'''simple docstring'''




import matplotlib.pyplot as plt
import numpy as np

def      UpperCamelCase_							( _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				float   )   ->   np.ndarray:


       """simple docstring"""







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

       return weights

def      UpperCamelCase_							( _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				float   )   ->   np.ndarray:


       """simple docstring"""







       _UpperCAmelCase					:  str											=   weight_matrix(_UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase   )
       _UpperCAmelCase					:  str											=   np.linalg.inv(x_train.T @ weight_mat @ x_train   ) @ (
           x_train.T @ weight_mat @ y_train.T
       )

       return weight

def      UpperCamelCase_							( _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				float   )   ->   np.ndarray:


       """simple docstring"""







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

       return y_pred

def      UpperCamelCase_							( _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				str   )   ->   tuple[np.ndarray, np.ndarray, np.ndarray]:


       """simple docstring"""







       import seaborn as sns

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

       _UpperCAmelCase					:  Dict											=   np.ones(len(_UpperCAmelCase   )   )

       # pairing elements of one and x_data
       _UpperCAmelCase					:  Any											=   np.column_stack((one, x_data)   )

       return x_train, x_data, y_data

def      UpperCamelCase_							( _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				np.ndarray      ,     _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				str      ,     )   ->   None:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[int]											=   np.sort(_UpperCAmelCase      ,     axis=0   )
       plt.scatter(_UpperCAmelCase      ,     _UpperCAmelCase      ,     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(_UpperCAmelCase   )
       plt.ylabel(_UpperCAmelCase   )
       plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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




'''simple docstring'''




import warnings

from .generation import FlaxGenerationMixin

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      warnings.warn(
          "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
          "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."							,			snake_case__							,			)

