


"""simple docstring"""





import matplotlib.pyplot as plt
import numpy as np





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

  return weights





def     lowercase__	(  lowerCamelCase     :  np.ndarray				,	lowerCamelCase     :  np.ndarray				,	lowerCamelCase     :  np.ndarray				,	lowerCamelCase     :  float				)					->     np.ndarray:
  lowerCAmelCase__    :    Union[str, Any]								=					weight_matrix(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)
  lowerCAmelCase__    :    Tuple								=					np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight





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

  return y_pred





def     lowercase__	(  lowerCamelCase     :  str				,	lowerCamelCase     :  str				,	lowerCamelCase     :  str				)					->     tuple[np.ndarray, np.ndarray, np.ndarray]:
  import seaborn as sns

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

  lowerCAmelCase__    :    Optional[int]								=					np.ones(len(lowerCamelCase				)				)

  # pairing elements of one and x_data
  lowerCAmelCase__    :    Optional[Any]								=					np.column_stack((one, x_data)				)

  return x_train, x_data, y_data





def     lowercase__	(  lowerCamelCase     :  np.ndarray				,	lowerCamelCase     :  np.ndarray				,	lowerCamelCase     :  np.ndarray				,	lowerCamelCase     :  np.ndarray				,	lowerCamelCase     :  str				,	lowerCamelCase     :  str				,	)					->     None:
  lowerCAmelCase__    :    str								=					np.sort(lowerCamelCase				,	axis=0				)
  plt.scatter(lowerCamelCase				,	lowerCamelCase				,	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(lowerCamelCase				)
  plt.ylabel(lowerCamelCase				)
  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")




"""simple docstring"""





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__)
