In this package, you will find some utilities to apply fuzzy logic
t-norms and t-conorms to datasets.
You can find all the information about the T-norms and T-conorms in
the following links:
Fields used as operands must contain real values between 0 and
1. As they are logical values it doesn't make sense having values
outside this range. In this case, the script will raise an error.
The script will create a new column in the dataset for each one of the
norms specified in the norms list.
T-norms and T-conorms
Below, you can find an example of the content of the norms input:
["min-tnorm","product","lukasiewicz", "drastic-tnorm",
"nilpotent-min",["schweizer-sklar", 0.5], ["hamacher", 0,5]]
As you can see there are some norms that need a parameter that has to
be specified in this way.
These are the possibles t-norms:
- min-tnorm: Minimum t-norm. Also called the Gödel t-norm. No
parameters.
- product: Product t-norm. The ordinary product of real
numbers. No parameters.
- lukasiewicz: Łukasiewicz t-norm. No parameters.
- drastic-tnorm: Drastic t-norm. No parameters.
- nilpotent-min: Nilpotent minimum t-norm. No parameters.
- [schweizer-sklar p]: Schweizer–Sklar t-norms. Parameter p in
the range [-∞, ∞]
- [hamacher p]: Hamacher t-norms. Parameter p in the range [0,
∞]
- [yager p]: Yager t-norms. Parameter p in the range [0,
∞]
- [aczel-alsina p]: Aczél–Alsina t-norms. Parameter p in the range [0,
∞]
- [dombi p]: Dombi t-norms. Parameter p in the range [0,
∞]
- [sugeno-weber p]: Sugeno–Weber t-norms. Parameter p in the range [-1,
∞]
These are the possible t-conorms:
- max-tconorm: Maximum t-norm. Dual to the minimum t-norm, is the
smallest t-conorm. No parameters
- probabilistic: Probabilistic t-norm. It's dual to the product
t-norm. No parameters.
- bounded: Bounded t-norm. It's dual to the Łukasiewicz t-norm. No
parameters.
- drastic-tconorm: Drastic t-conorm. It's dual to the drastic
t-norm. No paramters.
- nilpotent-max: Nilpotent maximum t-conorm. It's dual to the
nilpotent minimum.
- einstein-sum: Einstein t-conorm. It's a dual to one of the Hamacher t-norms.
Find more info in readme
In this package, you will find some utilities to apply fuzzy logic
t-norms and t-conorms to datasets.
You can find all the information about the T-norms and T-conorms in
the following links:
Fields used as operands must contain real values between 0 and
1. As they are logical values it doesn't make sense having values
outside this range. In this case, the script will raise an error.
The script will create a new column in the dataset for each one of the
norms specified in the norms list.
T-norms and T-conorms
Below, you can find an example of the content of the norms input:
["min-tnorm","product","lukasiewicz", "drastic-tnorm",
"nilpotent-min",["schweizer-sklar", 0.5], ["hamacher", 0,5]]
As you can see there are some norms that need a parameter that has to
be specified in this way.
These are the possibles t-norms:
- min-tnorm: Minimum t-norm. Also called the Gödel t-norm. No
parameters.
- product: Product t-norm. The ordinary product of real
numbers. No parameters.
- lukasiewicz: Łukasiewicz t-norm. No parameters.
- drastic-tnorm: Drastic t-norm. No parameters.
- nilpotent-min: Nilpotent minimum t-norm. No parameters.
- [schweizer-sklar p]: Schweizer–Sklar t-norms. Parameter p in
the range [-∞, ∞]
- [hamacher p]: Hamacher t-norms. Parameter p in the range [0,
∞]
- [yager p]: Yager t-norms. Parameter p in the range [0,
∞]
- [aczel-alsina p]: Aczél–Alsina t-norms. Parameter p in the range [0,
∞]
- [dombi p]: Dombi t-norms. Parameter p in the range [0,
∞]
- [sugeno-weber p]: Sugeno–Weber t-norms. Parameter p in the range [-1,
∞]
These are the possible t-conorms:
- max-tconorm: Maximum t-norm. Dual to the minimum t-norm, is the
smallest t-conorm. No parameters
- probabilistic: Probabilistic t-norm. It's dual to the product
t-norm. No parameters.
- bounded: Bounded t-norm. It's dual to the Łukasiewicz t-norm. No
parameters.
- drastic-tconorm: Drastic t-conorm. It's dual to the drastic
t-norm. No paramters.
- nilpotent-max: Nilpotent maximum t-conorm. It's dual to the
nilpotent minimum.
- einstein-sum: Einstein t-conorm. It's a dual to one of the Hamacher t-norms.
Find more info in readme
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