


import matplotlib.pyplot as plt
import numpy as np
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Dict							= len(_lowercase							)  # Number of training samples
   UpperCAmelCase_				:   Union[str, Any]							= np.eye(_lowercase							)  # Initialize weights as identity matrix
   for j in range(_lowercase							):
      UpperCAmelCase_				:   Union[str, Any]							= point - x_train[j]
      UpperCAmelCase_				:   Optional[int]							= np.exp(diff @ diff.T / (-2.0 * tau**2)							)

   return weights
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Union[str, Any]							= weight_matrix(_lowercase      ,							_lowercase      ,							_lowercase							)
   UpperCAmelCase_				:   str							= np.linalg.inv(x_train.T @ weight_mat @ x_train							) @ (
       x_train.T @ weight_mat @ y_train.T
   )

   return weight
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   List[Any]							= np.zeros(len(_lowercase							)							)  # Initialize array of predictions
   for i, item in enumerate(_lowercase							):
      UpperCAmelCase_				:   Tuple							= item @ local_weight(_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase							)

   return y_pred
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase							):
   import seaborn as sns

   UpperCAmelCase_				:   Optional[int]							= sns.load_dataset(_lowercase							)
   UpperCAmelCase_				:   Tuple							= np.array(data[x_name]							)
   UpperCAmelCase_				:   str							= np.array(data[y_name]							)

   UpperCAmelCase_				:   Optional[Any]							= np.ones(len(_lowercase							)							)

   # pairing elements of one and x_data
   UpperCAmelCase_				:   str							= np.column_stack((one, x_data)							)

   return x_train, x_data, y_data
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							):
   UpperCAmelCase_				:   Optional[Any]							= np.sort(_lowercase      ,							axis=0							)
   plt.scatter(_lowercase      ,							_lowercase      ,							color='''blue'''							)
   plt.plot(
       x_train_sorted[:, 1]      ,							preds[x_train[:, 1].argsort(0							)]      ,							color='''yellow'''      ,							linewidth=5      ,							)
   plt.title('''Local Weighted Regression'''							)
   plt.xlabel(_lowercase							)
   plt.ylabel(_lowercase							)
   plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # Demo with a dataset from the seaborn module
       __UpperCamelCase					:	Tuple          =				load_data('tips', 'total_bill', 'tip')
       __UpperCamelCase					:	List[Any]          =				local_weight_regression(training_data_x, tip, 5)
       plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')



'''simple docstring'''



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__UpperCAmelCase     :Tuple												=  [(1,)]




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     bool(os.getenv('''RUN_SLOW'''     ,   0    )    )
     if run_slow:
          assert True
     else:
          assert False, "should have been skipped"




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					str    				=     bool(os.getenv('''RUN_SLOW'''     ,   0    )    )
     if run_slow and torch_device == "cuda":
          assert True
     else:
          assert False, "should have been skipped"



@require_torch
class       a							(						unittest.TestCase			):


     """simple docstring"""






     @slow
     @require_torch_gpu
     def 					lowerCamelCase__					(							self	:						Tuple     )		->				str:
          check_slow_torch_cuda()

     @require_torch_gpu
     @slow
     def 					lowerCamelCase__					(							self	:						Any     )		->				List[Any]:
          check_slow_torch_cuda()






     @parameterized.expand(snake_case     )
     @slow
     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						int=None     )		->				Union[str, Any]:
          check_slow()




@slow
@require_torch_gpu
def       _a      (		):





     '''simple docstring'''






     check_slow_torch_cuda()




@require_torch_gpu
@slow
def       _a      (		):





     '''simple docstring'''






     check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param'''     ,   [1]    )
def       _a      (		_lowercase   :      Union[str, Any]    ):





     '''simple docstring'''






     check_slow()




@pytest.mark.parametrize('''param'''     ,   [1]    )
@slow
def       _a      (		_lowercase   :      Dict    ):





     '''simple docstring'''






     check_slow()