
import random
class      A__       :





							def __init__(			self  :       int					,			_UpperCAmelCase  :       list[list[float]]					,			_UpperCAmelCase  :       list[int]					,			_UpperCAmelCase  :       float = 0.01					,			_UpperCAmelCase  :       int = 10_00					,			_UpperCAmelCase  :       float = -1					,			)						->		None:



											"""simple docstring"""


											__lowercase								= sample
											if len(self.sample    ) == 0:
															raise ValueError('Sample data can not be empty'    )
											__lowercase								= 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'    )
											__lowercase								= learning_rate
											__lowercase								= epoch_number
											__lowercase								= bias
											__lowercase								= len(_UpperCAmelCase    )
											__lowercase								= len(sample[0]    )  # number of columns in dataset
											__lowercase								= []





							def       a__			(			self  :       List[str]    )						->		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    )

											__lowercase								= 0

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



											"""simple docstring"""


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

											__lowercase								= self.sign(_UpperCAmelCase    )

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





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       float    )						->		int:



											"""simple docstring"""


											return 1 if u >= 0 else -1






SCREAMING_SNAKE_CASE__							=    [
    [-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],
]

SCREAMING_SNAKE_CASE__							=    [
    -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__							=    Perceptron(
							    sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
							)
							network.training()
							print("""Finished training perceptron""")
							print("""Enter values to predict or q to exit""")
							while True:
														SCREAMING_SNAKE_CASE__							=    []
														for i in range(len(samples[0])):
																					SCREAMING_SNAKE_CASE__							=    input("""value: """).strip()
																					if user_input == "q":
																												break
																					SCREAMING_SNAKE_CASE__							=    float(user_input)
																					sample.insert(i, observation)
														network.sort(sample)







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types
class      A__       (						unittest.TestCase   ,						lowerCAmelCase__    ):





							def       a__			(			self  :       Any    )						->		Dict:



											"""simple docstring"""


											__lowercase								= load_tool('translation'    )
											self.tool.setup()
											__lowercase								= load_tool('translation'					,			remote=_UpperCAmelCase    )





							def       a__			(			self  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.remote_tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Union[str, Any]    )						->		Tuple:



											"""simple docstring"""


											__lowercase								= self.tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.remote_tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											# There is a single output
											if len(self.tool.outputs    ) == 1:
															__lowercase								= [outputs]

											self.assertListEqual(output_types(_UpperCAmelCase    )					,			self.tool.outputs    )





							def       a__			(			self  :       List[str]    )						->		Any:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )

											for output, output_type in zip(_UpperCAmelCase					,			self.tool.outputs    ):
															__lowercase								= AGENT_TYPE_MAPPING[output_type]
															self.assertTrue(isinstance(_UpperCAmelCase					,			_UpperCAmelCase    )    )





							def       a__			(			self  :       Tuple    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']

											__lowercase								= []

											for _input, input_type in zip(_UpperCAmelCase					,			self.tool.inputs    ):
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
															else:
																			_inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )





