fugologic

Fugologic is a basic implementation of a fuzzy logic system.

TL;DR

Define fuzzy values, create the rules and evaluate them.

Describe complex rules manually or using a builder
Describe simple rules using specific methods (like the fuzzy associative matrix)

For example, implement the following rules

HP/FP => Act	Very low HP	Low HP	Medium HP	High HP	Very high HP
Very weak FP	Retreat!	Retreat!	Defend	Defend	Defend
Weak FP	Retreat!	Defend	Defend	Attack	Attack
Medium FP	Retreat!	Defend	Attack	Attack	Full attack!
High FP	Retreat!	Defend	Attack	Attack	Full attack!
Very high FP	Defend	Attack	Attack	Full attack!	Full attack!

// Create a crisp set, fuzzy sets and a fuzzy value
// Input HP [0 ; 100]
crispHP, _ := crisp.NewSetN(0, 100, 1000)
fsHP, _ := fuzzy.NewIDSets(map[id.ID]fuzzy.SetBuilder{
  "Very low HP":  fuzzy.StepDown{A: 0, B: 20},
  "Low HP":       fuzzy.Trapezoid{A: 0, B: 20, C: 40, D: 60},
  "Medium HP":    fuzzy.Triangular{A: 40, B: 50, C: 60},
  "High HP":      fuzzy.Trapezoid{A: 40, B: 60, C: 80, D: 100},
  "Very high HP": fuzzy.StepUp{A: 80, B: 100},
})
fvHP, _ := fuzzy.NewIDVal("HP", crispHP, fsHP)

// Create other fuzzy values the same way
// * fvFP  [0 ; 100]:  "Very weak FP", "Weak FP", "Medium FP", "High FP", "Very high FP"
// * fvAct [-10 ; 10]: "Retreat!", "Defend", "Attack", "Full attack!"

// Express all rules using the "fuzzy associative matrix"
// if <HP> and <FP> then <Act>
bld := builder.Mamdani().FuzzyAssoMatrix()
_ = bld.
  Asso(fvHP, fvFP, fvAct).
  Matrix(
    []id.ID{"Very low HP", "Low HP", "Medium HP", "High HP", "Very high HP"},
    map[id.ID][]id.ID{
      "Very weak FP": {"Retreat!", "Retreat!", "Defend", "Defend", "Defend"},
      "Weak FP":      {"Retreat!", "Defend", "Defend", "Attack", "Attack"},
      "Medium FP":    {"Retreat!", "Defend", "Attack", "Attack", "Full attack!"},
      "High FP":      {"Retreat!", "Defend", "Attack", "Attack", "Full attack!"},
      "Very high FP": {"Defend", "Attack", "Attack", "Full attack!", "Full attack!"},
    },
  )

// Create an engine and evaluate it
engine, _ := bld.Engine()
result, _ := engine.Evaluate(fuzzy.DataInput{
  fvHP: 75,
  fvFP: 30,
})

// Manage output
return result[fvAct]

Getting started

For more examples, see /fugologic/example/

Define the system

Crisp values definition

Defuzzification requires a crisp interval of discrete values.

It is defined as crisp.Set (x min, x max, dx)

// Each values from 0.0 to 0.3 every 0.1 => [0.0, 0.1, 0.2, 0.3]
set, err := crisp.NewSet(0.0, 0.3, 0.1)
if err != nil{
  return err
}

It can also be defined with n values (x min, x max, n values)

// 4 values in [0.0 ; 0.3]
set, err := crisp.NewSetN(0.0, 0.3, 4)
if err != nil{
  return err
}

Membership function definition

A membership function is defined as a fuzzy.Set. Several methods are proposed, like :

method	description	shape
`Gauss`	Gaussian	▁/⁀\▁
`Gbell`	Generalized bell-shaped	▁/⁀\▁
`Trapezoid`	Trapezoïdal	▁/▔\▁
`Triangular`	Triangular	▁/\▁
`StepUp`	Step up (S shape)	▁/▔
`StepDown`	Step down (Z shape)	▔\▁
`Sigmoid`	Sigmoïdal (S or Z shape)	▁/▔ or ▔\▁

