


'''simple docstring'''



import matplotlib.pyplot as plt
import numpy as np




def       _a      (		_lowercase   :      np.ndarray     ,   _lowercase   :      np.ndarray     ,   _lowercase   :      float    ):





     '''simple docstring'''






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

     return weights




def       _a      (		_lowercase   :      np.ndarray     ,   _lowercase   :      np.ndarray     ,   _lowercase   :      np.ndarray     ,   _lowercase   :      float    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Union[str, Any]    				=     weight_matrix(_lowercase     ,   _lowercase     ,   _lowercase    )
     __UpperCAmelCase      :					str    				=     np.linalg.inv(x_train.T @ weight_mat @ x_train    ) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight




def       _a      (		_lowercase   :      np.ndarray     ,   _lowercase   :      np.ndarray     ,   _lowercase   :      float    ):





     '''simple docstring'''






     __UpperCAmelCase      :					List[Any]    				=     np.zeros(len(_lowercase    )    )  # Initialize array of predictions
     for i, item in enumerate(_lowercase    ):
          __UpperCAmelCase      :					Tuple    				=     item @ local_weight(_lowercase     ,   _lowercase     ,   _lowercase     ,   _lowercase    )

     return y_pred




def       _a      (		_lowercase   :      str     ,   _lowercase   :      str     ,   _lowercase   :      str    ):





     '''simple docstring'''






     import seaborn as sns

     __UpperCAmelCase      :					Optional[int]    				=     sns.load_dataset(_lowercase    )
     __UpperCAmelCase      :					Tuple    				=     np.array(data[x_name]    )
     __UpperCAmelCase      :					str    				=     np.array(data[y_name]    )

     __UpperCAmelCase      :					Optional[Any]    				=     np.ones(len(_lowercase    )    )

     # pairing elements of one and x_data
     __UpperCAmelCase      :					str    				=     np.column_stack((one, x_data)    )

     return x_train, x_data, y_data




def       _a      (		_lowercase   :      np.ndarray     ,   _lowercase   :      np.ndarray     ,   _lowercase   :      np.ndarray     ,   _lowercase   :      np.ndarray     ,   _lowercase   :      str     ,   _lowercase   :      str     ,   ):





     '''simple docstring'''






     __UpperCAmelCase      :					Optional[Any]    				=     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
      __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :Tuple												=  load_data("tips", "total_bill", "tip")
      __UpperCAmelCase     :List[Any]												=  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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Optional[int]												=  ["MPNetTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MPNetForMaskedLM",
          "MPNetForMultipleChoice",
          "MPNetForQuestionAnswering",
          "MPNetForSequenceClassification",
          "MPNetForTokenClassification",
          "MPNetLayer",
          "MPNetModel",
          "MPNetPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Any												=  [
          "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMPNetEmbeddings",
          "TFMPNetForMaskedLM",
          "TFMPNetForMultipleChoice",
          "TFMPNetForQuestionAnswering",
          "TFMPNetForSequenceClassification",
          "TFMPNetForTokenClassification",
          "TFMPNetMainLayer",
          "TFMPNetModel",
          "TFMPNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

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