





"""simple docstring"""




import random




class 					_a		:






				def __init__(					self						:       Union[str, Any],					lowerCAmelCase__						:       list[list[float]],					lowerCAmelCase__						:       list[int],					lowerCAmelCase__						:       float = 0.01,					lowerCAmelCase__						:       int = 1_0_0_0,					lowerCAmelCase__						:       float = -1,					)      -> None:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       int				)      -> 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				)

										_UpperCamelCase     :   int      =       0

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




										'''simple docstring'''



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

										_UpperCamelCase     :   Union[str, Any]      =       self.sign(lowerCAmelCase__				)

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






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       float				)      -> int:




										'''simple docstring'''



										return 1 if u >= 0 else -1

UpperCamelCase_	 =[
    [-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],
]

UpperCamelCase_	 =[
    -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()

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__  	=      {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      ["ImageGPTFeatureExtractor"]
						lowerCAmelCase__  	=      ["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      [
						    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ImageGPTForCausalImageModeling",
						    "ImageGPTForImageClassification",
						    "ImageGPTModel",
						    "ImageGPTPreTrainedModel",
						    "load_tf_weights_in_imagegpt",
						]


if TYPE_CHECKING:
						from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
												from .image_processing_imagegpt import ImageGPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_imagegpt import (
												    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ImageGPTForCausalImageModeling,
												    ImageGPTForImageClassification,
												    ImageGPTModel,
												    ImageGPTPreTrainedModel,
												    load_tf_weights_in_imagegpt,
												)

else:
						import sys

						lowerCAmelCase__  	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




