TrapezoidalFunction(40, 100, TrapezoidalFunction.EdgeType.Right);
FuzzySet set1 = new FuzzySet("mała", function1);
TrapezoidalFunction function2 = new TrapezoidalFunction(70, 100, 130);
FuzzySet set2 = new FuzzySet("średnia", function2);
TrapezoidalFunction function3 = new TrapezoidalFunction(100, 160, TrapezoidalFunction.EdgeType.Left);
FuzzySet set3 = new FuzzySet("duża", function3);
Power.AddLabel(set1);
Power.AddLabel(set2);
Power.AddLabel(set3);
database.AddVariable(Power);
double y1;
double y2;
double y3;
for (float x = 20; x < 180; x += 0.5f)
{
if (Power.GetLabelMembership("mała", x + 0.5f) + Power.GetLabelMembership("mała", x) > 0)
{
y1 = Power.GetLabelMembership("mała", x);
chart5.Series["Mała"].Points.AddXY(x, y1);
}
if (Power.GetLabelMembership("średnia", x + 0.5f) + Power.GetLabelMembership("średnia", x) > 0)
{
y2 = Power.GetLabelMembership("średnia", x);
chart5.Series["Średnia"].Points.AddXY(x, y2);
}
if (Power.GetLabelMembership("duża", x + 0.5f) + Power.GetLabelMembership("duża", x) > 0)
{
y3 = Power.GetLabelMembership("duża", x);
chart5.Series["Duża"].Points.AddXY(x, y3);
}
}
}
/// <summary>
/// Adds a linguistic label to the variable.
/// </summary>
///
/// <param name="label">A <see cref="FuzzySet"/> that will be a linguistic label of the linguistic variable.</param>
///
/// <remarks>Linguistic labels are fuzzy sets (<see cref="FuzzySet"/>). Each
/// label of the variable must have a unique name. The range of the label
/// (left and right limits) cannot be greater than
/// the linguistic variable range (start/end).</remarks>
///
/// <exception cref="NullReferenceException">The fuzzy set was not initialized.</exception>
/// <exception cref="ArgumentException">The linguistic label name already exists in the linguistic variable.</exception>
/// <exception cref="ArgumentException">The left limit of the fuzzy set can not be lower than the linguistic variable's starting point.</exception>
/// <exception cref="ArgumentException">"The right limit of the fuzzy set can not be greater than the linguistic variable's ending point."</exception>
///
public void AddLabel(FuzzySet label)
{
// checking for existing name
if (this.labels.ContainsKey(label.Name))
{
throw new ArgumentException("The linguistic label name already exists in the linguistic variable.");
}
// checking ranges
if (label.LeftLimit < this.start)
{
throw new ArgumentException("The left limit of the fuzzy set can not be lower than the linguistic variable's starting point.");
}
if (label.RightLimit > this.end)
{
throw new ArgumentException("The right limit of the fuzzy set can not be greater than the linguistic variable's ending point.");
}
// adding label
this.labels.Add(label.Name, label);
}
public void SetInput1()
{
this.Temp_opon = new LinguisticVariable("Temperatura", 0, 180);
TrapezoidalFunction function1 = new TrapezoidalFunction(30, 90, TrapezoidalFunction.EdgeType.Right);
FuzzySet set1 = new FuzzySet("zimne", function1);
TrapezoidalFunction function2 = new TrapezoidalFunction(70, 90, 110);
FuzzySet set2 = new FuzzySet("średnie", function2);
TrapezoidalFunction function3 = new TrapezoidalFunction(90, 150, TrapezoidalFunction.EdgeType.Left);
FuzzySet set3 = new FuzzySet("gorące", function3);
Temp_opon.AddLabel(set1);
Temp_opon.AddLabel(set2);
Temp_opon.AddLabel(set3);
database.AddVariable(Temp_opon);
double y1;
double y2;
double y3;
for (float x = 0; x < 180; x += 0.5f)
{
if (Temp_opon.GetLabelMembership("zimne", x + 0.5f) + Temp_opon.GetLabelMembership("zimne", x) > 0)
{
y1 = Temp_opon.GetLabelMembership("zimne", x);
chart4.Series["Zimne"].Points.AddXY(x, y1);
}
if (Temp_opon.GetLabelMembership("średnie", x + 0.5f) + Temp_opon.GetLabelMembership("średnie", x) > 0)
{
y2 = Temp_opon.GetLabelMembership("średnie", x);
chart4.Series["Średnie"].Points.AddXY(x, y2);
}
if (Temp_opon.GetLabelMembership("gorące", x + 0.5f) + Temp_opon.GetLabelMembership("gorące", x) > 0)
{
y3 = Temp_opon.GetLabelMembership("gorące", x);
chart4.Series["Gorące"].Points.AddXY(x, y3);
}
}
}
/// <summary>
/// Initializes a new instance of the <see cref="Clause"/> class.
