"""Membership Function Module."""
import warnings
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from lsapy.core.functions import declare_equation, equations, get_function_from_name
__all__ = [
"fit_membership",
"logistic",
"sigmoid",
"vetharaniam2022_eq3",
"vetharaniam2022_eq5",
"vetharaniam2024_eq8",
"vetharaniam2024_eq10",
]
EQUATION_TYPES = ["sigmoid", "gaussian"]
[docs]
@declare_equation("sigmoid")
def logistic(x, a, b):
r"""
Logistic function.
Parameters
----------
x : any
Input values to map.
a : float | int
Steepness of the function parameter.
b : float | int
Value of the function's midpoint.
Returns
-------
float
Suitability values.
Notes
-----
The logistic function is defined as:
.. math::
f(x) = \frac{1}{1 + e^{-a(x - b)}}
"""
return 1 / (1 + np.exp(-a * (x - b)))
[docs]
@declare_equation("sigmoid")
def sigmoid(x):
r"""
Sigmoid function.
Parameters
----------
x : any
Input values to map.
Returns
-------
float
Suitability values.
Notes
-----
The sigmoid function is defined as:
.. math::
f(x) = \frac{1}{1 + e^{-x}}
"""
return logistic(x, 1, 0)
[docs]
@declare_equation("sigmoid", "VTR22_3")
def vetharaniam2022_eq3(x, a, b):
r"""
Sigmoid like function.
# TODO: add a more detailed description.
Parameters
----------
x : any
Input values to map.
a : float | int
Steepness of the function parameter.
b : float | int
Value of the function's midpoint.
Returns
-------
float
Suitability values.
Notes
-----
The sigmoid like function is defined as:
.. math::
f(x) = \frac{e^{a(x - b)}}{1 + e^{a(x - b)}}
References
----------
:cite:cts:`vetharaniam_lsa_2022`
"""
return np.exp(a * (x - b)) / (1 + np.exp(a * (x - b)))
[docs]
@declare_equation("sigmoid", "VTR22_5")
def vetharaniam2022_eq5(x, a, b):
r"""
Sigmoid like function.
# TODO: add a more detailed description.
Parameters
----------
x : any
Input values to map.
a : float | int
Steepness of the function parameter.
b : float | int
Value of the function's midpoint.
Returns
-------
float
Suitability values.
Notes
-----
The sigmoid like function is defined as:
.. math::
f(x) = \frac{1}{1 + e^{a(\sqrt{x} - \sqrt{b})}}
References
----------
:cite:cts:`vetharaniam_lsa_2022`
"""
return 1 / (1 + np.exp(a * (np.sqrt(x) - np.sqrt(b))))
[docs]
@declare_equation("gaussian", "VTR24_8")
def vetharaniam2024_eq8(x, a, b, c):
r"""
Gaussian like function.
# TODO: add a more detailed description.
Parameters
----------
x : any
Input values to map.
a : float | int
Steepness of the function parameter.
b : float | int
Value of the function's midpoint.
c : float | int
Scaling parameter.
Returns
-------
float
Suitability values.
Notes
-----
The Gaussian like function is defined as:
.. math::
f(x) = e^{-a(x - b)^c}
References
----------
:cite:cts:`vetharaniam_lsa_2024`
"""
return np.exp(-a * np.power(x - b, c))
[docs]
@declare_equation("gaussian", "VTR24_10")
def vetharaniam2024_eq10(x, a, b, c):
r"""
Gaussian like function.
# TODO: add a more detailed description.
Parameters
----------
x : any
Input values to map.
a : float | int
Steepness of the function parameter.
b : float | int
Value of the function's midpoint.
c : float | int
Scaling parameter.
Returns
-------
float
Suitability values.
Notes
-----
The Gaussian like function is defined as:
.. math::
f(x) = e^{-a(x^c - b^c)}
References
----------
:cite:cts:`vetharaniam_lsa_2024`
"""
return 2 / (1 + np.exp(a * np.power(np.power(x, c) - np.power(b, c), 2)))
def fit_membership(x, y=None, fit_on: str | list[str] = "all", plot: bool = False, verbose: bool = False):
"""
Fit membership function to data.
This function fits membership functions to the provided data. It helps to determine the best membership function
to use on the data.
Parameters
----------
x : any
Input values to fit the functions on.
y : any, optional
Target suitability values to fit the functions. Should be the same length as `x`. If not provided,
the default values are used (0, 0.25, 0.5, 0.75, 1).
fit_on : str | list[str], optional
List of equation or equation types to fit. `all, `sigmoid_like` and `gaussian_like` can also be used.
