


import matplotlib.pyplot as plt
import numpy as np




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case	)    ->							np.ndarray:
       lowercase__:  List[Any]         =  len(snake_case	)  # Number of training samples
       lowercase__:  List[str]         =  np.eye(snake_case	)  # Initialize weights as identity matrix
       for j in range(snake_case	):
              lowercase__:  Dict         =  point - x_train[j]
              lowercase__:  List[str]         =  np.exp(diff @ diff.T / (-2.0 * tau**2)	)

       return weights




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)    ->							np.ndarray:
       lowercase__:  int         =  weight_matrix(snake_case     ,				snake_case     ,				snake_case	)
       lowercase__:  Optional[int]         =  np.linalg.inv(x_train.T @ weight_mat @ x_train	) @ (
           x_train.T @ weight_mat @ y_train.T
       )

       return weight




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case	)    ->							np.ndarray:
       lowercase__:  List[Any]         =  np.zeros(len(snake_case	)	)  # Initialize array of predictions
       for i, item in enumerate(snake_case	):
              lowercase__:  Optional[int]         =  item @ local_weight(snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)

       return y_pred




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case	)    ->							tuple[np.ndarray, np.ndarray, np.ndarray]:
       import seaborn as sns

       lowercase__:  List[str]         =  sns.load_dataset(snake_case	)
       lowercase__:  Optional[Any]         =  np.array(data[x_name]	)
       lowercase__:  str         =  np.array(data[y_name]	)

       lowercase__:  Any         =  np.ones(len(snake_case	)	)

       # pairing elements of one and x_data
       lowercase__:  List[str]         =  np.column_stack((one, x_data)	)

       return x_train, x_data, y_data




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				)    ->							None:
       lowercase__:  Any         =  np.sort(snake_case     ,				axis=0	)
       plt.scatter(snake_case     ,				snake_case     ,				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(snake_case	)
       plt.ylabel(snake_case	)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Demo with a dataset from the seaborn module
 __lowerCAmelCase			,__lowerCAmelCase			,__lowerCAmelCase									=				load_data('''tips''', '''total_bill''', '''tip''')
 __lowerCAmelCase									=				local_weight_regression(training_data_x, tip, 5)
 plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')






from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase									=				{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
 from .tokenization_phobert import PhobertTokenizer

else:
 import sys

 __lowerCAmelCase									=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


