
'''simple docstring'''





import matplotlib.pyplot as plt
import numpy as np







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

  return weights







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				Any												=       weight_matrix(lowercase__      ,					lowercase__      ,					lowercase__		)
  UpperCAmelCase__			:				Optional[Any]												=       np.linalg.inv(x_train.T @ weight_mat @ x_train		) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  UpperCAmelCase__			:				Union[str, Any]												=       np.zeros(len(lowercase__		)		)  # Initialize array of predictions
  for i, item in enumerate(lowercase__		):
    UpperCAmelCase__			:				Dict												=       item @ local_weight(lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__		)

  return y_pred







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):
  import seaborn as sns

  UpperCAmelCase__			:				int												=       sns.load_dataset(lowercase__		)
  UpperCAmelCase__			:				Tuple												=       np.array(data[x_name]		)
  UpperCAmelCase__			:				List[str]												=       np.array(data[y_name]		)

  UpperCAmelCase__			:				List[Any]												=       np.ones(len(lowercase__		)		)

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

  return x_train, x_data, y_data







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__      ,					lowercase__      ,					):
  UpperCAmelCase__			:				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
      SCREAMING_SNAKE_CASE,	SCREAMING_SNAKE_CASE,	SCREAMING_SNAKE_CASE    =					load_data("""tips""", """total_bill""", """tip""")
      SCREAMING_SNAKE_CASE    =					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


SCREAMING_SNAKE_CASE    =					{"""configuration_visual_bert""": ["""VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VisualBertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					[
          """VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """VisualBertForMultipleChoice""",
          """VisualBertForPreTraining""",
          """VisualBertForQuestionAnswering""",
          """VisualBertForRegionToPhraseAlignment""",
          """VisualBertForVisualReasoning""",
          """VisualBertLayer""",
          """VisualBertModel""",
          """VisualBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_visual_bert import (
                VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VisualBertForMultipleChoice,
                VisualBertForPreTraining,
                VisualBertForQuestionAnswering,
                VisualBertForRegionToPhraseAlignment,
                VisualBertForVisualReasoning,
                VisualBertLayer,
                VisualBertModel,
                VisualBertPreTrainedModel,
            )


else:
      import sys

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



