






'''simple docstring'''


import numpy as np
import skfuzzy as fuzz

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

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

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						pyspark.sql.DataFrame ,      lowercase							:						Optional[NamedSplit] = None ,      lowercase							:						Optional[Features] = None ,      lowercase							:						bool = True ,      lowercase							:						str = None ,      lowercase							:						bool = False ,      lowercase							:						str = None ,      lowercase							:						bool = True ,      lowercase							:						str = "arrow" ,      **lowercase							:						Any ,      )							->    Optional[int]:

									'''simple docstring'''







									super().__init__(
									    split=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      keep_in_memory=lowercase ,      streaming=lowercase ,      **lowercase ,      )
									UpperCamelCase__         = load_from_cache_file
									UpperCamelCase__         = file_format
									UpperCamelCase__         = Spark(
									    df=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      working_dir=lowercase ,      **lowercase ,      )







			def 				A      (   self							:						int			)							->    Any:

									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split			)
									UpperCamelCase__         = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=lowercase ,      file_format=self._file_format ,      )
									return self.builder.as_dataset(split=self.split			)
