
import random


class 		__magic_name__   :





		def __init__(							self    :	str						,  lowerCamelCase__    :	list[list[float]]						,  lowerCamelCase__    :	list[int]						,  lowerCamelCase__    :	float = 0.0_1						,  lowerCamelCase__    :	int = 1_0_0_0						,  lowerCamelCase__    :	float = -1						,  ):
							lowerCAmelCase		:					Optional[int]  	=     sample
							if len(self.sample				) == 0:
												raise ValueError('''Sample data can not be empty'''				)
							lowerCAmelCase		:					List[str]  	=     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'''				)
							lowerCAmelCase		:					Optional[int]  	=     learning_rate
							lowerCAmelCase		:					str  	=     epoch_number
							lowerCAmelCase		:					List[Any]  	=     bias
							lowerCAmelCase		:					Dict  	=     len(lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, Any]  	=     len(sample[0]				)  # number of columns in dataset
							lowerCAmelCase		:					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				)

							lowerCAmelCase		:					List[Any]  	=     0

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

							lowerCAmelCase		:					Optional[Any]  	=     self.sign(lowerCamelCase__				)

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





		def       _A							(							self    :	Dict						,  lowerCamelCase__    :	float				):
							return 1 if u >= 0 else -1



__SCREAMING_SNAKE_CASE			:   str         =     [
    [-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],
]

__SCREAMING_SNAKE_CASE			:   Dict         =     [
    -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			:   List[str]         =     Perceptron(
		    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, 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			:   int         =     input('value: ').strip()
						if user_input == "q":
								break
						__SCREAMING_SNAKE_CASE			:   str         =     float(user_input)
						sample.insert(i, observation)
				network.sort(sample)


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__SCREAMING_SNAKE_CASE			:   List[Any]         =     10_00_00_00_00_00
__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     1_00_00

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Any         =     os.path.split(__file__)
__SCREAMING_SNAKE_CASE			:   List[str]         =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[int]  	=     pa.Table.from_pydict({'''col''': [0] * chunk_size}  )
					lowerCAmelCase		:					int  	=     pa.concat_tables([table] * (num_examples // chunk_size)  )
					return datasets.Dataset(__magic_name__							,  fingerprint='''table_100B'''  )


@dataclass
class 		__magic_name__   :
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Union[str, Any]  	=     np.random.default_rng(self.seed				)
							lowerCAmelCase		:					Tuple  	=     rng.integers(low=self.low						,  high=self.high						,  size=self.size				).tolist()





		def __iter__(							self    :	Dict				):
							return iter(self._sampled_values				)





		def __len__(							self    :	Tuple				):
							return self.size



@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     dataset[0]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     dataset[-1]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     dataset[range(len(__magic_name__  ) // 2							,  len(__magic_name__  ) // 2 + 10_24  )]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[Any]  	=     dataset[RandIter(0							,  len(__magic_name__  )							,  10_24							,  seed=42  )]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
					lowerCAmelCase		:					Tuple  	=     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('''generating dataset'''  )
					lowerCAmelCase		:					Dict  	=     generate_100B_dataset(num_examples=__magic_name__							,  chunk_size=__magic_name__  )
					print('''Functions'''  )
					for func in functions:
										print(func.__name__  )
										lowerCAmelCase		:					List[Any]  	=     func(__magic_name__  )

					with open(__magic_name__							,  '''wb'''  ) as f:
										f.write(json.dumps(__magic_name__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()
