
'''simple docstring'''
import random






class   snake_case__					:







		"""simple docstring"""

		def __init__(			self							: List[str]     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Any = 0.01     ,  UpperCamelCase__							: List[Any] = 1000     ,  UpperCamelCase__							: Dict = -1     ,  )	-> None:






				"""simple docstring"""







				snake_case     :					Dict     =	sample
				if len(self.sample			) == 0:
						raise ValueError('''Sample data can not be empty'''			)
				snake_case     :					Tuple     =	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'''			)
				snake_case     :					List[Any]     =	learning_rate
				snake_case     :					Union[str, Any]     =	epoch_number
				snake_case     :					Any     =	bias
				snake_case     :					str     =	len(UpperCamelCase__			)
				snake_case     :					Tuple     =	len(sample[0]			)  # number of columns in dataset
				snake_case     :					list     =	[]



		def 			lowerCAmelCase							(			self							: str			)	-> 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			)

				snake_case     :					Tuple     =	0

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






				"""simple docstring"""







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

				snake_case     :					Optional[int]     =	self.sign(UpperCamelCase__			)

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



		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: Optional[Any]			)	-> int:






				"""simple docstring"""







				return 1 if u >= 0 else -1







lowercase__						=			[
    [-0.65_08, 0.10_97, 4.00_09],
    [-1.44_92, 0.88_96, 4.40_05],
    [2.08_50, 0.68_76, 12.07_10],
    [0.26_26, 1.14_76, 7.79_85],
    [0.64_18, 1.02_34, 7.04_27],
    [0.25_69, 0.67_30, 8.32_65],
    [1.11_55, 0.60_43, 7.44_46],
    [0.09_14, 0.33_99, 7.06_77],
    [0.01_21, 0.52_56, 4.63_16],
    [-0.04_29, 0.46_60, 5.43_23],
    [0.43_40, 0.68_70, 8.22_87],
    [0.27_35, 1.02_87, 7.19_34],
    [0.48_39, 0.48_51, 7.48_50],
    [0.40_89, -0.12_67, 5.50_19],
    [1.43_91, 0.16_14, 8.58_43],
    [-0.91_15, -0.19_73, 2.19_62],
    [0.36_54, 1.04_75, 7.48_58],
    [0.21_44, 0.75_15, 7.16_99],
    [0.20_13, 1.00_14, 6.54_89],
    [0.64_83, 0.21_83, 5.89_91],
    [-0.11_47, 0.22_42, 7.24_35],
    [-0.79_70, 0.87_95, 3.87_62],
    [-1.06_25, 0.63_66, 2.47_07],
    [0.53_07, 0.12_85, 5.68_83],
    [-1.22_00, 0.77_77, 1.72_52],
    [0.39_57, 0.10_76, 5.66_23],
    [-0.10_13, 0.59_89, 7.18_12],
    [2.44_82, 0.94_55, 11.20_95],
    [2.01_49, 0.61_92, 10.92_63],
    [0.20_12, 0.26_11, 5.46_31],
]

lowercase__						=			[
    -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()

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




'''simple docstring'''
def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       int:


		'''simple docstring'''


		snake_case     :					Optional[Any]     =	0
		for ele in arr:
				total_sum += product_sum(SCREAMING_SNAKE_CASE__   ,					depth + 1 ) if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) else ele
		return total_sum * depth




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:


		'''simple docstring'''


		return product_sum(SCREAMING_SNAKE_CASE__   ,					1 )


if __name__ == "__main__":
						import doctest

						doctest.testmod()


