






'''simple docstring'''





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor






def    lowerCamelCase_     (    lowercase__):
 return (data["data"], data["target"])






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     XGBRegressor(verbosity=0				,     random_state=42)
 xgb.fit(lowercase__				,     lowercase__)
 # Predict target for test data
 lowerCamelCase__  =     xgb.predict(lowercase__)
 lowerCamelCase__  =     predictions.reshape(len(lowercase__)				,     1)
 return predictions






def    lowerCamelCase_     (    ):
 lowerCamelCase__  =     fetch_california_housing()
 lowerCamelCase__  ,	lowerCamelCase__  =     data_handling(lowercase__)
 lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     train_test_split(
     lowercase__				,     lowercase__				,     test_size=0.25				,     random_state=1)
 lowerCamelCase__  =     xgboost(lowercase__				,     lowercase__				,     lowercase__)
 # Error printing
 print(F'''Mean Absolute Error : {mean_absolute_error(lowercase__				,     lowercase__)}''')
 print(F'''Mean Square Error  : {mean_squared_error(lowercase__				,     lowercase__)}''')


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
 main()









'''simple docstring'''





from __future__ import annotations
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     int     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     order

     # a_{0} ... a_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order
     # b_{0} ... b_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     lowerCamelCase__  =     [0.0] * self.order
     # y[n-1] ... y[n-k]
     lowerCamelCase__  =     [0.0] * self.order

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     list[float]				,     __lowerCamelCase     :     list[float]     )							->							None:

     '''simple docstring'''




     if len(__lowerCamelCase     ) < self.order:
      lowerCamelCase__  =     [1.0, *a_coeffs]

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected a_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected b_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     lowerCamelCase__  =     a_coeffs
     lowerCamelCase__  =     b_coeffs




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     float     )							->							float:

     '''simple docstring'''




     lowerCamelCase__  =     0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1				,     self.order + 1     ):
      result += (
          self.b_coeffs[i] * self.input_history[i - 1]
          - self.a_coeffs[i] * self.output_history[i - 1]
      )

     lowerCamelCase__  =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     lowerCamelCase__  =     self.input_history[:-1]
     lowerCamelCase__  =     self.output_history[:-1]

     lowerCamelCase__  =     sample
     lowerCamelCase__  =     result

     return result

