
import random







class 			UpperCamelCase__		:







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__ = 0.0_1					,							UpperCamelCase__ = 1000					,							UpperCamelCase__ = -1					,							):
					A__	:						List[str]								=				sample
					if len(self.sample		) == 0:
									raise ValueError('''Sample data can not be empty'''		)
					A__	:						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'''		)
					A__	:						Dict								=				learning_rate
					A__	:						Union[str, Any]								=				epoch_number
					A__	:						Tuple								=				bias
					A__	:						int								=				len(UpperCamelCase__		)
					A__	:						str								=				len(sample[0]		)  # number of columns in dataset
					A__	:						list								=				[]





	def 	__snake_case					(      self		):
					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		)

					A__	:						Any								=				0

					while True:
									A__	:						int								=				False
									for i in range(self.number_sample		):
													A__	:						Optional[Any]								=				0
													for j in range(self.col_sample + 1		):
																	A__	:						Tuple								=				u + self.weight[j] * self.sample[i][j]
													A__	:						List[Any]								=				self.sign(UpperCamelCase__		)
													if y != self.target[i]:
																	for j in range(self.col_sample + 1		):
																					A__	:						Optional[Any]								=				(
																					    self.weight[j]
																					    + self.learning_rate
																					    * (self.target[i] - y)
																					    * self.sample[i][j]
																					)
																	A__	:						int								=				True
            # print('Epoch: \n',epoch_count)
									A__	:						str								=				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					,							UpperCamelCase__		):
					if len(self.sample		) == 0:
									raise ValueError('''Sample data can not be empty'''		)
					sample.insert(0					,							self.bias		)
					A__	:						Optional[Any]								=				0
					for i in range(self.col_sample + 1		):
									A__	:						int								=				u + self.weight[i] * sample[i]

					A__	:						Any								=				self.sign(UpperCamelCase__		)

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





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					return 1 if u >= 0 else -1




_SCREAMING_SNAKE_CASE     :   Any							 =      [
    [-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],
]

_SCREAMING_SNAKE_CASE     :   Union[str, Any]							 =      [
    -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()

					_SCREAMING_SNAKE_CASE     :   Any							 =      Perceptron(
					    sample=samples, target=target, learning_rate=0.01, epoch_number=1_0_0_0, bias=-1
					)
					network.training()
					print('Finished training perceptron')
					print('Enter values to predict or q to exit')
					while True:
										_SCREAMING_SNAKE_CASE     :   list							 =      []
										for i in range(len(samples[0])):
															_SCREAMING_SNAKE_CASE     :   str							 =      input('value: ').strip()
															if user_input == "q":
																				break
															_SCREAMING_SNAKE_CASE     :   Optional[int]							 =      float(user_input)
															sample.insert(i, observation)
										network.sort(sample)

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652E26,
}

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660540199E-27,
}

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    float							)			->							float:

				"""simple docstring"""







				if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
								A__	:						int								=				(
								    F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
								    F"Supported values are: {', '.join(__UpperCamelCase							)}"
								)
								raise ValueError(__UpperCamelCase							)
				return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()