'''simple docstring'''

import matplotlib.pyplot as plt
import numpy as np



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__   ):


						'''simple docstring'''






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

						return weights



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__   ):


						'''simple docstring'''






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

						return weight



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__   ):


						'''simple docstring'''






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

						return y_pred



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__   ):


						'''simple docstring'''






						import seaborn as sns

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

						_lowerCAmelCase					    =np.ones(len(a__   )   )

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

						return x_train, x_data, y_data



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   ):


						'''simple docstring'''






						_lowerCAmelCase					    =np.sort(a__      ,   axis=0   )
						plt.scatter(a__      ,   a__      ,   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(a__   )
						plt.ylabel(a__   )
						plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							lowercase_  		=			load_data('''tips''', '''total_bill''', '''tip''')
							lowercase_  		=			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_sentencepiece_available, is_torch_available


lowercase						:   int      						=							{
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Any      						=							['''XLMProphetNetTokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Tuple      						=							[
          '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XLMProphetNetDecoder''',
          '''XLMProphetNetEncoder''',
          '''XLMProphetNetForCausalLM''',
          '''XLMProphetNetForConditionalGeneration''',
          '''XLMProphetNetModel''',
          '''XLMProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

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




