

"""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=75, num=75, endpoint=True, retstep=False)

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

    # Compute the different operations using inbuilt functions.
    _A							=  np.ones(75)
    _A							=  np.zeros((75,))
    # 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, 10)
    plt.plot(X, bdd_difference)
    plt.title("""bdd_difference""")
    plt.grid(True)

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




"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels

