code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import matplotlib.pyplot as plt
  import numpy as np
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=len(lowercase	)  # Number of training samples
      a					=np.eye(lowercase	)  # Initialize weights as identity matrix
      for j in range(lowercase	):
          a					=point - x_train[j]
          a					=np.exp(diff @ diff.T / (-2.0 * tau**2)	)
  
      return weights
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=weight_matrix(lowercase ,					lowercase ,					lowercase	)
      a					=np.linalg.inv(x_train.T @ weight_mat @ x_train	) @ (
          x_train.T @ weight_mat @ y_train.T
      )
  
      return weight
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=np.zeros(len(lowercase	)	)  # Initialize array of predictions
      for i, item in enumerate(lowercase	):
          a					=item @ local_weight(lowercase ,					lowercase ,					lowercase ,					lowercase	)
  
      return y_pred
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      import seaborn as sns
  
      a					=sns.load_dataset(lowercase	)
      a					=np.array(data[x_name]	)
      a					=np.array(data[y_name]	)
  
      a					=np.ones(len(lowercase	)	)
  
      # pairing elements of one and x_data
      a					=np.column_stack((one, x_data)	)
  
      return x_train, x_data, y_data
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase ,					):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=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
    lowerCamelCase_     :     List[Any]    		=						load_data("""tips""", """total_bill""", """tip""")
    lowerCamelCase_     :     List[Any]    		=						local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  lowerCamelCase_     :     Any    		=						{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase_     :     List[str]    		=						[
             """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
             """DinatForImageClassification""",
             """DinatModel""",
             """DinatPreTrainedModel""",
             """DinatBackbone""",
         ]
  
  if TYPE_CHECKING:
         from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_dinat import (
                    DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DinatBackbone,
                    DinatForImageClassification,
                    DinatModel,
                    DinatPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase_     :     List[Any]    		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)