


import matplotlib.pyplot as plt
import numpy as np
def 	_lowerCamelCase  (		A_  :	np.ndarray							,  A_  :	np.ndarray							,  A_  :	float      )			->			np.ndarray:

   '''simple docstring'''






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

   return weights
def 	_lowerCamelCase  (		A_  :	np.ndarray							,  A_  :	np.ndarray							,  A_  :	np.ndarray							,  A_  :	float      )			->			np.ndarray:

   '''simple docstring'''






   UpperCamelCase__ :			int 						=weight_matrix(A_							,  A_							,  A_      )
   UpperCamelCase__ :			List[Any] 						=np.linalg.inv(x_train.T @ weight_mat @ x_train      ) @ (
       x_train.T @ weight_mat @ y_train.T
   )

   return weight
def 	_lowerCamelCase  (		A_  :	np.ndarray							,  A_  :	np.ndarray							,  A_  :	float      )			->			np.ndarray:

   '''simple docstring'''






   UpperCamelCase__ :			Tuple 						=np.zeros(len(A_      )      )  # Initialize array of predictions
   for i, item in enumerate(A_      ):
      UpperCamelCase__ :			Union[str, Any] 						=item @ local_weight(A_							,  A_							,  A_							,  A_      )

   return y_pred
def 	_lowerCamelCase  (		A_  :	str							,  A_  :	str							,  A_  :	str      )			->			tuple[np.ndarray, np.ndarray, np.ndarray]:

   '''simple docstring'''






   import seaborn as sns

   UpperCamelCase__ :			List[Any] 						=sns.load_dataset(A_      )
   UpperCamelCase__ :			int 						=np.array(data[x_name]      )
   UpperCamelCase__ :			Any 						=np.array(data[y_name]      )

   UpperCamelCase__ :			List[Any] 						=np.ones(len(A_      )      )

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

   return x_train, x_data, y_data
def 	_lowerCamelCase  (		A_  :	np.ndarray							,  A_  :	np.ndarray							,  A_  :	np.ndarray							,  A_  :	np.ndarray							,  A_  :	str							,  A_  :	str							,  )			->			None:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[int] 						=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			,				__UpperCAmelCase			,				__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""")





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      			=  {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SEWDForCTC""",
         """SEWDForSequenceClassification""",
         """SEWDModel""",
         """SEWDPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_sew_d import (
              SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
              SEWDForCTC,
              SEWDForSequenceClassification,
              SEWDModel,
              SEWDPreTrainedModel,
          )


else:
     import sys

     __UpperCAmelCase      			=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

