

import random



class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = 0.01     ,      _UpperCAmelCase = 1000     ,      _UpperCAmelCase = -1     ,      ):




										"""simple docstring"""




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




										"""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      )

										__magic_name__				:							List[str]				=      0

										while True:
																__magic_name__				:							int				=      False
																for i in range(self.number_sample      ):
																						__magic_name__				:							Optional[int]				=      0
																						for j in range(self.col_sample + 1      ):
																												__magic_name__				:							Union[str, Any]				=      u + self.weight[j] * self.sample[i][j]
																						__magic_name__				:							List[Any]				=      self.sign(__SCREAMING_SNAKE_CASE      )
																						if y != self.target[i]:
																												for j in range(self.col_sample + 1      ):
																																		__magic_name__				:							List[str]				=      (
																																		    self.weight[j]
																																		    + self.learning_rate
																																		    * (self.target[i] - y)
																																		    * self.sample[i][j]
																																		)
																												__magic_name__				:							Union[str, Any]				=      True
            # print('Epoch: \n',epoch_count)
																__magic_name__				:							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  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




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

										__magic_name__				:							Optional[Any]				=      self.sign(__SCREAMING_SNAKE_CASE      )

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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return 1 if u >= 0 else -1






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],
]

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()

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






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


