




import matplotlib.pyplot as plt
import numpy as np

def      lowerCAmelCase_     (					lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    float  )   ->     np.ndarray:


      '''simple docstring'''




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

      return weights

def      lowerCAmelCase_     (					lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    float  )   ->     np.ndarray:


      '''simple docstring'''




      lowerCamelCase__ 					=			weight_matrix(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
      lowerCamelCase__ 					=			np.linalg.inv(x_train.T @ weight_mat @ x_train  ) @ (
          x_train.T @ weight_mat @ y_train.T
      )

      return weight

def      lowerCAmelCase_     (					lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    float  )   ->     np.ndarray:


      '''simple docstring'''




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

      return y_pred

def      lowerCAmelCase_     (					lowerCamelCase:    str       ,			lowerCamelCase:    str       ,			lowerCamelCase:    str  )   ->     tuple[np.ndarray, np.ndarray, np.ndarray]:


      '''simple docstring'''




      import seaborn as sns

      lowerCamelCase__ 					=			sns.load_dataset(lowerCamelCase  )
      lowerCamelCase__ 					=			np.array(data[x_name]  )
      lowerCamelCase__ 					=			np.array(data[y_name]  )

      lowerCamelCase__ 					=			np.ones(len(lowerCamelCase  )  )

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

      return x_train, x_data, y_data

def      lowerCAmelCase_     (					lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    np.ndarray       ,			lowerCamelCase:    str       ,			lowerCamelCase:    str       ,			)   ->     None:


      '''simple docstring'''




      lowerCamelCase__ 					=			np.sort(lowerCamelCase       ,			axis=0  )
      plt.scatter(lowerCamelCase       ,			lowerCamelCase       ,			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(lowerCamelCase  )
      plt.ylabel(lowerCamelCase  )
      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      :				Any				      =load_data("""tips""", """total_bill""", """tip""")
      __SCREAMING_SNAKE_CASE      :				List[Any]				      =local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import tensorflow as tf






class      A     (						TensorFormatter[Mapping, "tf.Tensor", Mapping]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                       # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                       return tf.ragged.stack(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

           if isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.integer     ):
                 lowerCamelCase__ 					=			{'dtype': tf.intaa}
           elif isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.floating     ):
                 lowerCamelCase__ 					=			{'dtype': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

           return tf.convert_to_tensor(A__		,					**{**default_dtype, **self.tf_tensor_kwargs}     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(A__		,					torch.Tensor     ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
           if hasattr(A__		,					'__array__'     ) and not isinstance(A__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                       return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           elif isinstance(A__		,					(list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           return self._tensorize(A__     )




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch