






import random

class      UpperCamelCase_							:




						def __init__(    self		,	lowercase		,	lowercase		,	lowercase = 0.01		,	lowercase = 1_000		,	lowercase = -1		,	)			->						None:
													_a  :    int			=						sample
													if len(self.sample       ) == 0:
																				raise ValueError('''Sample data can not be empty'''       )
													_a  :    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'''       )
													_a  :    int			=						learning_rate
													_a  :    str			=						epoch_number
													_a  :    Dict			=						bias
													_a  :    int			=						len(lowercase       )
													_a  :    str			=						len(sample[0]       )  # number of columns in dataset
													_a  :    list			=						[]




						def      snake_case__(    self       )			->						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       )

													_a  :    Any			=						0

													while True:
																				_a  :    Tuple			=						False
																				for i in range(self.number_sample       ):
																											_a  :    Optional[int]			=						0
																											for j in range(self.col_sample + 1       ):
																																		_a  :    Tuple			=						u + self.weight[j] * self.sample[i][j]
																											_a  :    str			=						self.sign(lowercase       )
																											if y != self.target[i]:
																																		for j in range(self.col_sample + 1       ):
																																									_a  :    List[Any]			=						(
																																									    self.weight[j]
																																									    + self.learning_rate
																																									    * (self.target[i] - y)
																																									    * self.sample[i][j]
																																									)
																																		_a  :    Optional[Any]			=						True
            # print('Epoch: \n',epoch_count)
																				_a  :    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      snake_case__(    self		,	lowercase       )			->						None:
													if len(self.sample       ) == 0:
																				raise ValueError('''Sample data can not be empty'''       )
													sample.insert(0		,	self.bias       )
													_a  :    Tuple			=						0
													for i in range(self.col_sample + 1       ):
																				_a  :    Tuple			=						u + self.weight[i] * sample[i]

													_a  :    Any			=						self.sign(lowercase       )

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







						def      snake_case__(    self		,	lowercase       )			->						int:
													return 1 if u >= 0 else -1

__lowerCamelCase						= [
    [-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],
]

__lowerCamelCase						= [
    -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()

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= ['BartTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowerCamelCase						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)