from __future__ import annotations

import queue
class SCREAMING_SNAKE_CASE_						:







   def __init__(			self		:		int					,					lowerCamelCase_		:		List[str]							):





    """simple docstring"""


    UpperCamelCase          =	data
    UpperCamelCase          =	None
    UpperCamelCase          =	None





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







 '''simple docstring'''
 print("""\n********Press N to stop entering at any point of time********\n"""    )
 UpperCamelCase          =	input("""Enter the value of the root node: """    ).strip().lower()
 UpperCamelCase          =	queue.Queue()
 UpperCamelCase          =	TreeNode(int(UpperCamelCase_    )    )
 q.put(UpperCamelCase_    )
 while not q.empty():
  UpperCamelCase          =	q.get()
  UpperCamelCase          =	f"""Enter the left node of {node_found.data}: """
  UpperCamelCase          =	input(UpperCamelCase_    ).strip().lower() or """n"""
  if check == "n":
   return tree_node
  UpperCamelCase          =	TreeNode(int(UpperCamelCase_    )    )
  UpperCamelCase          =	left_node
  q.put(UpperCamelCase_    )
  UpperCamelCase          =	f"""Enter the right node of {node_found.data}: """
  UpperCamelCase          =	input(UpperCamelCase_    ).strip().lower() or """n"""
  if check == "n":
   return tree_node
  UpperCamelCase          =	TreeNode(int(UpperCamelCase_    )    )
  UpperCamelCase          =	right_node
  q.put(UpperCamelCase_    )
 raise





def        lowercase(     UpperCamelCase_    )						->     str:







 '''simple docstring'''
 if not isinstance(UpperCamelCase_				, UpperCamelCase_    ) or not node:
  return
 print(node.data				, end=""","""    )
 pre_order(node.left    )
 pre_order(node.right    )





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







 '''simple docstring'''
 if not isinstance(UpperCamelCase_				, UpperCamelCase_    ) or not node:
  return
 in_order(node.left    )
 print(node.data				, end=""","""    )
 in_order(node.right    )





def        lowercase(     UpperCamelCase_    )						->     List[str]:







 '''simple docstring'''
 if not isinstance(UpperCamelCase_				, UpperCamelCase_    ) or not node:
  return
 post_order(node.left    )
 post_order(node.right    )
 print(node.data				, end=""","""    )





def        lowercase(     UpperCamelCase_    )						->     Optional[int]:







 '''simple docstring'''
 if not isinstance(UpperCamelCase_				, UpperCamelCase_    ) or not node:
  return
 UpperCamelCase          =	queue.Queue()
 q.put(UpperCamelCase_    )
 while not q.empty():
  UpperCamelCase          =	q.get()
  print(node_dequeued.data				, end=""","""    )
  if node_dequeued.left:
   q.put(node_dequeued.left    )
  if node_dequeued.right:
   q.put(node_dequeued.right    )





def        lowercase(     UpperCamelCase_    )						->     int:







 '''simple docstring'''
 if not isinstance(UpperCamelCase_				, UpperCamelCase_    ) or not node:
  return
 UpperCamelCase          =	queue.Queue()
 q.put(UpperCamelCase_    )
 while not q.empty():
  UpperCamelCase          =	[]
  while not q.empty():
   UpperCamelCase          =	q.get()
   print(node_dequeued.data				, end=""","""    )
   if node_dequeued.left:
    list_.append(node_dequeued.left    )
   if node_dequeued.right:
    list_.append(node_dequeued.right    )
  print()
  for node in list_:
   q.put(UpperCamelCase_    )





def        lowercase(     UpperCamelCase_    )						->     Optional[int]:







 '''simple docstring'''
 if not isinstance(UpperCamelCase_				, UpperCamelCase_    ) or not node:
  return
 UpperCamelCase          =	[]
 UpperCamelCase          =	node
 while n or stack:
  while n:  # start from root node, find its left child
   print(n.data				, end=""","""    )
   stack.append(UpperCamelCase_    )
   UpperCamelCase          =	n.left
  # end of while means current node doesn't have left child
  UpperCamelCase          =	stack.pop()
  # start to traverse its right child
  UpperCamelCase          =	n.right





def        lowercase(     UpperCamelCase_    )						->     Optional[Any]:







 '''simple docstring'''
 if not isinstance(UpperCamelCase_				, UpperCamelCase_    ) or not node:
  return
 UpperCamelCase          =	[]
 UpperCamelCase          =	node
 while n or stack:
  while n:
   stack.append(UpperCamelCase_    )
   UpperCamelCase          =	n.left
  UpperCamelCase          =	stack.pop()
  print(n.data				, end=""","""    )
  UpperCamelCase          =	n.right





def        lowercase(     UpperCamelCase_    )						->     Dict:







 '''simple docstring'''
 if not isinstance(UpperCamelCase_				, UpperCamelCase_    ) or not node:
  return
 UpperCamelCase   ,			UpperCamelCase          =	[], []
 UpperCamelCase          =	node
 stacka.append(UpperCamelCase_    )
 while stacka:  # to find the reversed order of post order, store it in stack2
  UpperCamelCase          =	stacka.pop()
  if n.left:
   stacka.append(n.left    )
  if n.right:
   stacka.append(n.right    )
  stacka.append(UpperCamelCase_    )
 while stacka:  # pop up from stack2 will be the post order
  print(stacka.pop().data				, end=""","""    )





def        lowercase(     UpperCamelCase_ = ""				, UpperCamelCase_=50				, UpperCamelCase_="*"    )						->     Any:







 '''simple docstring'''
 if not s:
  return "\n" + width * char
 UpperCamelCase   ,			UpperCamelCase          =	divmod(width - len(UpperCamelCase_    ) - 2				, 2    )
 return f"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(prompt("""Binary Tree Traversals"""))

     _SCREAMING_SNAKE_CASE      						=	build_tree()
     print(prompt("""Pre Order Traversal"""))
     pre_order(node)
     print(prompt() + """\n""")

     print(prompt("""In Order Traversal"""))
     in_order(node)
     print(prompt() + """\n""")

     print(prompt("""Post Order Traversal"""))
     post_order(node)
     print(prompt() + """\n""")

     print(prompt("""Level Order Traversal"""))
     level_order(node)
     print(prompt() + """\n""")

     print(prompt("""Actual Level Order Traversal"""))
     level_order_actual(node)
     print("""*""" * 5_0 + """\n""")

     print(prompt("""Pre Order Traversal - Iteration Version"""))
     pre_order_iter(node)
     print(prompt() + """\n""")

     print(prompt("""In Order Traversal - Iteration Version"""))
     in_order_iter(node)
     print(prompt() + """\n""")

     print(prompt("""Post Order Traversal - Iteration Version"""))
     post_order_iter(node)
     print(prompt())

"""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()



