



"""simple docstring"""


import random







class    A_    :



						"""simple docstring"""






						def __init__(      self				:Any     ,						lowercase_				:Dict     ,						lowercase_				:Tuple     ,						lowercase_				:Any = 0.01     ,						lowercase_				:Optional[int] = 10_00     ,						lowercase_				:str = -1     ,						)       ->    None:
												UpperCAmelCase    =				sample
												if len(self.sample      ) == 0:
																		raise ValueError('Sample data can not be empty'      )
												UpperCAmelCase    =				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    =				learning_rate
												UpperCAmelCase    =				epoch_number
												UpperCAmelCase    =				bias
												UpperCAmelCase    =				len(lowercase_      )
												UpperCAmelCase    =				len(sample[0]      )  # number of columns in dataset
												UpperCAmelCase    =				[]






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    None:






												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    =				0

												while True:
																		UpperCAmelCase    =				False
																		for i in range(self.number_sample      ):
																								UpperCAmelCase    =				0
																								for j in range(self.col_sample + 1      ):
																														UpperCAmelCase    =				u + self.weight[j] * self.sample[i][j]
																								UpperCAmelCase    =				self.sign(lowercase_      )
																								if y != self.target[i]:
																														for j in range(self.col_sample + 1      ):
																																				UpperCAmelCase    =				(
																																				    self.weight[j]
																																				    + self.learning_rate
																																				    * (self.target[i] - y)
																																				    * self.sample[i][j]
																																				)
																														UpperCAmelCase    =				True
																								# print('Epoch: \n',epoch_count)
																		UpperCAmelCase    =				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 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:int      )       ->    None:






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

												UpperCAmelCase    =				self.sign(lowercase_      )

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






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						lowercase_				:List[str]      )       ->    int:
												return 1 if u >= 0 else -1


snake_case_         = [
    [-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],
]

snake_case_         = [
    -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()

				snake_case_         = 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:
								snake_case_         = []
								for i in range(len(samples[0])):
												snake_case_         = input("""value: """).strip()
												if user_input == "q":
																break
												snake_case_         = float(user_input)
												sample.insert(i, observation)
								network.sort(sample)










"""simple docstring"""


from math import exp

def 				_lowerCAmelCase   (     lowercase_		):
						return 8 * x - 2 * exp(-x		)

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_		):
						UpperCAmelCase    =				lower_bound
						UpperCAmelCase    =				upper_bound
						for _ in range(0	,			lowercase_		):
												UpperCAmelCase   ,					UpperCAmelCase    =				xa, xa - (f(lowercase_		) * (xa - xa)) / (f(lowercase_		) - f(lowercase_		))
						return xa


if __name__ == "__main__":
				print(f'''Example: {secant_method(1, 3, 2)}''')





