


'''simple docstring'''






import matplotlib.pyplot as plt
import numpy as np


def A_					(				__SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					float       )     ->  np.ndarray:
     __SCREAMING_SNAKE_CASE					:							Dict			   = len(__SCREAMING_SNAKE_CASE       )  # Number of training samples
     __SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = np.eye(__SCREAMING_SNAKE_CASE       )  # Initialize weights as identity matrix
     for j in range(__SCREAMING_SNAKE_CASE       ):
          __SCREAMING_SNAKE_CASE					:							Any			   = point - x_train[j]
          __SCREAMING_SNAKE_CASE					:							List[Any]			   = np.exp(diff @ diff.T / (-2.0 * tau**2)       )

     return weights


def A_					(				__SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					float       )     ->  np.ndarray:
     __SCREAMING_SNAKE_CASE					:							int			   = weight_matrix(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )
     __SCREAMING_SNAKE_CASE					:							Optional[int]			   = np.linalg.inv(x_train.T @ weight_mat @ x_train       ) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight


def A_					(				__SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					float       )     ->  np.ndarray:
     __SCREAMING_SNAKE_CASE					:							int			   = np.zeros(len(__SCREAMING_SNAKE_CASE       )       )  # Initialize array of predictions
     for i, item in enumerate(__SCREAMING_SNAKE_CASE       ):
          __SCREAMING_SNAKE_CASE					:							Tuple			   = item @ local_weight(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )

     return y_pred


def A_					(				__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					str       )     ->  tuple[np.ndarray, np.ndarray, np.ndarray]:
     import seaborn as sns

     __SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sns.load_dataset(__SCREAMING_SNAKE_CASE       )
     __SCREAMING_SNAKE_CASE					:							Optional[Any]			   = np.array(data[x_name]       )
     __SCREAMING_SNAKE_CASE					:							Optional[Any]			   = np.array(data[y_name]       )

     __SCREAMING_SNAKE_CASE					:							Optional[Any]			   = np.ones(len(__SCREAMING_SNAKE_CASE       )       )

     # pairing elements of one and x_data
     __SCREAMING_SNAKE_CASE					:							int			   = np.column_stack((one, x_data)       )

     return x_train, x_data, y_data


def A_					(				__SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					np.ndarray	,   __SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					str	,   )     ->  None:
     __SCREAMING_SNAKE_CASE					:							Any			   = np.sort(__SCREAMING_SNAKE_CASE	,   axis=0       )
     plt.scatter(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   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(__SCREAMING_SNAKE_CASE       )
     plt.ylabel(__SCREAMING_SNAKE_CASE       )
     plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Demo with a dataset from the seaborn module
      _A     ,    _A     ,    _A      =     load_data("""tips""", """total_bill""", """tip""")
      _A      =     local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")






'''simple docstring'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_A      =     TypeVar("""T""", bound="""TaskTemplate""")






@dataclass(frozen=snake_case		)
class      SCREAMING_SNAKE_CASE_	( abc.ABC		):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			__a					:      str
			__a					:      ClassVar[Features]
			__a					:      ClassVar[Features]




			def 			_snake_case   (      self					,      lowercase       )			->						T:


								'''simple docstring'''






								return copy.deepcopy(self       )




			@property
			def 			_snake_case   (      self       )			->						Features:


								'''simple docstring'''






								return Features(**self.input_schema					,      **self.label_schema       )




			@property
			@abc.abstractmethod
			def 			_snake_case   (      self       )			->						Dict[str, str]:


								'''simple docstring'''






								raise NotImplementedError




			@classmethod
			def 			_snake_case   (      cls					,      lowercase       )			->						T:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = {f.name for f in dataclasses.fields(cls       )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )


