





"""simple docstring"""




import random




class 					_a		:






				def __init__(					self						:       Union[str, Any],					lowerCAmelCase__						:       list[list[float]],					lowerCAmelCase__						:       list[int],					lowerCAmelCase__						:       float = 0.01,					lowerCAmelCase__						:       int = 1_0_0_0,					lowerCAmelCase__						:       float = -1,					)      -> None:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       sample
										if len(self.sample				) == 0:
																raise ValueError('''Sample data can not be empty'''				)
										_UpperCamelCase     :   Optional[int]      =       target
										if len(self.target				) == 0:
																raise ValueError('''Target data can not be empty'''				)
										if len(self.sample				) != len(self.target				):
																raise ValueError('''Sample data and Target data do not have matching lengths'''				)
										_UpperCamelCase     :   Any      =       learning_rate
										_UpperCamelCase     :   List[str]      =       epoch_number
										_UpperCamelCase     :   Union[str, Any]      =       bias
										_UpperCamelCase     :   Optional[int]      =       len(lowerCAmelCase__				)
										_UpperCamelCase     :   Optional[int]      =       len(sample[0]				)  # number of columns in dataset
										_UpperCamelCase     :   list      =       []






				def 							snake_case      (					self						:       int				)      -> None:




										'''simple docstring'''



										for sample in self.sample:
																sample.insert(0,					self.bias				)

										for _ in range(self.col_sample				):
																self.weight.append(random.random()				)

										self.weight.insert(0,					self.bias				)

										_UpperCamelCase     :   int      =       0

										while True:
																_UpperCamelCase     :   int      =       False
																for i in range(self.number_sample				):
																						_UpperCamelCase     :   Dict      =       0
																						for j in range(self.col_sample + 1				):
																												_UpperCamelCase     :   Dict      =       u + self.weight[j] * self.sample[i][j]
																						_UpperCamelCase     :   Union[str, Any]      =       self.sign(lowerCAmelCase__				)
																						if y != self.target[i]:
																												for j in range(self.col_sample + 1				):
																																		_UpperCamelCase     :   int      =       (
																																		    self.weight[j]
																																		    + self.learning_rate
																																		    * (self.target[i] - y)
																																		    * self.sample[i][j]
																																		)
																												_UpperCamelCase     :   Optional[int]      =       True
            # print('Epoch: \n',epoch_count)
																_UpperCamelCase     :   Dict      =       epoch_count + 1
																# if you want control the epoch or just by error
																if not has_misclassified:
																						print(('''\nEpoch:\n''', epoch_count)				)
																						print('''------------------------\n'''				)
																						# if epoch_count > self.epoch_number or not error:
																						break






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       list[float]				)      -> None:




										'''simple docstring'''



										if len(self.sample				) == 0:
																raise ValueError('''Sample data can not be empty'''				)
										sample.insert(0,					self.bias				)
										_UpperCamelCase     :   Tuple      =       0
										for i in range(self.col_sample + 1				):
																_UpperCamelCase     :   Optional[Any]      =       u + self.weight[i] * sample[i]

										_UpperCamelCase     :   Union[str, Any]      =       self.sign(lowerCAmelCase__				)

										if y == -1:
																print(('''Sample: ''', sample)				)
																print('''classification: P1'''				)
										else:
																print(('''Sample: ''', sample)				)
																print('''classification: P2'''				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       float				)      -> int:




										'''simple docstring'''



										return 1 if u >= 0 else -1

UpperCamelCase_	 =[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

UpperCamelCase_	 =[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						UpperCamelCase_	 =Perceptron(
						    sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
						)
						network.training()
						print("""Finished training perceptron""")
						print("""Enter values to predict or q to exit""")
						while True:
												UpperCamelCase_	 =[]
												for i in range(len(samples[0])):
																		UpperCamelCase_	 =input("""value: """).strip()
																		if user_input == "q":
																								break
																		UpperCamelCase_	 =float(user_input)
																		sample.insert(i, observation)
												network.sort(sample)





"""simple docstring"""

from __future__ import annotations

from queue import Queue
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		a ^= b
		b ^= a
		a ^= b
		return a, b
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       1
		while (1 << j) < max_node:
				for i in range(1,		max_node + 1     ):
						lowerCamelCase	:     Tuple            =       parent[j - 1][parent[j - 1][i]]
				j += 1
		return parent
def 						UpperCAmelCase   (						a_,		a_,		a_,		a_     ):





		'''simple docstring'''


		if level[u] < level[v]:
				lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       swap(a_,		a_     )
		# making depth of u same as depth of v
		for i in range(18,		-1,		-1     ):
				if level[u] - (1 << i) >= level[v]:
						lowerCamelCase	:     int            =       parent[i][u]
    # at the same depth if u==v that mean lca is found
		if u == v:
				return u
		# moving both nodes upwards till lca in found
		for i in range(18,		-1,		-1     ):
				if parent[i][u] not in [0, parent[i][v]]:
						lowerCamelCase      ,       lowerCamelCase	:     str            =       parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
		return parent[0][u]
def 						UpperCAmelCase   (						a_,		a_,		a_,		a_,		a_ = 1,		):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       0
		lowerCamelCase	:     Queue[int]            =       Queue(maxsize=a_     )
		q.put(a_     )
		while q.qsize() != 0:
				lowerCamelCase	:     Tuple            =       q.get()
				for v in graph[u]:
						if level[v] == -1:
								lowerCamelCase	:     Optional[Any]            =       level[u] + 1
								q.put(a_     )
								lowerCamelCase	:     str            =       u
		return level, parent
def 						UpperCAmelCase   (						):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       13
		# initializing with 0
		lowerCamelCase	:     Dict            =       [[0 for _ in range(max_node + 10     )] for _ in range(20     )]
		# initializing with -1 which means every node is unvisited
		lowerCamelCase	:     List[Any]            =       [-1 for _ in range(max_node + 10     )]
		lowerCamelCase	:     dict[int, list[int]]            =       {
		    1: [2, 3, 4],
		    2: [5],
		    3: [6, 7],
		    4: [8],
		    5: [9, 10],
		    6: [11],
		    7: [],
		    8: [12, 13],
		    9: [],
		    10: [],
		    11: [],
		    12: [],
		    13: [],
		}
		lowerCamelCase      ,       lowerCamelCase	:     Dict            =       breadth_first_search(a_,		a_,		a_,		a_,		1     )
		lowerCamelCase	:     List[Any]            =       create_sparse(a_,		a_     )
		print('LCA of node 1 and 3 is: ',		lowest_common_ancestor(1,		3,		a_,		a_     )     )
		print('LCA of node 5 and 6 is: ',		lowest_common_ancestor(5,		6,		a_,		a_     )     )
		print('LCA of node 7 and 11 is: ',		lowest_common_ancestor(7,		11,		a_,		a_     )     )
		print('LCA of node 6 and 7 is: ',		lowest_common_ancestor(6,		7,		a_,		a_     )     )
		print('LCA of node 4 and 12 is: ',		lowest_common_ancestor(4,		12,		a_,		a_     )     )
		print('LCA of node 8 and 8 is: ',		lowest_common_ancestor(8,		8,		a_,		a_     )     )


if __name__ == "__main__":
					main()