Initialise a builder and call New to get the fuzzy.Set and check for errors

set, err := Triangular{A: 1, B: 2, C: 3}.New()
if err != nil {
  return err
}

Fuzzy values definition

Fuzzy values and fuzzy sets are defined as :

fuzzy.IDVal: a fuzzy value that contains,
- an identifier id.ID
- a crisp.Set interval of values (required for defuzzification)
- a list of fuzzy.IDSet ; and each on contains,
  - an identifier id.ID
  - a membership method fuzzy.Set
  - its fuzzy.IDVal parent

Notes : every identifier shall be unique in a fuzzy.Engine

Define fuzzy inputs / outputs

First, create a fuzzy value and link it to a list of fuzzy sets.

Ensure that the crisp interval of the fuzzy value covers all the fuzzy sets intervals.

// Fuzzy sets "a1", "a2"
// Use the builder or create them manually
fsA, _ := fuzzy.NewIDSets(map[id.ID]fuzzy.SetBuilder{
  "a1": fuzzy.Triangular{-3, -1, 1},
  "a2": fuzzy.Trapezoid{-1, 1, 3, 5},
})

// Fuzzy value "a"
crispA, _ := crisp.NewSet(-3, 5, 0.1)
fvA, _ := fuzzy.NewIDVal("a", crispA, fsA)

// Retrieve fuzzy sets using their ids
fsA1 := fvA.Get("a1")
fsA2 := fvA.Get("a2")

// Or fetch a fuzzy set and its presence
// - fsUnknown is empty
// - ok is false
fsUnknown, ok := fvA.Fetch("unknown")

Create other inputs and outputs the same way.

Define the rules

A rule is defined with 3 components :

expression : connects several fuzzy sets together
implication : defines an implication method
consequence : defines several fuzzy sets as the outputs

rule = <expression> <implication> <consequence>
rule = A1 and B1    then          C1, D1

Use a rule builder

The rule builder is optional but helps creating simple rules, and then, an engine.

Use a predefined configuration, and then create a builder

cfg := builder.Mamdani() // Predefined configuration
bld := cfg.FuzzyLogic()  // Rule builder using the predefined configuration

Or create a custom configuration, and then create a builder

cfg := builder.Config{
  Optr:   fuzzy.OperatorZadeh{},
  Impl:   fuzzy.ImplicationMin,
  Agg:    fuzzy.AggregationUnion,
  Defuzz: fuzzy.DefuzzificationCentroid,
}
bld := cfg.FuzzyLogic()

Or use your configuration to created the wanted builder

bld := builder.NewFuzzyLogic(
  fuzzy.OperatorZadeh{},
  fuzzy.ImplicationMin,
  fuzzy.AggregationUnion,
  fuzzy.DefuzzificationCentroid,
)

Details of the configuration parameters

type	example	description
`fuzzy.Operator`		connect several rule premises together to create an expression
	`OperatorZadeh`	Zadeh `And`, `Or`, `XOr` connectors
	`OperatorHyperbolic`	Hyperbolic `And`, `Or`, `XOr` connectors
`fuzzy.Implication`		propagates the expression results into consequences
	`ImplicationMin`	Mamdani implication minimum
	`ImplicationProd`	Sugeno implication product
`fuzzy.Aggregation`		merges all coherent implications
	`AggregationUnion`	union
	`AggregationIntersection`	intersection
`fuzzy.Defuzzification`		extracts one value from the aggregated results
	`DefuzzificationCentroid`	centroïd: center of gravity
	`DefuzzificationBisector`	bisector: position under the curve where the areas on both sides are equal
	`DefuzzificationSmallestOfMaxs`	if several `y` maximums are found, get the one with the smallest `x`
	`DefuzzificationMiddleOfMaxs`	if several `y` maximums are found, get the point at the middle of the smallest and the largest `x`
	`DefuzzificationLargestOfMaxs`	if several `y` maximums are found, get the one with the largest `x`

Describe an input expression

Select the input fuzzy.IDSet and link them using a fuzzy.Operator.

Simplest case : the expression has only one premise and no connector (directly use the fuzzy set)

// A1
exp := fsA1

An expression is a flat list of several fuzzy.IDSet linked with the same fuzzy.Connector.

For example : A1 and B1 not C1.

