
'''simple docstring'''






import numpy as np
import skfuzzy as fuzz

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

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





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__lowerCamelCase									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

    # print entropy
				print(F"""{round(-1 * my_fir_sum ):.1f}""" )

				# two len string
				__lowerCamelCase									=       sum(two_char_strings.values() )
				__lowerCamelCase									=       0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__lowerCamelCase									=       cha + cha
												if sequence in two_char_strings:
																__lowerCamelCase									=       two_char_strings[sequence]
																__lowerCamelCase									=       int(UpperCamelCase__ ) / all_sum
																my_sec_sum += prob * math.loga(UpperCamelCase__ )

    # print second entropy
				print(F"""{round(-1 * my_sec_sum ):.1f}""" )

				# print the difference between them
				print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0					,	len(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()


