




import matplotlib.pyplot as plt
import numpy as np
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> str:
							lowercase__  :       Optional[int]   					=							len(A_				)  # Number of training samples
							lowercase__  :       List[str]   					=							np.eye(A_				)  # Initialize weights as identity matrix
							for j in range(A_				):
														lowercase__  :       Optional[Any]   					=							point - x_train[j]
														lowercase__  :       Union[str, Any]   					=							np.exp(diff @ diff.T / (-2.0 * tau**2)				)

							return weights
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> Tuple:
							lowercase__  :       List[str]   					=							weight_matrix(A_				,A_				,A_				)
							lowercase__  :       Tuple   					=							np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
							    x_train.T @ weight_mat @ y_train.T
							)

							return weight
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> List[str]:
							lowercase__  :       Optional[Any]   					=							np.zeros(len(A_				)				)  # Initialize array of predictions
							for i, item in enumerate(A_				):
														lowercase__  :       Tuple   					=							item @ local_weight(A_				,A_				,A_				,A_				)

							return y_pred
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> List[Any]:
							import seaborn as sns

							lowercase__  :       str   					=							sns.load_dataset(A_				)
							lowercase__  :       Union[str, Any]   					=							np.array(data[x_name]				)
							lowercase__  :       int   					=							np.array(data[y_name]				)

							lowercase__  :       Union[str, Any]   					=							np.ones(len(A_				)				)

							# pairing elements of one and x_data
							lowercase__  :       Dict   					=							np.column_stack((one, x_data)				)

							return x_train, x_data, y_data
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,)			-> str:
							lowercase__  :       str   					=							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
						__a  :		Tuple			=    load_data('''tips''', '''total_bill''', '''tip''')
						__a  :		Tuple			=    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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:    List[Any]     =						{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    Any     =						['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    int     =						[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    List[str]     =						[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase				:    Union[str, Any]     =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




