





"""simple docstring"""


import matplotlib.pyplot as plt
import numpy as np





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

       return weights





def SCREAMING_SNAKE_CASE_   (       snake_case   :					np.ndarray						,  snake_case   :					np.ndarray						,  snake_case   :					np.ndarray						,  snake_case   :					float		)->		np.ndarray:
       _lowerCamelCase    	=					weight_matrix(snake_case						,  snake_case						,  snake_case		)
       _lowerCamelCase    	=					np.linalg.inv(x_train.T @ weight_mat @ x_train		) @ (
           x_train.T @ weight_mat @ y_train.T
       )

       return weight





def SCREAMING_SNAKE_CASE_   (       snake_case   :					np.ndarray						,  snake_case   :					np.ndarray						,  snake_case   :					float		)->		np.ndarray:
       _lowerCamelCase    	=					np.zeros(len(snake_case		)		)  # Initialize array of predictions
       for i, item in enumerate(snake_case		):
              _lowerCamelCase    	=					item @ local_weight(snake_case						,  snake_case						,  snake_case						,  snake_case		)

       return y_pred





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

       _lowerCamelCase    	=					sns.load_dataset(snake_case		)
       _lowerCamelCase    	=					np.array(data[x_name]		)
       _lowerCamelCase    	=					np.array(data[y_name]		)

       _lowerCamelCase    	=					np.ones(len(snake_case		)		)

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

       return x_train, x_data, y_data





def SCREAMING_SNAKE_CASE_   (       snake_case   :					np.ndarray						,  snake_case   :					np.ndarray						,  snake_case   :					np.ndarray						,  snake_case   :					np.ndarray						,  snake_case   :					str						,  snake_case   :					str						,  )->		None:
       _lowerCamelCase    	=					np.sort(snake_case						,  axis=0		)
       plt.scatter(snake_case						,  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(snake_case		)
       plt.ylabel(snake_case		)
       plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Demo with a dataset from the seaborn module
      A_							,	A_							,	A_     :      int								=load_data("""tips""", """total_bill""", """tip""")
      A_     :      Union[str, Any]								=local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")













"""simple docstring"""


A_     :      Dict								=1_0**8





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int		)->		bool:
       if n % 10 == 0:
              return False
       _lowerCamelCase    	=					str(snake_case		)
       return s == s[::-1]





def SCREAMING_SNAKE_CASE_   (       )->		int:
       _lowerCamelCase    	=					set()
       _lowerCamelCase    	=					1
       _lowerCamelCase    	=					5
       while sum_squares < LIMIT:
              _lowerCamelCase    	=					first_square + 1
              while sum_squares < LIMIT:
                     if is_palindrome(snake_case		):
                            answer.add(snake_case		)
                     last_square += 1
                     sum_squares += last_square**2
              first_square += 1
              _lowerCamelCase    	=					first_square**2 + (first_square + 1) ** 2

       return sum(snake_case		)


if __name__ == "__main__":
      print(solution())