/// </summary>
///
/// <param name="variable">Linguistic variable of the clause. </param>
///
/// <param name="label">Label of the linguistic variable, a fuzzy set used as label into the linguistic variable.</param>
///
/// <exception cref="KeyNotFoundException">The label indicated was not found in the linguistic variable.</exception>
///
public Clause( LinguisticVariable variable, FuzzySet label )
{
// check if label belongs to var.
variable.GetLabel( label.Name );
// initializing attributes
this.label = label;
this.variable = variable;
}
// Hardcode initializing the Fuzzy Inference System
void InitFuzzyEngine( )
{
// Linguistic labels (fuzzy sets) that compose the distances
FuzzySet fsNear = new FuzzySet( "Near", new TrapezoidalFunction( 15, 50, TrapezoidalFunction.EdgeType.Right ) );
FuzzySet fsMedium = new FuzzySet( "Medium", new TrapezoidalFunction( 15, 50, 60, 100 ) );
FuzzySet fsFar = new FuzzySet( "Far", new TrapezoidalFunction( 60, 100, TrapezoidalFunction.EdgeType.Left ) );
// Right Distance (Input)
LinguisticVariable lvRight = new LinguisticVariable( "RightDistance", 0, 120 );
lvRight.AddLabel( fsNear );
lvRight.AddLabel( fsMedium );
lvRight.AddLabel( fsFar );
// Left Distance (Input)
LinguisticVariable lvLeft = new LinguisticVariable( "LeftDistance", 0, 120 );
lvLeft.AddLabel( fsNear );
lvLeft.AddLabel( fsMedium );
lvLeft.AddLabel( fsFar );
// Front Distance (Input)
LinguisticVariable lvFront = new LinguisticVariable( "FrontalDistance", 0, 120 );
lvFront.AddLabel( fsNear );
lvFront.AddLabel( fsMedium );
lvFront.AddLabel( fsFar );
// Linguistic labels (fuzzy sets) that compose the angle
FuzzySet fsVN = new FuzzySet( "VeryNegative", new TrapezoidalFunction( -40, -35, TrapezoidalFunction.EdgeType.Right ) );
FuzzySet fsN = new FuzzySet( "Negative", new TrapezoidalFunction( -40, -35, -25, -20 ) );
FuzzySet fsLN = new FuzzySet( "LittleNegative", new TrapezoidalFunction( -25, -20, -10, -5 ) );
FuzzySet fsZero = new FuzzySet( "Zero", new TrapezoidalFunction( -10, 5, 5, 10 ) );
FuzzySet fsLP = new FuzzySet( "LittlePositive", new TrapezoidalFunction( 5, 10, 20, 25 ) );
FuzzySet fsP = new FuzzySet( "Positive", new TrapezoidalFunction( 20, 25, 35, 40 ) );
FuzzySet fsVP = new FuzzySet( "VeryPositive", new TrapezoidalFunction( 35, 40, TrapezoidalFunction.EdgeType.Left ) );
// Angle
LinguisticVariable lvAngle = new LinguisticVariable( "Angle", -50, 50 );
lvAngle.AddLabel( fsVN );
lvAngle.AddLabel( fsN );
lvAngle.AddLabel( fsLN );
lvAngle.AddLabel( fsZero );
lvAngle.AddLabel( fsLP );
lvAngle.AddLabel( fsP );
lvAngle.AddLabel( fsVP );
// The database
Database fuzzyDB = new Database( );
fuzzyDB.AddVariable( lvFront );
fuzzyDB.AddVariable( lvLeft );
fuzzyDB.AddVariable( lvRight );
fuzzyDB.AddVariable( lvAngle );
// Creating the inference system
IS = new InferenceSystem( fuzzyDB, new CentroidDefuzzifier( 1000 ) );
// Going Straight
IS.NewRule( "Rule 1", "IF FrontalDistance IS Far THEN Angle IS Zero" );
// Going Straight (if can go anywhere)
IS.NewRule( "Rule 2", "IF FrontalDistance IS Far AND RightDistance IS Far AND LeftDistance IS Far THEN Angle IS Zero" );
// Near right wall
IS.NewRule( "Rule 3", "IF RightDistance IS Near AND LeftDistance IS Not Near THEN Angle IS LittleNegative" );
// Near left wall
IS.NewRule("Rule 4", "IF RightDistance IS Not Near AND LeftDistance IS Near THEN Angle IS LittlePositive");
// Near front wall - room at right
IS.NewRule( "Rule 5", "IF RightDistance IS Far AND FrontalDistance IS Near THEN Angle IS Positive" );
// Near front wall - room at left
IS.NewRule( "Rule 6", "IF LeftDistance IS Far AND FrontalDistance IS Near THEN Angle IS Negative" );
