






"""simple docstring"""



import matplotlib.pyplot as plt
import numpy as np
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    np.ndarray						,   __snake_case     :    np.ndarray						,   __snake_case     :    float       ):





  '''simple docstring'''





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

  return weights
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    np.ndarray						,   __snake_case     :    np.ndarray						,   __snake_case     :    np.ndarray						,   __snake_case     :    float       ):





  '''simple docstring'''





  lowercase				       =			weight_matrix(lowercase__						,   lowercase__						,   lowercase__       )
  lowercase				       =			np.linalg.inv(x_train.T @ weight_mat @ x_train       ) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    np.ndarray						,   __snake_case     :    np.ndarray						,   __snake_case     :    float       ):





  '''simple docstring'''





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

  return y_pred
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    str						,   __snake_case     :    str       ):





  '''simple docstring'''





  import seaborn as sns

  lowercase				       =			sns.load_dataset(lowercase__       )
  lowercase				       =			np.array(data[x_name]       )
  lowercase				       =			np.array(data[y_name]       )

  lowercase				       =			np.ones(len(lowercase__       )       )

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

  return x_train, x_data, y_data
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    np.ndarray						,   __snake_case     :    np.ndarray						,   __snake_case     :    np.ndarray						,   __snake_case     :    np.ndarray						,   __snake_case     :    str						,   __snake_case     :    str						,   ):





  '''simple docstring'''





  lowercase				       =			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						:    int		=						load_data('tips', 'total_bill', 'tip')
    _UpperCamelCase						:    str		=						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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase 					=    {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['MarianTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    [
				    'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MarianForCausalLM',
				    'MarianModel',
				    'MarianMTModel',
				    'MarianPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
				from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_marian import MarianTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_marian import (
								    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MarianForCausalLM,
								    MarianModel,
								    MarianMTModel,
								    MarianPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
				import sys

				__UpperCAmelCase 					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






