Extending fuzzytool

The core principle (shared with the sibling project turboswarm): the inference loop knows nothing about concrete variants. Everything that varies lives behind a small Protocol, so extending the library means adding a callable — never editing the engine.

A new membership function

Any callable x -> degree (vectorized over NumPy arrays) works directly:

import numpy as np, fuzzytool as fz

def asym_gauss(c, sl, sr):
    def mf(x):
        x = np.asarray(x, float)
        s = np.where(x < c, sl, sr)
        return np.exp(-0.5 * ((x - c) / s) ** 2)
    return mf

v = fz.Variable("v", (0, 10))
v["mid"] = asym_gauss(5, 1.0, 3.0)

A new connective (t-norm / s-norm)

Register a vectorized (a, b) -> result:

from fuzzytool import norms

def t_einstein(a, b):
    return (a * b) / (2 - (a + b - a * b))

norms._TNORMS["einstein"] = t_einstein
sys = fz.Mamdani(tnorm="einstein")

Or pass the callable straight to the engine: fz.Mamdani(tnorm=t_einstein).

A new defuzzifier

A callable (x, y) -> float:

sys = fz.Mamdani(defuzz=lambda x, y: float((x * y).sum() / y.sum()))

A new inference engine

Implement __call__(**inputs) and reuse the antecedent tree (antecedent.eval(inputs, tnorm, snorm)) plus the shared Rule. See fuzzytool/inference/mamdani.py and tsk.py as templates.