


import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

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

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	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
    __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''')






import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__snake_case     =		2 << 2_0  # 2mb
__snake_case     =		get_logger(__name__)



class 					__lowerCamelCase	(_a		):







       def 						snake_case_					(	self:				Optional[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	self._options.get_all_options().get('runner'			)
            return runner in [None, "DirectRunner", "PortableRunner"]






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase=False				)			->				List[Any]:


     """simple docstring"""


     __UpperCamelCase				=	FileSystems
     if fs.exists(_lowercase				):
          if force_upload:
               logger.info(f'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''				)
          else:
               logger.info(f'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''				)
               return
     with fs.create(_lowercase				) as remote_file:
          with open(_lowercase			,       'rb'				) as local_file:
               __UpperCamelCase				=	local_file.read(_lowercase				)
               while chunk:
                    remote_file.write(_lowercase				)
                    __UpperCamelCase				=	local_file.read(_lowercase				)






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase=False				)			->				Optional[Any]:


     """simple docstring"""


     __UpperCamelCase				=	FileSystems
     if os.path.exists(_lowercase				):
          if force_download:
               logger.info(f'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''				)
          else:
               logger.info(f'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''				)
               return
     with fs.open(_lowercase				) as remote_file:
          with open(_lowercase			,       'wb'				) as local_file:
               __UpperCamelCase				=	remote_file.read(_lowercase				)
               while chunk:
                    local_file.write(_lowercase				)
                    __UpperCamelCase				=	remote_file.read(_lowercase				)


