import numpy as np
import skfuzzy as fuzz

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

					# Compute the different operations using inbuilt functions.
					__UpperCamelCase	: Tuple    		=  np.ones(75)
					__UpperCamelCase	: Optional[int]    		=  np.zeros((75,))
					# 1. Union = max(µA(x), µB(x))
					__UpperCamelCase	: str    		=  fuzz.fuzzy_or(X, young, X, middle_aged)[1]
					# 2. Intersection = min(µA(x), µB(x))
					__UpperCamelCase	: Tuple    		=  fuzz.fuzzy_and(X, young, X, middle_aged)[1]
					# 3. Complement (A) = (1- min(µA(x))
					__UpperCamelCase	: int    		=  fuzz.fuzzy_not(young)
					# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
					__UpperCamelCase	: 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))]
					__UpperCamelCase	: Union[str, Any]    		=  young + middle_aged - (young * middle_aged)
					# 6. Algebraic Product = (µA(x) * µB(x))
					__UpperCamelCase	: Optional[int]    		=  young * middle_aged
					# 7. Bounded Sum = min[1,(µA(x), µB(x))]
					__UpperCamelCase	: Any    		=  fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
					# 8. Bounded difference = min[0,(µA(x), µB(x))]
					__UpperCamelCase	: List[str]    		=  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    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							analyze_text(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							list(''' ''' + ascii_lowercase		)
						# what is our total sum of probabilities.
						SCREAMING_SNAKE_CASE							:					Tuple           =							sum(single_char_strings.values()		)

						# one length string
						SCREAMING_SNAKE_CASE							:					Tuple           =							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:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							single_char_strings[ch]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							my_str / all_sum
																		my_fir_sum += prob * math.loga(_lowercase		)  # entropy formula.

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

						# two len string
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							sum(two_char_strings.values()		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							0
						# for each alpha (two in size) calculate entropy.
						for cha in my_alphas:
												for cha in my_alphas:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							cha + cha
																		if sequence in two_char_strings:
																								SCREAMING_SNAKE_CASE							:					Any           =							two_char_strings[sequence]
																								SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		) / all_sum
																								my_sec_sum += prob * math.loga(_lowercase		)

    # 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    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							Counter()  # type: ignore
						SCREAMING_SNAKE_CASE							:					Any           =							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(_lowercase		) - 1		):
												single_char_strings[text[i]] += 1
												two_char_strings[text[i : i + 2]] += 1
						return single_char_strings, two_char_strings
def    A			(					):
						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()