// Using a builder
// A1 and B1 and C1
exp := bld.If(fsA1).And(fsB1).And(fsC1)

Or in a more explicit way

// Using explicit syntax
// A1 and B1 and C1
exp := fuzzy.NewExpression([]fuzzy.Premise{fsA1, fsB1, fsC1}, fuzzy.OperatorZadeh{}.And)

At last, an expression can be more complex like (A1 and B1 and C1) not-or (D1 and E1).

Note : an expression can be complemented using the Not function

// Using a builder
expABC := bld.If(fsA1).And(fsB1).And(fsC1) // A1 and B1 and C1
expDE := bld.If(fsD1).And(fsE1)            // D1 and E1
exp := expABC.Or(expDE).Not()              // (A1 and B1 and C1) not-or (D1 and E1)

Or in a more explicit way

// Using explicit syntax
expABC := fuzzy.NewExpression([]fuzzy.Premise{fsA1, fsB1, fsC1}, fuzzy.OperatorZadeh{}.And) // A1 and B1 and C1
expDE := fuzzy.NewExpression([]fuzzy.Premise{fsD1, fsE1}, fuzzy.OperatorZadeh{}.And)        // D1 and E1
exp := fuzzy.NewExpression([]fuzzy.Premise{expABC, expDE}, fuzzy.OperatorZadeh{}.Or).Not()  // (A1 and B1 and C1) not-or (D1 and E1)

Describe an implication

An implication links the input expression and the ouput consequences (using a fuzzy.Implication)

Describe an output consequence

A consequence is just a list of fuzzy.IDSet.

Write a rule

Combine several items previously seen to describe the rules.

Write a rule manually

This method can be used to easily generate rules manually. Connectors can be explicitely choosen, unlike for the first method.

Note : create a list of rules to use it afterwards.

// Using explicit syntax, the rule has to be part of a list
rules := []fuzzy.Rule{
  // A1 and B1 => C1
  fuzzy.NewRule(
    fuzzy.NewExpression([]fuzzy.Premise{fsA1, fsB1}, fuzzy.OperatorZadeh{}.And), // expression
    fuzzy.ImplicationMin,                                                        // implication
    []fuzzy.IDSet{fsC1},                                                         // consequence
  ),
  // Describe other rules the same way, for example:
  //  * A1 and B2 => C2
  //  * A2 and B1 => C1
  //  * A2 and B2 => C2
}

Write a rule using a builder

This method is useful when describing rules directly in the code (using a builder)

Note : the builder that creates a rule stores it.

// Using a builder, the rule is stored in the builder
bld := Mamdani().FuzzyLogic()
// A1 and B1 => C1
bld.If(fsA1).And(fsB1).Then(fsC1)
// Describe other rules the same way
// ...

Write rules using a fuzzy associative matrix

This method allows compact description of all rules using a fuzzy associative matrix

Notes :

it can only be used to express rules like if <a> and <b> then <c> in a tabular form
the first operand describe the columns values
the second operand describes the rows values
the last operand if the result of value of the row #i and the column #j ; an empty identifier means no rule

Eg.: express all rules using the following tabular form

if a1 and b1 then c1
if a1 and b2 then c2
...

a/b => c	a1	a2	a3
b1	c1	c2	c3
b2	c2	c3	c4
b3	c3	c4	c5
b4	c4	c5	c6
b5	c5	c6	c7

// Express all rules using a "fuzzy associative matrix"
bld := builder.Mamdani().FuzzyAssoMatrix()
err := bld.
  Asso(fvA, fvB, fvC).
  Matrix(
    []id.ID{"a1", "a2", "a3"},
    map[id.ID][]id.ID{
      "b1": {"c1", "c2", "c3"},
      "b2": {"c2", "c3", "c4"},
      "b3": {"c3", "c4", "c5"},
      "b4": {"c4", "c5", "c6"},
      "b5": {"c5", "c6", "c7"},
    },
  )
if err != nil {
  return err
}

Create an engine

A fuzzy.Engine evaluates a list of fuzzy.Rule, applies a fuzzy.Aggregation to get a fuzzy result, and extracts one crisp value for each output using a fuzzy.Defuzzification method.

