
import matplotlib.pyplot as plt
import numpy as np


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




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

  return weights


def 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						weight_matrix(a_			,   a_			,   a_  )
  _lowerCamelCase      =						np.linalg.inv(x_train.T @ weight_mat @ x_train  ) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						np.zeros(len(a_  )  )  # Initialize array of predictions
  for i, item in enumerate(a_  ):
    _lowerCamelCase      =						item @ local_weight(a_			,   a_			,   a_			,   a_  )

  return y_pred


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  import seaborn as sns

  _lowerCamelCase      =						sns.load_dataset(a_  )
  _lowerCamelCase      =						np.array(data[x_name]  )
  _lowerCamelCase      =						np.array(data[y_name]  )

  _lowerCamelCase      =						np.ones(len(a_  )  )

  # pairing elements of one and x_data
  _lowerCamelCase      =						np.column_stack((one, x_data)  )

  return x_train, x_data, y_data


def 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a			,   _a			,   _a			,   ):
  """simple docstring"""




  _lowerCamelCase      =						np.sort(a_			,   axis=0  )
  plt.scatter(a_			,   a_			,   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(a_  )
  plt.ylabel(a_  )
  plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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









'''simple docstring'''





from ..utils import DummyObject, requires_backends


class 							_lowerCAmelCase	(  metaclass=__A							):


 """simple docstring"""

 lowerCamelCase									=     ['''torch''', '''librosa''']







 def __init__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    str:
    requires_backends(self					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Optional[int]:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )


class 							_lowerCAmelCase	(  metaclass=__A							):


 """simple docstring"""

 lowerCamelCase									=     ['''torch''', '''librosa''']







 def __init__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(self					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    str:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )
