





import random

class      A_	:
	def __init__(   self							, _A							, _A							, _A = 0.01							, _A = 1_0_0_0							, _A = -1							, ):






				'''simple docstring'''







				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(_A		)
				UpperCAmelCase       		=    len(sample[0]		)  # number of columns in dataset
				UpperCAmelCase       		=    []
	def 					_lowercase    (   self		):






				'''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		)

				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(_A		)
										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 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				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(_A		)

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







	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				return 1 if u >= 0 else -1
__A    :   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],
]

__A    :   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()

				__A    :   Dict          =       Perceptron(
				    sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
				)
				network.training()
				print("Finished training perceptron")
				print("Enter values to predict or q to exit")
				while True:
								__A    :   list          =       []
								for i in range(len(samples[0])):
												__A    :   Optional[int]          =       input("value: ").strip()
												if user_input == "q":
																break
												__A    :   Any          =       float(user_input)
												sample.insert(i, observation)
								network.sort(sample)










import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_utf16_encoded.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''      ,      encoding='''utf-16'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts_field.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Dict:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}






@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			),
            },
        ),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}