// Near front wall - room at both sides - go right
IS.NewRule( "Rule 7", "IF RightDistance IS Far AND LeftDistance IS Far AND FrontalDistance IS Near THEN Angle IS Positive" );
}
/// <summary>
/// Calculate the membership of a given value to a given label. Used to evaluate linguistics clauses like
/// "X IS A", where X is a value and A is a linguistic label.
/// </summary>
///
/// <param name="labelName">Label (fuzzy set) to evaluate value's membership.</param>
/// <param name="value">Value which label's membership will to be calculated.</param>
///
/// <returns>Degree of membership [0..1] of the value to the label (fuzzy set).</returns>
///
/// <exception cref="KeyNotFoundException">The label indicated in labelName was not found in the linguistic variable.</exception>
///
public float GetLabelMembership(string labelName, float value)
{
FuzzySet fs = labels[labelName];
return(fs.GetMembership(value));
}
/// <summary>
/// Adds a linguistic label to the variable.
/// </summary>
///
/// <param name="label">A <see cref="FuzzySet"/> that will be a linguistic label of the linguistic variable.</param>
///
/// <remarks>Linguistic labels are fuzzy sets (<see cref="FuzzySet"/>). Each
/// label of the variable must have a unique name. The range of the label
/// (left and right limits) cannot be greater than
/// the linguistic variable range (start/end).</remarks>
///
/// <exception cref="NullReferenceException">The fuzzy set was not initialized.</exception>
/// <exception cref="ArgumentException">The linguistic label name already exists in the linguistic variable.</exception>
/// <exception cref="ArgumentException">The left limit of the fuzzy set can not be lower than the linguistic variable's starting point.</exception>
/// <exception cref="ArgumentException">"The right limit of the fuzzy set can not be greater than the linguistic variable's ending point."</exception>
///
public void AddLabel( FuzzySet label )
{
// checking for existing name
if ( this.labels.ContainsKey( label.Name ) )
throw new ArgumentException( "The linguistic label name already exists in the linguistic variable." );
// checking ranges
if ( label.LeftLimit < this.start )
throw new ArgumentException( "The left limit of the fuzzy set can not be lower than the linguistic variable's starting point." );
if ( label.RightLimit > this.end )
throw new ArgumentException( "The right limit of the fuzzy set can not be greater than the linguistic variable's ending point." );
// adding label
this.labels.Add( label.Name, label );
}
private InferenceSystem SetupInferenceSystem(byte minLuma, byte maxLuma, byte meanLuma)
{
var lumaIn = new LinguisticVariable("LumaIn", minLuma, maxLuma);
var lumaOut = new LinguisticVariable("LumaOut", 0, 255);
var darkFunction = new TrapezoidalFunction(minLuma, meanLuma, TrapezoidalFunction.EdgeType.Right);
var darkSet = new FuzzySet("Dark", darkFunction);
var mediumFunction = new TrapezoidalFunction(minLuma, meanLuma, maxLuma);
var mediumSet = new FuzzySet("Medium", mediumFunction);
var lightFunction = new TrapezoidalFunction(meanLuma, maxLuma, TrapezoidalFunction.EdgeType.Left);
var lightSet = new FuzzySet("Light", lightFunction);
lumaIn.AddLabel(darkSet);
lumaIn.AddLabel(mediumSet);
lumaIn.AddLabel(lightSet);
var whiteFunction = new SingletonFunction(255);
var whiteSet = new FuzzySet("White", whiteFunction);
var blackFunction = new SingletonFunction(0);
var blackSet = new FuzzySet("Black", blackFunction);
var grayFunction = new SingletonFunction(128);
var graySet = new FuzzySet("Gray", grayFunction);
lumaOut.AddLabel(blackSet);
lumaOut.AddLabel(graySet);
lumaOut.AddLabel(whiteSet);
var database = new Database();
