from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
_SCREAMING_SNAKE_CASE      						=	Lock()





def        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )						->     Tuple:







 '''simple docstring'''
 global process_lock

 # we perform n swaps since after n swaps we know we are sorted
 # we *could* stop early if we are sorted already, but it takes as long to
 # find out we are sorted as it does to sort the list with this algorithm
 for i in range(0				, 10    ):
  if (i + position) % 2 == 0 and r_send is not None:
   # send your value to your right neighbor
   process_lock.acquire()
   r_send[1].send(UpperCamelCase_    )
   process_lock.release()

   # receive your right neighbor's value
   process_lock.acquire()
   UpperCamelCase          =	rr_cv[0].recv()
   process_lock.release()

   # take the lower value since you are on the left
   UpperCamelCase          =	min(UpperCamelCase_				, UpperCamelCase_    )
  elif (i + position) % 2 != 0 and l_send is not None:
   # send your value to your left neighbor
   process_lock.acquire()
   l_send[1].send(UpperCamelCase_    )
   process_lock.release()

   # receive your left neighbor's value
   process_lock.acquire()
   UpperCamelCase          =	lr_cv[0].recv()
   process_lock.release()

   # take the higher value since you are on the right
   UpperCamelCase          =	max(UpperCamelCase_				, UpperCamelCase_    )
    # after all swaps are performed, send the values back to main
 result_pipe[1].send(UpperCamelCase_    )





def        lowercase(     UpperCamelCase_    )						->     Union[str, Any]:







 '''simple docstring'''
 UpperCamelCase          =	[]
 UpperCamelCase          =	[]
 # initialize the list of pipes where the values will be retrieved
 for _ in arr:
  result_pipe.append(Pipe()    )
 # creates the processes
 # the first and last process only have one neighbor so they are made outside
 # of the loop
 UpperCamelCase          =	Pipe()
 UpperCamelCase          =	Pipe()
 process_array_.append(
     Process(
         target=UpperCamelCase_				, args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])				, )    )
 UpperCamelCase          =	temp_rs
 UpperCamelCase          =	temp_rr

 for i in range(1				, len(UpperCamelCase_    ) - 1    ):
  UpperCamelCase          =	Pipe()
  UpperCamelCase          =	Pipe()
  process_array_.append(
      Process(
          target=UpperCamelCase_				, args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])				, )    )
  UpperCamelCase          =	temp_rs
  UpperCamelCase          =	temp_rr

 process_array_.append(
     Process(
         target=UpperCamelCase_				, args=(
             len(UpperCamelCase_    ) - 1,
             arr[len(UpperCamelCase_    ) - 1],
             temp_ls,
             None,
             temp_lr,
             None,
             result_pipe[len(UpperCamelCase_    ) - 1],
         )				, )    )

 # start the processes
 for p in process_array_:
  p.start()

 # wait for the processes to end and write their values to the list
 for p in range(0				, len(UpperCamelCase_    )    ):
  UpperCamelCase          =	result_pipe[p][0].recv()
  process_array_[p].join()
 return arr





def        lowercase(     )						->     List[Any]:







 '''simple docstring'''
 UpperCamelCase          =	list(range(10				, 0				, -1    )    )
 print("""Initial List"""    )
 print(*UpperCamelCase_    )
 UpperCamelCase          =	odd_even_transposition(UpperCamelCase_    )
 print("""Sorted List\n"""    )
 print(*UpperCamelCase_    )


if __name__ == "__main__":
     main()

"""simple docstring"""






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def   a_						(					lowerCamelCase       ):
				return np.dot(lowerCamelCase					,			lowerCamelCase       )
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	int    ,*,
					    lowerCamelCase__   :	float = np.inf    ,lowerCamelCase__   :	str = "linear"    ,lowerCamelCase__   :	float = 0.0    ,):
									UpperCAmelCase__             =							regularization
									UpperCAmelCase__             =							gamma
									if kernel == "linear":
													UpperCAmelCase__             =							self.__linear
									elif kernel == "rbf":
													if self.gamma == 0:
																	raise ValueError('rbf kernel requires gamma'			)
													if not isinstance(self.gamma    ,(float, int)			):
																	raise ValueError('gamma must be float or int'			)
													if not self.gamma > 0:
																	raise ValueError('gamma must be > 0'			)
													UpperCAmelCase__             =							self.__rbf
													# in the future, there could be a default value like in sklearn
													# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
													# previously it was 1/(n_features)
									else:
													UpperCAmelCase__             =							f'''Unknown kernel: {kernel}'''
													raise ValueError(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.dot(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	list[ndarray]    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							observations
									UpperCAmelCase__             =							classes

									# using Wolfe's Dual to calculate w.
									# Primal problem: minimize 1/2*norm_squared(w)
									#   constraint: yn(w . xn + b) >= 1
									#
									# With l a vector
									# Dual problem: maximize sum_n(ln) -
									#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
									#   constraint: self.C >= ln >= 0
									#           and sum_n(ln*yn) = 0
									# Then we get w using w = sum_n(ln*yn*xn)
									# At the end we can get b ~= mean(yn - w . xn)
									#
									# Since we use kernels, we only need l_star to calculate b
									# and to classify observations

									((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)

									def to_minimize(lowerCamelCase__   :	ndarray			) -> float:
													UpperCAmelCase__             =							0
													((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)
													for i in range(lowerCamelCase__			):
																	for j in range(lowerCamelCase__			):
																					s += (
																					    candidate[i]
																					    * candidate[j]
																					    * classes[i]
																					    * classes[j]
																					    * self.kernel(observations[i]    ,observations[j]			)
																					)
													return 1 / 2 * s - sum(lowerCamelCase__			)

									UpperCAmelCase__             =							LinearConstraint(lowerCamelCase__    ,0    ,0			)
									UpperCAmelCase__             =							Bounds(0    ,self.regularization			)

									UpperCAmelCase__             =							minimize(
									    lowerCamelCase__    ,np.ones(lowerCamelCase__			)    ,bounds=lowerCamelCase__    ,constraints=[ly_contraint]			).x
									UpperCAmelCase__             =							l_star

									# calculating mean offset of separation plane to points
									UpperCAmelCase__             =							0
									for i in range(lowerCamelCase__			):
													for j in range(lowerCamelCase__			):
																	s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																	    observations[i]    ,observations[j]			)
									UpperCAmelCase__             =							s / n







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]    ,lowerCamelCase__			)
									    for n in range(len(self.classes			)			)			)
									return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()



