



'''simple docstring'''



import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						bool = True				,		lowerCamelCase   :						float = math.inf				,		lowerCamelCase   :						float = -math.inf				,		lowerCamelCase   :						float = math.inf				,		lowerCamelCase   :						float = -math.inf				,		lowerCamelCase   :						bool = False				,		lowerCamelCase   :						float = 1_00				,		lowerCamelCase   :						float = 0.0_1				,		lowerCamelCase   :						float = 1				,		):






 '''simple docstring'''




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

 while not search_end:
  __lowerCAmelCase												=  current_state.score()
  if best_state is None or current_score > best_state.score():
   __lowerCAmelCase												=  current_state
  scores.append(_lowerCAmelCase					)
  iterations += 1
  __lowerCAmelCase												=  None
  __lowerCAmelCase												=  current_state.get_neighbors()
  while (
      next_state is None and neighbors
  ):  # till we do not find a neighbor that we can move to
   __lowerCAmelCase												=  random.randint(0				,		len(_lowerCAmelCase					) - 1					)  # picking a random neighbor
   __lowerCAmelCase												=  neighbors.pop(_lowerCAmelCase					)
   __lowerCAmelCase												=  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:
    __lowerCAmelCase												=  change * -1  # in case we are finding minimum
   if change > 0:  # improves the solution
    __lowerCAmelCase												=  picked_neighbor
   else:
    __lowerCAmelCase												=  (math.e) ** (
        change / current_temp
    )  # probability generation function
    if random.random() < probability:  # random number within probability
     __lowerCAmelCase												=  picked_neighbor
  __lowerCAmelCase												=  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
   __lowerCAmelCase												=  True
  else:
   __lowerCAmelCase												=  next_state

 if visualization:
  from matplotlib import pyplot as plt

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


if __name__ == "__main__":



 def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						List[Any]					):






  '''simple docstring'''




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

 # starting the problem with initial coordinates (12, 47)
 lowerCAmelCase	:		str				=   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
 lowerCAmelCase	:		List[Any]				=   simulated_annealing(
     prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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)
 lowerCAmelCase	:		Optional[Any]				=   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
 lowerCAmelCase	:		Dict				=   simulated_annealing(
     prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						Dict					):






  '''simple docstring'''




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

 lowerCAmelCase	:		Union[str, Any]				=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 lowerCAmelCase	:		Dict				=   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()}'
 )

 lowerCAmelCase	:		str				=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 lowerCAmelCase	:		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()}'
 )




'''simple docstring'''



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase	:		List[str]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Dict[Optional[str], Type[Formatter]]				=   {}
lowerCAmelCase	:		Dict[Optional[str], str]				=   {}
lowerCAmelCase	:		Dict[Optional[str], Exception]				=   {}



def   __lowerCAmelCase					(							lowerCamelCase   :						type				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''					)
	__lowerCAmelCase												=  formatter_cls
	for alias in set(aliases + [format_type]					):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''					)
		__lowerCAmelCase												=  format_type



def   __lowerCAmelCase					(							lowerCamelCase   :						Exception				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None					):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	for alias in set(aliases + [format_type]					):
		__lowerCAmelCase												=  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	lowerCAmelCase	:		Optional[int]				=   ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	lowerCAmelCase	:		str				=   ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	lowerCAmelCase	:		Any				=   ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]					):






	'''simple docstring'''




	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]				,		**lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_format_type_from_alias(lowerCamelCase					)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowerCamelCase					)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None					)}, but got \'{format_type}\''''					)