database.AddVariable(lumaIn);
database.AddVariable(lumaOut);
var inferenceSystem = new InferenceSystem(database, new CogDefuzzifier());
inferenceSystem.NewRule("Rule 1", "IF LumaIn IS Dark THEN LumaOut is Black");
inferenceSystem.NewRule("Rule 2", "IF LumaIn IS Medium THEN LumaOut is Gray");
inferenceSystem.NewRule("Rule 3", "IF LumaIn IS Light THEN LumaOut is White");
return inferenceSystem;
}
/// <summary>
/// Converts the Fuzzy Rule to RPN (Reverse Polish Notation). For debug proposes, the string representation of the
/// RPN expression can be acessed by calling <see cref="GetRPNExpression"/> method.
/// </summary>
///
private void ParseRule( )
{
// flag to incicate we are on consequent state
bool consequent = false;
// tokens like IF and THEN will be searched always in upper case
string upRule = rule.ToUpper( );
// the rule must start with IF, and must have a THEN somewhere
if (!upRule.StartsWith("IF"))
{
throw new ArgumentException("A Fuzzy Rule must start with an IF statement.");
}
if (upRule.IndexOf("THEN") < 0)
{
throw new ArgumentException("Missing the consequent (THEN) statement.");
}
// building a list with all the expression (rule) string tokens
string spacedRule = rule.Replace("(", " ( ").Replace(")", " ) ");
// getting the tokens list
string[] tokensList = GetRuleTokens(spacedRule);
// stack to convert to RPN
Stack <string> s = new Stack <string>( );
// storing the last token
string lastToken = "IF";
// linguistic var read, used to build clause
LinguisticVariable lingVar = null;
// verifying each token
for (int i = 0; i < tokensList.Length; i++)
{
// removing spaces
string token = tokensList[i].Trim( );
// getting upper case
string upToken = token.ToUpper( );
// ignoring these tokens
if (upToken == "" || upToken == "IF")
{
continue;
}
// if the THEN is found, the rule is now on consequent
if (upToken == "THEN")
{
lastToken = upToken;
consequent = true;
continue;
}
// if we got a linguistic variable, an IS statement and a label is needed
if (lastToken == "VAR")
{
if (upToken == "IS")
{
lastToken = upToken;
}
else
{
throw new ArgumentException("An IS statement is expected after a linguistic variable.");
}
}
// if we got an IS statement, a label must follow it
else if (lastToken == "IS")
{
try
{
FuzzySet fs = lingVar.GetLabel(token);
Clause c = new Clause(lingVar, fs);
if (consequent)
{
output = c;
}
else
{
rpnTokenList.Add(c);
}
lastToken = "LAB";
}
catch (KeyNotFoundException)
{
throw new ArgumentException("Linguistic label " + token + " was not found on the variable " + lingVar.Name + ".");
}
}
// not VAR and not IS statement
else
{
// openning new scope
if (upToken == "(")
{
// if we are on consequent, only variables can be found
if (consequent)
{
throw new ArgumentException("Linguistic variable expected after a THEN statement.");
}
// if its a (, just push it
s.Push(upToken);
lastToken = upToken;
}
// operators
else if (upToken == "AND" || upToken == "OR" || unaryOperators.IndexOf(upToken) >= 0)
{
// if we are on consequent, only variables can be found
if (consequent)
{
throw new ArgumentException("Linguistic variable expected after a THEN statement.");
}
// pop all the higher priority operators until the stack is empty
while ((s.Count > 0) && (Priority(s.Peek( )) > Priority(upToken)))
{
rpnTokenList.Add(s.Pop( ));
}
// pushing the operator
s.Push(upToken);
lastToken = upToken;
}
// closing the scope
else if (upToken == ")")
{
// if we are on consequent, only variables can be found
if (consequent)
{
throw new ArgumentException("Linguistic variable expected after a THEN statement.");
}
// if there is nothing on the stack, an oppening parenthesis is missing.
