





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

# Load iris file
_lowerCAmelCase							:      Dict						=	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
_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase							:      Any						=	train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

_lowerCAmelCase							:      Tuple						=	KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_lowerCAmelCase							:      str						=	[[1, 2, 1, 4], [2, 3, 4, 5]]

_lowerCAmelCase							:      Union[str, Any]						=	knn.predict(X_new)

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







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
