"""simple docstring"""






import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
       # Create universe of discourse in Python using linspace ()
       __A			   = 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).
       __A			   = [0, 2_5, 5_0]
       __A			   = [2_5, 5_0, 7_5]
       __A			   = fuzz.membership.trimf(X, abca)
       __A			   = fuzz.membership.trimf(X, abca)

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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :       Union[str, Any]			   =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase :       Union[str, Any]			   =     [
      'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SEWForCTC',
      'SEWForSequenceClassification',
      'SEWModel',
      'SEWPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase :       int			   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