If 'all', all available equations are fitted. If '{TYPES}_like', all equations corresponding to the
type are fitted. Default is 'all'.
plot : bool, optional
Whether to plot the fitted functions. Default is False.
verbose : bool, optional
Whether to print the fitting results. Default is False.
Returns
-------
tuple
A tuple containing the best fitting function and its parameters.
Examples
--------
>>> from lsapy.functions.membership import fit_membership
>>> fit_membership([1, 3, 5, 7, 10]) # doctest: +ELLIPSIS
(<function vetharaniam2022_eq5 at 0x...>, array([-2.78959209, 4.86485647]))
By default, the function will fit all available membership equations. If you want to fit only specific equations,
you can specify it using the `fit_on` parameter: "all", "sigmoid_like", "gaussian_like", or a list of equations.
The default `y` values can also be changed.
>>> fit_membership(x=[1, 3, 5, 5, 7, 9], y=[0, 0.5, 1, 1, 0.5, 0], fit_on="gaussian_like") # doctest: +ELLIPSIS
(<function vetharaniam2024_eq10 at 0x...>, array([0.38213219, 4.97273138, 0.93922461]))
"""
if y is None:
y = [0, 0.25, 0.5, 0.75, 1]
y = np.array(y)
functions, skipped = _check_fitting(fit_on)
x_ = np.linspace(min(x), max(x), 100)
rms_errors = []
f_params = []
for func in functions:
try:
f = get_function_from_name(func)
p0 = _get_function_p0(func, x)
popt, _ = curve_fit(f, x, y, p0=p0, maxfev=15000)
y_ = f(x_, *popt)
f_params.append(popt)
rmse = _rmse(y, f(x, *popt))
rms_errors.append(rmse)
if plot:
plt.plot(x_, y_, label=func + f" (RMSE={rmse:.2f})")
except Exception:
skipped.append(func)
warnings.warn(f"Failed to fit `{func}`. Skipped.", stacklevel=2)
if all([f in skipped for f in functions]):
warnings.warn(f"No methods to fit. fitting for the following methods: {', '.join(skipped)}.", stacklevel=2)
return None, None
if plot:
plt.scatter(x, y, c="r")
plt.legend()
plt.show()
f_best, p_best = _get_best_fit([m for m in functions if m not in skipped], rms_errors, f_params, verbose=verbose)
return get_function_from_name(f_best), p_best
def _check_fitting(fit_on: str | list[str] = "all"):
_types = [t + "_like" for t in EQUATION_TYPES]
_skipped = []
if not isinstance(fit_on, str) and not isinstance(fit_on, list):
raise ValueError(f"`fit_on` should be a str or a list of string. Got {type(fit_on)}")
functions = []
if isinstance(fit_on, str):
if fit_on == "all":
for t in EQUATION_TYPES:
functions.extend(equations[t].keys())
fit_on = None
else:
fit_on = [fit_on]
if fit_on is not None:
for func in fit_on:
if not isinstance(func, str):
continue
if func in _types:
for f in equations[func.replace("_like", "")].keys():
if f not in functions:
functions.append(f)
else:
try:
get_function_from_name(func)
if func not in functions:
functions.append(func)
except Exception:
_skipped.append(func)
warnings.warn(f"`{func}` not found in implemented equations. Skipped.", stacklevel=3)
for f in ["sigmoid", "vetharaniam2024_eq8"]:
if f in functions:
functions.remove(f)
_skipped.append(f)
if f == "sigmoid":
warnings.warn("No parameters to determine for `sigmoid`. Skipped.", stacklevel=3)
if f == "vetharaniam2024_eq8":
warnings.warn("Fitting does not support `vetharaniam2024_eq8`. Skipped.", stacklevel=3)
if len(functions) == 0:
raise ValueError("No functions to fit. Try to modify `fit_on` parameter.")
return functions, _skipped
def _get_function_p0(func: str, x: np.ndarray) -> list[float]:
if func in equations["sigmoid"]:
return [1, np.median(x)]
if func in equations["gaussian"]:
return [1, np.median(x), 1]
return []
def _rmse(y_true, y_pred):
diff = abs(y_true - y_pred)
return np.sqrt(np.mean(diff**2))
def _get_best_fit(functions, rmse, params, verbose=True):
best_fit = np.nanargmin(rmse)
if verbose:
print(f"""
Best fit: {functions[best_fit]}
RMSE: {rmse[best_fit]:.5f}
Params: a={params[best_fit][0]}, b={params[best_fit][1]}
""")
return functions[best_fit], params[best_fit]