if (s.Count == 0)
{
throw new ArgumentException("Openning parenthesis missing.");
}
// pop the tokens and copy to output until openning is found
while (s.Peek( ) != "(")
{
rpnTokenList.Add(s.Pop( ));
if (s.Count == 0)
{
throw new ArgumentException("Openning parenthesis missing.");
}
}
s.Pop( );
// saving last token...
lastToken = upToken;
}
// finally, the token is a variable
else
{
// find the variable
try
{
lingVar = database.GetVariable(token);
lastToken = "VAR";
}
catch (KeyNotFoundException)
{
throw new ArgumentException("Linguistic variable " + token + " was not found on the database.");
}
}
}
}
// popping all operators left in stack
while (s.Count > 0)
{
rpnTokenList.Add(s.Pop( ));
}
}
public void SetOutput()
{
this.Risk = new LinguisticVariable("Ryzyko", 0, 30);
TrapezoidalFunction function1 = new TrapezoidalFunction(5, 10, TrapezoidalFunction.EdgeType.Right);
FuzzySet set1 = new FuzzySet("niskie", function1);
TrapezoidalFunction function2 = new TrapezoidalFunction(5, 10, 15);
FuzzySet set2 = new FuzzySet("średnio_niskie", function2);
TrapezoidalFunction function3 = new TrapezoidalFunction(10, 15, 20);
FuzzySet set3 = new FuzzySet("średnie", function3);
TrapezoidalFunction function4 = new TrapezoidalFunction(15, 20, 25);
FuzzySet set4 = new FuzzySet("średnio_wysokie", function4);
TrapezoidalFunction function5 = new TrapezoidalFunction(20, 25, TrapezoidalFunction.EdgeType.Left);
FuzzySet set5 = new FuzzySet("wysokie", function5);
Risk.AddLabel(set1);
Risk.AddLabel(set2);
Risk.AddLabel(set3);
Risk.AddLabel(set4);
Risk.AddLabel(set5);
database.AddVariable(Risk);
double y1;
double y2;
double y3;
double y4;
double y5;
for (float x = 0; x < 30; x += 0.05f)
{
if (Risk.GetLabelMembership("niskie", x + 0.05f) + Risk.GetLabelMembership("niskie", x) > 0)
{
y1 = Risk.GetLabelMembership("niskie", x);
chart6.Series["Niskie"].Points.AddXY(x, y1);
}
if (Risk.GetLabelMembership("średnio_niskie", x + 0.05f) + Risk.GetLabelMembership("średnio_niskie", x) > 0)
{
y2 = Risk.GetLabelMembership("średnio_niskie", x);
chart6.Series["Śr_nisk"].Points.AddXY(x, y2);
}
if (Risk.GetLabelMembership("średnie", x + 0.05f) + Risk.GetLabelMembership("średnie", x) > 0)
{
y3 = Risk.GetLabelMembership("średnie", x);
chart6.Series["Średnie"].Points.AddXY(x, y3);
}
if (Risk.GetLabelMembership("średnio_wysokie", x + 0.05f) + Risk.GetLabelMembership("średnio_wysokie", x) > 0)
{
y4 = Risk.GetLabelMembership("średnio_wysokie", x);
chart6.Series["Śr_wys"].Points.AddXY(x, y4);
}
