
import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 						UpperCAmelCase      (     a_		,       a_ = True		,       a_ = math.inf		,       a_ = -math.inf		,       a_ = math.inf		,       a_ = -math.inf		,       a_ = False		,       a_ = 1_0_0		,       a_ = 0.01		,       a_ = 1		,       )		->     Any:







				"""simple docstring"""



				__A             =			False
				__A             =			search_prob
				__A             =			start_temperate
				__A             =			[]
				__A             =			0
				__A             =			None

				while not search_end:
								__A             =			current_state.score()
								if best_state is None or current_score > best_state.score():
												__A             =			current_state
								scores.append(a_						)
								iterations += 1
								__A             =			None
								__A             =			current_state.get_neighbors()
								while (
								    next_state is None and neighbors
								):  # till we do not find a neighbor that we can move to
												__A             =			random.randint(0		,       len(a_						) - 1						)  # picking a random neighbor
												__A             =			neighbors.pop(a_						)
												__A             =			picked_neighbor.score() - current_score

												if (
												    picked_neighbor.x > max_x
												    or picked_neighbor.x < min_x
												    or picked_neighbor.y > max_y
												    or picked_neighbor.y < min_y
												):
																continue  # neighbor outside our bounds

												if not find_max:
																__A             =			change * -1  # in case we are finding minimum
												if change > 0:  # improves the solution
																__A             =			picked_neighbor
												else:
																__A             =			(math.e) ** (
																    change / current_temp
																)  # probability generation function
																if random.random() < probability:  # random number within probability
																				__A             =			picked_neighbor
								__A             =			current_temp - (current_temp * rate_of_decrease)

								if current_temp < threshold_temp or next_state is None:
												# temperature below threshold, or could not find a suitable neighbor
												__A             =			True
								else:
												__A             =			next_state

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(a_						)		,       a_						)
								plt.xlabel("Iterations"						)
								plt.ylabel("Function values"						)
								plt.show()
				return best_state


if __name__ == "__main__":


					def 						UpperCAmelCase      (     a_		,       a_						)		->     Optional[int]:







									"""simple docstring"""



									return (x**2) + (y**2)

					# starting the problem with initial coordinates (12, 47)
					SCREAMING_SNAKE_CASE							:Dict										=    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE							:Optional[int]										=    simulated_annealing(
					    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
					)
					print(
					    'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
					    f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
					)

					# starting the problem with initial coordinates (12, 47)
					SCREAMING_SNAKE_CASE							:Optional[Any]										=    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE							:Optional[Any]										=    simulated_annealing(
					    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
					)
					print(
					    'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
					    f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
					)


					def 						UpperCAmelCase      (     a_		,       a_						)		->     Any:







									"""simple docstring"""



									return (3 * x**2) - (6 * y)

					SCREAMING_SNAKE_CASE							:str										=    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE							:str										=    simulated_annealing(prob, find_max=False, visualization=True)
					print(
					    'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
					    f'''{local_min.score()}'''
					)

					SCREAMING_SNAKE_CASE							:Tuple										=    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE							:str										=    simulated_annealing(prob, find_max=True, visualization=True)
					print(
					    'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
					    f'''{local_min.score()}'''
					)


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				__A      ,							__A             =			analyze_text(a_						)
				__A             =			list(" " + ascii_lowercase						)
				# what is our total sum of probabilities.
				__A             =			sum(single_char_strings.values()						)

				# one length string
				__A             =			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:
												__A             =			single_char_strings[ch]
												__A             =			my_str / all_sum
												my_fir_sum += prob * math.loga(a_						)  # entropy formula.

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

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

    # 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 						UpperCAmelCase      (     a_						)		->     tuple[dict, dict]:







				"""simple docstring"""



				__A             =			Counter()  # type: ignore
				__A             =			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(a_						) - 1						):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings


def 						UpperCAmelCase      (     )		->     Optional[Any]:







				"""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()
