


'''simple docstring'''

import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	_UpperCAmelCase				: Optional[int]							    =				np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

	# Create two fuzzy sets by defining any membership function
	# (trapmf(), gbellmf(), gaussmf(), etc).
	_UpperCAmelCase				: Optional[int]							    =				[0, 2_5, 5_0]
	_UpperCAmelCase				: int							    =				[2_5, 5_0, 7_5]
	_UpperCAmelCase				: Union[str, Any]							    =				fuzz.membership.trimf(X, abca)
	_UpperCAmelCase				: Dict							    =				fuzz.membership.trimf(X, abca)

	# Compute the different operations using inbuilt functions.
	_UpperCAmelCase				: Optional[Any]							    =				np.ones(7_5)
	_UpperCAmelCase				: Tuple							    =				np.zeros((7_5,))
	# 1. Union = max(µA(x), µB(x))
	_UpperCAmelCase				: List[Any]							    =				fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	_UpperCAmelCase				: Union[str, Any]							    =				fuzz.fuzzy_and(X, young, X, middle_aged)[1]
	# 3. Complement (A) = (1- min(µA(x))
	_UpperCAmelCase				: Tuple							    =				fuzz.fuzzy_not(young)
	# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
	_UpperCAmelCase				: List[str]							    =				fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
	# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
	_UpperCAmelCase				: Any							    =				young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	_UpperCAmelCase				: List[str]							    =				young * middle_aged
	# 7. Bounded Sum = min[1,(µA(x), µB(x))]
	_UpperCAmelCase				: Optional[Any]							    =				fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
	# 8. Bounded difference = min[0,(µA(x), µB(x))]
	_UpperCAmelCase				: List[Any]							    =				fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

	# max-min composition
	# max-product composition

	# Plot each set A, set B and each operation result using plot() and subplot().
	from matplotlib import pyplot as plt

	plt.figure()

	plt.subplot(4, 3, 1)
	plt.plot(X, young)
	plt.title("""Young""")
	plt.grid(True)

	plt.subplot(4, 3, 2)
	plt.plot(X, middle_aged)
	plt.title("""Middle aged""")
	plt.grid(True)

	plt.subplot(4, 3, 3)
	plt.plot(X, union)
	plt.title("""union""")
	plt.grid(True)

	plt.subplot(4, 3, 4)
	plt.plot(X, intersection)
	plt.title("""intersection""")
	plt.grid(True)

	plt.subplot(4, 3, 5)
	plt.plot(X, complement_a)
	plt.title("""complement_a""")
	plt.grid(True)

	plt.subplot(4, 3, 6)
	plt.plot(X, difference)
	plt.title("""difference a/b""")
	plt.grid(True)

	plt.subplot(4, 3, 7)
	plt.plot(X, alg_sum)
	plt.title("""alg_sum""")
	plt.grid(True)

	plt.subplot(4, 3, 8)
	plt.plot(X, alg_product)
	plt.title("""alg_product""")
	plt.grid(True)

	plt.subplot(4, 3, 9)
	plt.plot(X, bdd_sum)
	plt.title("""bdd_sum""")
	plt.grid(True)

	plt.subplot(4, 3, 1_0)
	plt.plot(X, bdd_difference)
	plt.title("""bdd_difference""")
	plt.grid(True)

	plt.subplots_adjust(hspace=0.5)
	plt.show()





'''simple docstring'''

from torch import nn

def 							__magic_name__(						lowerCamelCase):
						if act_fn in ["swish", "silu"]:
												return nn.SiLU()
						elif act_fn == "mish":
												return nn.Mish()
						elif act_fn == "gelu":
												return nn.GELU()
						else:
												raise ValueError(F"""Unsupported activation function: {act_fn}""")

