
import matplotlib.pyplot as plt
import numpy as np






def     lowerCamelCase__				(   a						,						a						,						a ) ->  np.ndarray:
   _A:    Union[str, Any]						     =			len(a )  # Number of training samples
   _A:    List[Any]						     =			np.eye(a )  # Initialize weights as identity matrix
   for j in range(a ):
      _A:    Dict						     =			point - x_train[j]
      _A:    Tuple						     =			np.exp(diff @ diff.T / (-2.0 * tau**2) )

   return weights






def     lowerCamelCase__				(   a						,						a						,						a						,						a ) ->  np.ndarray:
   _A:    Dict						     =			weight_matrix(a						,						a						,						a )
   _A:    Union[str, Any]						     =			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 ) ->  np.ndarray:
   _A:    Tuple						     =			np.zeros(len(a ) )  # Initialize array of predictions
   for i, item in enumerate(a ):
      _A:    Dict						     =			item @ local_weight(a						,						a						,						a						,						a )

   return y_pred






def     lowerCamelCase__				(   a						,						a						,						a ) ->  tuple[np.ndarray, np.ndarray, np.ndarray]:
   import seaborn as sns

   _A:    Optional[int]						     =			sns.load_dataset(a )
   _A:    Dict						     =			np.array(data[x_name] )
   _A:    str						     =			np.array(data[y_name] )

   _A:    Optional[Any]						     =			np.ones(len(a ) )

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

   return x_train, x_data, y_data






def     lowerCamelCase__				(   a						,						a						,						a						,						a						,						a						,						a						,						) ->  None:
   _A:    Optional[Any]						     =			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__						:	Dict	     = load_data('tips', 'total_bill', 'tip')
   UpperCAmelCase__						:	Dict	     = 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__						:	Any	     = {
    'configuration_decision_transformer': [
        'DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DecisionTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:	Optional[Any]	     = [
       'DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DecisionTransformerGPT2Model',
       'DecisionTransformerGPT2PreTrainedModel',
       'DecisionTransformerModel',
       'DecisionTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__						:	Optional[Any]	     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