Engine new instance

If the defined rules contains an error, the engine builder will return it.

Create an engine from the builder

// Using a builder
engine, err := bld.Engine()
if err != nil {
  return err
}

Or create an engine manually with custom methods

// Using explicit syntax
engine, err := fuzzy.NewEngine(rules, AggregationUnion, DefuzzificationCentroid)
if err != nil {
  return err
}

Engine evaluation

Then, launch the evaluation process by setting a new crisp input value for each fuzzy.IDVal of the engine.

The result contains a crisp value for each fuzzy output value defined.

// Evaluate all the rules of the engine
result, err := engine.Evaluate(fuzzy.DataInput{
  fvA: 1,
  fvB: 0.05,
})
if err != nil {
  return err
}

// result = fuzzy.DataOutput{
//   fvC: <crisp result>,
// }

Create a system

A system is an ordered list of engines. An output of an engine can be linked to the input of another engine.

System new instance

When creating a system, some contraints are checked, like:

all identifiers shall be unique
an output shall only be produced once
loops are forbidden : an output cannot be linked to an input of a previous engine

// Create engines
engine1, _ := fuzzy.NewEngine(rules1, AggregationUnion, DefuzzificationCentroid)
engine2, _ := fuzzy.NewEngine(rules2, AggregationUnion, DefuzzificationProd)

// Create and evaluate the system
system, err := fuzzy.NewSystem([]Engine{engine1, engine2})
if err != nil {
  return err
}

System evaluation

Then, launch the evaluation process by setting a new input value for each fuzzy.IDVal of the system.

The result contains a crisp value for each fuzzy.IDVal output value defined.

// Evaluation of the rules of each engines
result, err := system.Evaluate(fuzzy.DataInput{
  fvA: 1,
  fvB: 0.05,
})
if err != nil {
  return err
}

// result = fuzzy.DataOutput{
//   fvC: <crisp result>,
// }

Class diagram

Classes used to describe and evaluate a simple fuzzy system

classDiagram
  class Premise {
    <<interface>>
    + Evaluate()
  }
  class Expression {
    + Evaluate()
  }
  class Engine {
    + Evaluate()
  }
  class IDSet {
    + Evaluate()
  }
  class Defuzzer {
    + Defuzz()
  }
  class DataInput {
    - value()
  }
  class System {
    + Evaluate()
  }
  class Rule {
    - evaluate()
  }

  System --> "*" Engine
  DataInput --> "1" IDSet
  Defuzzer --> "1" IDSet : results

  IDSet "1" <--> "*" IDVal

  Premise "*" <-- Expression : premises
  Expression --|> Premise
  IDSet --|> Premise
  DataInput <.. Premise

  Engine --> "*" Rule : rules
  Engine --> "1" Defuzzer : defuzzer
  Engine ..> DataInput
  Engine ..> DataOutput

  Builder --> "*" Rule : rules
  Builder ..> Engine

  Rule --> "*" Premise : inputs
  Rule --> "*" IDSet : outputs

Name		Name	Last commit message	Last commit date
Latest commit History 135 Commits
builder		builder
crisp		crisp
example		example
fuzzy		fuzzy
graph		graph
id		id
.gitignore		.gitignore
go.mod		go.mod
go.sum		go.sum
readme.md		readme.md

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fugologic

TL;DR

Getting started

Define the system

Crisp values definition

Membership function definition

Fuzzy values definition

Define fuzzy inputs / outputs

Define the rules

Use a rule builder

Describe an input expression

Describe an implication

Describe an output consequence

Write a rule

Write a rule manually

Write a rule using a builder

Write rules using a fuzzy associative matrix

Create an engine

Engine new instance

Engine evaluation

Create a system

System new instance

System evaluation

Class diagram

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sbiemont/fugologic

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fugologic

TL;DR

Getting started

Define the system

Crisp values definition

Membership function definition

Fuzzy values definition

Define fuzzy inputs / outputs

Define the rules

Use a rule builder

Describe an input expression

Describe an implication

Describe an output consequence

Write a rule

Write a rule manually

Write a rule using a builder

Write rules using a fuzzy associative matrix

Create an engine

Engine new instance

Engine evaluation

Create a system

System new instance

System evaluation

Class diagram

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