'''simple docstring'''






import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
    # Create universe of discourse in Python using linspace ()
    SCREAMING_SNAKE_CASE_	   =			np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

    # Create two fuzzy sets by defining any membership function
    # (trapmf(), gbellmf(), gaussmf(), etc).
    SCREAMING_SNAKE_CASE_	   =			[0, 25, 50]
    SCREAMING_SNAKE_CASE_	   =			[25, 50, 75]
    SCREAMING_SNAKE_CASE_	   =			fuzz.membership.trimf(X, abca)
    SCREAMING_SNAKE_CASE_	   =			fuzz.membership.trimf(X, abca)

    # Compute the different operations using inbuilt functions.
    SCREAMING_SNAKE_CASE_	   =			np.ones(75)
    SCREAMING_SNAKE_CASE_	   =			np.zeros((75,))
    # 1. Union = max(µA(x), µB(x))
    SCREAMING_SNAKE_CASE_	   =			fuzz.fuzzy_or(X, young, X, middle_aged)[1]
    # 2. Intersection = min(µA(x), µB(x))
    SCREAMING_SNAKE_CASE_	   =			fuzz.fuzzy_and(X, young, X, middle_aged)[1]
    # 3. Complement (A) = (1- min(µA(x))
    SCREAMING_SNAKE_CASE_	   =			fuzz.fuzzy_not(young)
    # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
    SCREAMING_SNAKE_CASE_	   =			fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
    # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
    SCREAMING_SNAKE_CASE_	   =			young + middle_aged - (young * middle_aged)
    # 6. Algebraic Product = (µA(x) * µB(x))
    SCREAMING_SNAKE_CASE_	   =			young * middle_aged
    # 7. Bounded Sum = min[1,(µA(x), µB(x))]
    SCREAMING_SNAKE_CASE_	   =			fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
    # 8. Bounded difference = min[0,(µA(x), µB(x))]
    SCREAMING_SNAKE_CASE_	   =			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, 10)
    plt.plot(X, bdd_difference)
    plt.title('bdd_difference')
    plt.grid(True)

    plt.subplots_adjust(hspace=0.5)
    plt.show()
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)