




'''simple docstring'''






import random



class  A		:




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = 0.01			,  _UpperCAmelCase = 1_0_0_0			,  _UpperCAmelCase = -1			,  )     ->	Optional[int]:
							__UpperCamelCase				:      int									=       sample
							if len(self.sample      ) == 0:
										raise ValueError("Sample data can not be empty"      )
							__UpperCamelCase				:      Any									=       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				:      str									=       epoch_number
							__UpperCamelCase				:      Union[str, Any]									=       bias
							__UpperCamelCase				:      Tuple									=       len(_A      )
							__UpperCamelCase				:      Any									=       len(sample[0]      )  # number of columns in dataset
							__UpperCamelCase				:      list									=       []
				def 	a_	(self      )     ->	List[str]:






							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				:      List[Any]									=       0

							while True:
										__UpperCamelCase				:      Tuple									=       False
										for i in range(self.number_sample      ):
													__UpperCamelCase				:      Union[str, Any]									=       0
													for j in range(self.col_sample + 1      ):
																__UpperCamelCase				:      str									=       u + self.weight[j] * self.sample[i][j]
													__UpperCamelCase				:      int									=       self.sign(_A      )
													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				:      Any									=       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 	a_	(self			,  _UpperCAmelCase      )     ->	Tuple:






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

							__UpperCamelCase				:      Union[str, Any]									=       self.sign(_A      )

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





				def 	a_	(self			,  _UpperCAmelCase      )     ->	Optional[Any]:
							return 1 if u >= 0 else -1







_lowerCAmelCase				     = [
    [-0.6_508, 0.1_097, 4.0_009],
    [-1.4_492, 0.8_896, 4.4_005],
    [2.0_850, 0.6_876, 12.0_710],
    [0.2_626, 1.1_476, 7.7_985],
    [0.6_418, 1.0_234, 7.0_427],
    [0.2_569, 0.6_730, 8.3_265],
    [1.1_155, 0.6_043, 7.4_446],
    [0.0_914, 0.3_399, 7.0_677],
    [0.0_121, 0.5_256, 4.6_316],
    [-0.0_429, 0.4_660, 5.4_323],
    [0.4_340, 0.6_870, 8.2_287],
    [0.2_735, 1.0_287, 7.1_934],
    [0.4_839, 0.4_851, 7.4_850],
    [0.4_089, -0.1_267, 5.5_019],
    [1.4_391, 0.1_614, 8.5_843],
    [-0.9_115, -0.1_973, 2.1_962],
    [0.3_654, 1.0_475, 7.4_858],
    [0.2_144, 0.7_515, 7.1_699],
    [0.2_013, 1.0_014, 6.5_489],
    [0.6_483, 0.2_183, 5.8_991],
    [-0.1_147, 0.2_242, 7.2_435],
    [-0.7_970, 0.8_795, 3.8_762],
    [-1.0_625, 0.6_366, 2.4_707],
    [0.5_307, 0.1_285, 5.6_883],
    [-1.2_200, 0.7_777, 1.7_252],
    [0.3_957, 0.1_076, 5.6_623],
    [-0.1_013, 0.5_989, 7.1_812],
    [2.4_482, 0.9_455, 11.2_095],
    [2.0_149, 0.6_192, 10.9_263],
    [0.2_012, 0.2_611, 5.4_631],
]

_lowerCAmelCase				     = [
    -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()

				_lowerCAmelCase				     = Perceptron(
				    sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
				)
				network.training()
				print('''Finished training perceptron''')
				print('''Enter values to predict or q to exit''')
				while True:
								_lowerCAmelCase				     = []
								for i in range(len(samples[0])):
												_lowerCAmelCase				     = input('''value: ''').strip()
												if user_input == "q":
																break
												_lowerCAmelCase				     = float(user_input)
												sample.insert(i, observation)
								network.sort(sample)





'''simple docstring'''






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

							if random.choice([True, False]      ):
										__UpperCamelCase		, __UpperCamelCase				:      Dict									=       roota.right, roota.left

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





				def __bool__(self      )     ->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()



