
import random





class 		__lowerCamelCase     :



							def __init__(     self	,				__snake_case	,				__snake_case	,				__snake_case = 0.01	,				__snake_case = 1_0_0_0	,				__snake_case = -1	,				)						->     None:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     sample
								if len(self.sample							) == 0:
									raise ValueError("Sample data can not be empty"							)
								UpperCAmelCase:					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"							)
								UpperCAmelCase:					Optional[Any] 						=     learning_rate
								UpperCAmelCase:					Optional[int] 						=     epoch_number
								UpperCAmelCase:					Optional[int] 						=     bias
								UpperCAmelCase:					List[str] 						=     len(__snake_case							)
								UpperCAmelCase:					Tuple 						=     len(sample[0]							)  # number of columns in dataset
								UpperCAmelCase:					list 						=     []



							def     A__ (     self							)						->     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:					Dict 						=     0

								while True:
									UpperCAmelCase:					Any 						=     False
									for i in range(self.number_sample							):
										UpperCAmelCase:					Dict 						=     0
										for j in range(self.col_sample + 1							):
											UpperCAmelCase:					Tuple 						=     u + self.weight[j] * self.sample[i][j]
										UpperCAmelCase:					str 						=     self.sign(__snake_case							)
										if y != self.target[i]:
											for j in range(self.col_sample + 1							):
												UpperCAmelCase:					Union[str, Any] 						=     (
												    self.weight[j]
												    + self.learning_rate
												    * (self.target[i] - y)
												    * self.sample[i][j]
												)
											UpperCAmelCase:					List[str] 						=     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     A__ (     self	,				__snake_case							)						->     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:					Tuple 						=     u + self.weight[i] * sample[i]

								UpperCAmelCase:					str 						=     self.sign(__snake_case							)

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




							def     A__ (     self	,				__snake_case							)						->     int:






								"""simple docstring"""



								return 1 if u >= 0 else -1


snake_case_      :					Optional[Any]				      =     [
    [-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],
]

snake_case_      :					int				      =     [
    -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()

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


from typing import TYPE_CHECKING

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


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
