


import random


class        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    Tuple	,							lowerCamelCase_     :    list[list[float]]	,							lowerCamelCase_     :    list[int]	,							lowerCamelCase_     :    float = 0.01	,							lowerCamelCase_     :    int = 1000	,							lowerCamelCase_     :    float = -1	,							):
								_lowerCAmelCase  						=sample
								if len(self.sample					) == 0:
												raise ValueError("""Sample data can not be empty"""					)
								_lowerCAmelCase  						=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"""					)
								_lowerCAmelCase  						=learning_rate
								_lowerCAmelCase  						=epoch_number
								_lowerCAmelCase  						=bias
								_lowerCAmelCase  						=len(lowerCamelCase_					)
								_lowerCAmelCase  						=len(sample[0]					)  # number of columns in dataset
								_lowerCAmelCase  						=[]






				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								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					)

								_lowerCAmelCase  						=0

								while True:
												_lowerCAmelCase  						=False
												for i in range(self.number_sample					):
																_lowerCAmelCase  						=0
																for j in range(self.col_sample + 1					):
																				_lowerCAmelCase  						=u + self.weight[j] * self.sample[i][j]
																_lowerCAmelCase  						=self.sign(lowerCamelCase_					)
																if y != self.target[i]:
																				for j in range(self.col_sample + 1					):
																								_lowerCAmelCase  						=(
																								    self.weight[j]
																								    + self.learning_rate
																								    * (self.target[i] - y)
																								    * self.sample[i][j]
																								)
																				_lowerCAmelCase  						=True
            # print('Epoch: \n',epoch_count)
												_lowerCAmelCase  						=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        lowerCAmelCase__			(				self     :    Optional[int]	,							lowerCamelCase_     :    list[float]					):
								if len(self.sample					) == 0:
												raise ValueError("""Sample data can not be empty"""					)
								sample.insert(0	,							self.bias					)
								_lowerCAmelCase  						=0
								for i in range(self.col_sample + 1					):
												_lowerCAmelCase  						=u + self.weight[i] * sample[i]

								_lowerCAmelCase  						=self.sign(lowerCamelCase_					)

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






				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    float					):
								return 1 if u >= 0 else -1
__SCREAMING_SNAKE_CASE	:							Tuple           =  [
    [-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	:							Dict           =  [
    -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	:							Union[str, Any]           =  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:
				__SCREAMING_SNAKE_CASE	:							list           =  []
				for i in range(len(samples[0])):
						__SCREAMING_SNAKE_CASE	:							Tuple           =  input('''value: ''').strip()
						if user_input == "q":
								break
						__SCREAMING_SNAKE_CASE	:							Optional[Any]           =  float(user_input)
						sample.insert(i, observation)
				network.sort(sample)





__SCREAMING_SNAKE_CASE	:							Optional[Any]           =  '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__SCREAMING_SNAKE_CASE	:							Optional[int]           =  [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}

