






import matplotlib.pyplot as plt
import numpy as np


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






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

  return weights


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






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

  return weight


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






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

  return y_pred


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  import seaborn as sns

  SCREAMING_SNAKE_CASE				:					List[str]		  =			sns.load_dataset(__UpperCAmelCase )
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			np.array(data[x_name] )
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			np.array(data[y_name] )

  SCREAMING_SNAKE_CASE				:					List[str]		  =			np.ones(len(__UpperCAmelCase ) )

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

  return x_train, x_data, y_data


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase  ,   lowercase  ,   lowercase  ,   ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			np.sort(__UpperCAmelCase  ,   axis=0 )
  plt.scatter(__UpperCAmelCase  ,   __UpperCAmelCase  ,   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(__UpperCAmelCase )
  plt.ylabel(__UpperCAmelCase )
  plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Demo with a dataset from the seaborn module
   snake_case ,      snake_case ,      snake_case			 =						load_data("""tips""", """total_bill""", """tip""")
   snake_case			 =						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, is_vision_available


UpperCamelCase_	       =   {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTHybridForImageClassification",
							    "ViTHybridModel",
							    "ViTHybridPreTrainedModel",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

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