if (Risk.GetLabelMembership("wysokie", x + 0.05f) + Risk.GetLabelMembership("wysokie", x) > 0)
{
y5 = Risk.GetLabelMembership("wysokie", x);
chart6.Series["Wysokie"].Points.AddXY(x, y5);
}
}
}
public FuzzyEngine()
{
// Linguistic labels (fuzzy sets) for Momentum
FuzzySet momDown = new FuzzySet("Down", new TrapezoidalFunction(-20, 5, 5, 5));
FuzzySet momNeutral = new FuzzySet("Neutral", new TrapezoidalFunction(-20, 0, 0, 20));
FuzzySet momUp = new FuzzySet("Up", new TrapezoidalFunction(5, 20, 20, 20));
// Linguistic labels (fuzzy sets) for RSI
FuzzySet rsiLow = new FuzzySet("Low", new TrapezoidalFunction(0, 30, 30, 30));
FuzzySet rsiMedium = new FuzzySet("Medium", new TrapezoidalFunction(0, 50, 50, 100));
FuzzySet rsiHigh = new FuzzySet("High", new TrapezoidalFunction(70, 100, 100, 100));
// MOM (Input)
LinguisticVariable lvMom = new LinguisticVariable("MOM", -20, 20);
lvMom.AddLabel(momDown);
lvMom.AddLabel(momNeutral);
lvMom.AddLabel(momUp);
// RSI (Input)
LinguisticVariable lvRsi = new LinguisticVariable("RSI", 0, 100);
lvRsi.AddLabel(rsiLow);
lvRsi.AddLabel(rsiMedium);
lvRsi.AddLabel(rsiHigh);
// Linguistic labels (fuzzy sets) that compose the Signal
FuzzySet fsShort = new FuzzySet("Sell", new TrapezoidalFunction(-100, 0, 0, 00));
FuzzySet fsHold = new FuzzySet("Hold", new TrapezoidalFunction(-50, 0, 0, 50));
FuzzySet fsLong = new FuzzySet("Buy", new TrapezoidalFunction(0, 100, 100, 100));
// Output
LinguisticVariable lvSignal = new LinguisticVariable("Signal", -100, 100);
lvSignal.AddLabel(fsShort);
lvSignal.AddLabel(fsHold);
lvSignal.AddLabel(fsLong);
// The database
Database fuzzyDB = new Database();
fuzzyDB.AddVariable(lvMom);
fuzzyDB.AddVariable(lvRsi);
fuzzyDB.AddVariable(lvSignal);
// Creating the inference system
IS = new InferenceSystem(fuzzyDB, new CentroidDefuzzifier(1000));
// Rules
IS.NewRule("Rule 1", "IF RSI IS Low AND MOM IS Down THEN Signal IS Buy");
IS.NewRule("Rule 2", "IF RSI IS Medium AND MOM IS Down THEN Signal IS Buy");
IS.NewRule("Rule 3", "IF RSI IS High AND MOM IS Down THEN Signal IS Hold");
IS.NewRule("Rule 4", "IF RSI IS Low AND MOM IS Neutral THEN Signal IS Buy");
IS.NewRule("Rule 5", "IF RSI IS Medium AND MOM IS Neutral THEN Signal IS Hold");
IS.NewRule("Rule 6", "IF RSI IS High AND MOM IS Neutral THEN Signal IS Sell");
IS.NewRule("Rule 7", "IF RSI IS Low AND MOM IS Up THEN Signal IS Hold");
IS.NewRule("Rule 8", "IF RSI IS Medium AND MOM IS Up THEN Signal IS Sell");
IS.NewRule("Rule 9", "IF RSI IS High AND MOM IS Up THEN Signal IS Sell");
}
