



"""simple docstring"""


import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
					# Create universe of discourse in Python using linspace ()
					__A :				Optional[int]					    = 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 :				List[Any]					    = [0, 25, 50]
					__A :				Any					    = [25, 50, 75]
					__A :				Tuple					    = fuzz.membership.trimf(X, abca)
					__A :				int					    = fuzz.membership.trimf(X, abca)

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

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







import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
     from scipy.stats import pearsonr, spearmanr
     from sklearn.metrics import fa_score, matthews_corrcoef


snake_case							:       List[str]										=	(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      return (preds == labels).mean()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:Dict									=			simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)
      a		:Any									=			fa_score(y_true=UpperCAmelCase_					,    y_pred=UpperCAmelCase_		)
      return {
          "acc": acc,
          "f1": fa,
          "acc_and_f1": (acc + fa) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:str									=			pearsonr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      a		:Union[str, Any]									=			spearmanr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
          "corr": (pearson_corr + spearman_corr) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      assert len(UpperCAmelCase_		) == len(UpperCAmelCase_		), F'''Predictions and labels have mismatched lengths {len(UpperCAmelCase_		)} and {len(UpperCAmelCase_		)}'''
      if task_name == "cola":
            return {"mcc": matthews_corrcoef(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "sst-2":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "mrpc":
            return acc_and_fa(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "sts-b":
            return pearson_and_spearman(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "qqp":
            return acc_and_fa(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "mnli":
            return {"mnli/acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "mnli-mm":
            return {"mnli-mm/acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "qnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "rte":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "wnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "hans":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      else:
            raise KeyError(UpperCAmelCase_		)






def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      if len(UpperCAmelCase_		) != len(UpperCAmelCase_		):
            raise ValueError(F'''Predictions and labels have mismatched lengths {len(UpperCAmelCase_		)} and {len(UpperCAmelCase_		)}'''		)
      if task_name == "xnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      else:
            raise KeyError(UpperCAmelCase_		)





