





'''simple docstring'''






import matplotlib.pyplot as plt
import numpy as np
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


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

      return weights
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							weight_matrix(lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						)
      _lowerCAmelCase							  =							np.linalg.inv(x_train.T @ weight_mat @ x_train						) @ (
          x_train.T @ weight_mat @ y_train.T
      )

      return weight
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


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

      return y_pred
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      import seaborn as sns

      _lowerCAmelCase							  =							sns.load_dataset(lowerCAmelCase						)
      _lowerCAmelCase							  =							np.array(data[x_name]						)
      _lowerCAmelCase							  =							np.array(data[y_name]						)

      _lowerCAmelCase							  =							np.ones(len(lowerCAmelCase						)						)

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

      return x_train, x_data, y_data
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			):





      """simple docstring"""


      _lowerCAmelCase							  =							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
      A__     ,		A__     ,		A__       :     List[Any]          =load_data('''tips''', '''total_bill''', '''tip''')
      A__       :     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,
)


A__       :     Tuple          ={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Tuple          =['''MPNetTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[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:
      A__       :     str          =[
          '''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

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






