




'''simple docstring'''






import matplotlib.pyplot as plt
import numpy as np




def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->np.ndarray:






       '''simple docstring'''




       lowercase	:		Optional[int]    =   len(lowerCamelCase   )  # Number of training samples
       lowercase	:		Any    =   np.eye(lowerCamelCase   )  # Initialize weights as identity matrix
       for j in range(lowerCamelCase   ):
              lowercase	:		Dict    =   point - x_train[j]
              lowercase	:		Any    =   np.exp(diff @ diff.T / (-2.0 * tau**2)   )

       return weights




def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->np.ndarray:






       '''simple docstring'''




       lowercase	:		Optional[Any]    =   weight_matrix(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
       lowercase	:		Optional[int]    =   np.linalg.inv(x_train.T @ weight_mat @ x_train   ) @ (
           x_train.T @ weight_mat @ y_train.T
       )

       return weight




def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->np.ndarray:






       '''simple docstring'''




       lowercase	:		Optional[int]    =   np.zeros(len(lowerCamelCase   )   )  # Initialize array of predictions
       for i, item in enumerate(lowerCamelCase   ):
              lowercase	:		str    =   item @ local_weight(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )

       return y_pred




def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->tuple[np.ndarray, np.ndarray, np.ndarray]:






       '''simple docstring'''




       import seaborn as sns

       lowercase	:		List[str]    =   sns.load_dataset(lowerCamelCase   )
       lowercase	:		Dict    =   np.array(data[x_name]   )
       lowercase	:		List[str]    =   np.array(data[y_name]   )

       lowercase	:		Union[str, Any]    =   np.ones(len(lowerCamelCase   )   )

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

       return x_train, x_data, y_data




def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					)					->None:






       '''simple docstring'''




       lowercase	:		int    =   np.sort(lowerCamelCase ,					axis=0   )
       plt.scatter(lowerCamelCase ,					lowerCamelCase ,					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(lowerCamelCase   )
       plt.ylabel(lowerCamelCase   )
       plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Demo with a dataset from the seaborn module
      lowercase						,						lowercase						,						lowercase						:   int      						=							load_data('''tips''', '''total_bill''', '''tip''')
      lowercase						:   Optional[int]      						=							local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')










'''simple docstring'''






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase						:   Any      						=							TypeVar('''T''', bound='''Enum''')

@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Union[str, Any]       =   8_0
 __snake_case						:  Optional[Any]       =   7_0
 __snake_case						:  List[str]       =   6_0
 __snake_case						:  str       =   5_0
 __snake_case						:  Tuple       =   4_0
 __snake_case						:  Union[str, Any]       =   3_0
 __snake_case						:  Dict       =   2_0
 __snake_case						:  str       =   1_0






@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Optional[Any]       =   2_4
 __snake_case						:  List[str]       =   2_1
 __snake_case						:  Any       =   1_8
 __snake_case						:  Union[str, Any]       =   1_5
 __snake_case						:  Dict       =   1_2
 __snake_case						:  Union[str, Any]       =   9
 __snake_case						:  List[str]       =   6
 __snake_case						:  Optional[int]       =   3
 __snake_case						:  Tuple       =   2
 __snake_case						:  Any       =   1
 __snake_case						:  Optional[int]       =   -1
 __snake_case						:  Optional[int]       =   -2
 __snake_case						:  str       =   -3
 __snake_case						:  Optional[Any]       =   -6
 __snake_case						:  Optional[int]       =   -9
 __snake_case						:  List[Any]       =   -1_2
 __snake_case						:  str       =   -1_5
 __snake_case						:  str       =   -1_8
 __snake_case						:  Dict       =   -2_1
 __snake_case						:  int       =   -2_4



 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value > 0}





 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value < 0}






def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		Tuple    =   SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
       for name_prefix, value_prefix in prefixes.items():
              lowercase	:		Dict    =   value / (10**value_prefix)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {name_prefix}"""
       return str(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       for prefix in BinaryUnit:
              lowercase	:		Dict    =   value / (2**prefix.value)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {prefix.name}"""
       return str(lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()




