diff --git a/Sources/FuzzyKit/Functions/OperatorFunctions.swift b/Sources/FuzzyKit/Functions/OperatorFunctions.swift
index b806bc6..266a2d8 100644
--- a/Sources/FuzzyKit/Functions/OperatorFunctions.swift
+++ b/Sources/FuzzyKit/Functions/OperatorFunctions.swift
@@ -1,44 +1,121 @@
+import RealModule
+/// Describes a T-norm function, usually used for forming an intersection of fuzzy sets.
+///
+/// A function `T` must satisfy the following axioms in order to be T-normed:
+/// 1. Boundary condition: `T(1, 1) = 1`, `T(1, 0) = 0`, `T(0, 1) = 0`, `T(0, 0) = 0`
+/// 2. Commutativity: `T(a, b) = T(b, a)`
+/// 3. Monotonic: `If a <= a' and b <= b' Then T(a, b) <= T(a', b')`
+/// 4. Associativity: `T(T(a, b), c) = T(a, T(b, c))`
 public enum TNormFunction {
     case minimum
     case lukasiewicz
     case product
     case weak
-    case hamacher
-    case duboisAndPrade
-    case yager
-    case frank
+    case hamacher(gamma: Double)
+    case duboisAndPrade(alpha: Double)
+    case yager(p: Double)
+    case frank(lambda: Double)
+    
     case custom((Grade, Grade) -> Grade)
     
     var function: (Grade, Grade) -> Grade {
-        min
-        // TODO
+        switch self {
+        case .minimum:
+            return min
+        case .lukasiewicz:
+            return { max($0 + $1, 1) }
+        case .product:
+            return { $0 * $1 }
+        case .weak:
+            return { max($0, $1) == 1 ? min($0, $1) : 0 }
+        case .hamacher(gamma: let g):
+            return { $0 * $1 / (g + (1 - g)*($0 + $1 - $0*$1)) }
+        case .duboisAndPrade(alpha: let a):
+            return { $0 * $1 / max($0, $1, a) }
+        case .yager(p: let p):
+            return {
+                1 - min(1.0, ((1-$0).pow(p) + (1-$1).pow(p)).root(p))
+            }
+        case .frank(lambda: let l):
+            switch l {
+            case 0:
+                return TNormFunction.minimum.function
+            case 1:
+                return TNormFunction.product.function
+            case .infinity:
+                return TNormFunction.lukasiewicz.function
+            default:
+                return {
+                    1 - (1 + (l.pow($0) - 1)*(l.pow($1) - 1) / (l - 1)).log(base: l)
+                }
+            }
+        case .custom(let function):
+            return function
+        }
     }
 }
 
+/// Describes a S-norm (T-conorm) function, usually used for forming a union of fuzzy sets.
+///
+/// A function `S` must satisfy the following axioms in order to be S-normed (T-conormed):
+/// 1. Boundary condition: `S(1, 1) = 1`, `S(1, 0) = 1`, `S(0, 1) = 1`, `S(0, 0) = 0`
+/// 2. Commutativity: `S(a, b) = S(b, a)`
+/// 3. Monotonic: `If a <= a' and b <= b' Then S(a, b) <= S(a', b')`
+/// 4. Associativity: `S(T(a, b), c) = S(a, T(b, c))`
 public enum SNormFunction {
     case maximum
     case lukasiewicz
     case probabilistic
     case strong
-    case hamacher
-    case yager
+    case hamacher(gamma: Double)
+    case yager(p: Double)
+    
     case custom((Grade, Grade) -> Grade)
     
     var function: (Grade, Grade) -> Grade {
-        max
-        // TODO
+        switch self {
+        case .maximum:
+            return max
+        case .lukasiewicz:
+            return { min($0 + $1, 1) }
+        case .probabilistic:
+            return { $0 + $1 - $0*$1 }
+        case .strong:
+            return { min($0, $1) == 0 ? max($0, $1) : 1 }
+        case .hamacher(gamma: let g):
+            return { ($0 + $1 - (2 - g)*$0*$1) / (1 - (1 - g)*$0*$1) }
+        case .yager(p: let p):
+            return { min(1, ($0.pow(p) + $1.pow(p)).root(p)) }
+        case .custom(let function):
+            return function
+        }
     }
 }
 
+/// Describes a complement (negation) function, usually used for forming a complement (negation) of a fuzzy set.
+///
+/// A function `c` must satisfy the following axioms in order to be capable of negating a fuzzy set:
+/// 1. Boundary condition: `c(0) = 1`, `c(1) = 0`
+/// 2. Monotonic nonincreasing: `a < b -> c(a) >= c(b)`
+/// 3. Involution: `c(c(a)) = a`
 public enum ComplementFunction {
     case standard
-    case yager
-    case sugeno
+    case yager(w: Double)
+    case sugeno(s: Double)
+    
     case custom((Grade) -> Grade)
     
     var function: (Grade) -> Grade {
-        { 1 - $0 }
-        // TODO
+        switch self {
+        case .standard:
+            return { 1 - $0 }
+        case .yager(w: let w):
+            return { (1 - $0.pow(w)).root(w) }
+        case .sugeno(s: let s):
+            return { (1 - $0) / (1 - s * $0) }
+        case .custom(let function):
+            return function
+        }
     }
 }
 
@@ -75,9 +152,28 @@ public enum SymmetricDifferenceFunction {
         case .minMaxAndStandardComplement:
             return { max(min($0, 1 - $1), min($1, 1 - $0)) }
         case .tNormSNormAndComplement(let t, let s, let c):
-            return { s.function(t.function($0, c.function($1)), t.function($1, c.function($0))) }
+            return {
+                s.function(
+                    t.function($0, c.function($1)),
+                    t.function($1, c.function($0))
+                )
+            }
         case .custom(let function):
             return function
         }
     }
 }
+
+internal extension Double {
+    func pow(_ x: Double) -> Double {
+        Double.pow(self, x)
+    }
+    
+    func log(base: Double) -> Double {
+        Double.log(self) / Double.log(base)
+    }
+    
+    func root(_ x: Double) -> Double {
+        Double.pow(self, 1/x)
+    }
+}