
def 						__magic_name__			(   __a      :	str	,		__a      :	str						):



     '''simple docstring'''




     assert x is not None
     assert y is not None

     UpperCamelCase__				=    len(__a						)
     UpperCamelCase__				=    len(__a						)

     # declaring the array for storing the dp values
     UpperCamelCase__				=    [[0] * (n + 1) for _ in range(m + 1						)]  # noqa: E741

     for i in range(1	,		m + 1						):
          for j in range(1	,		n + 1						):
               UpperCamelCase__				=    1 if x[i - 1] == y[j - 1] else 0

               UpperCamelCase__				=    max(l[i - 1][j]	,		l[i][j - 1]	,		l[i - 1][j - 1] + match						)

     UpperCamelCase__				=    """"""
     UpperCamelCase__		,	UpperCamelCase__				=    m, n
     while i > 0 and j > 0:
          UpperCamelCase__				=    1 if x[i - 1] == y[j - 1] else 0

          if l[i][j] == l[i - 1][j - 1] + match:
               if match == 1:
                    UpperCamelCase__				=    x[i - 1] + seq
               i -= 1
               j -= 1
          elif l[i][j] == l[i - 1][j]:
               i -= 1
          else:
               j -= 1

     return l[m][n], seq


if __name__ == "__main__":
    lowerCamelCase_						     =					'''AGGTAB'''
    lowerCamelCase_						     =					'''GXTXAYB'''
    lowerCamelCase_						     =					4
    lowerCamelCase_						     =					'''GTAB'''

    lowerCamelCase_     ,					lowerCamelCase_						     =					longest_common_subsequence(a, b)
    print('''len =''', ln, ''', sub-sequence =''', subseq)
    import doctest

    doctest.testmod()







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 						__magic_name__			(   __a      :	dict						):



     '''simple docstring'''




     return (data["data"], data["target"])






def 						__magic_name__			(   __a      :	np.ndarray	,		__a      :	np.ndarray	,		__a      :	np.ndarray						):



     '''simple docstring'''




     UpperCamelCase__				=    XGBRegressor(verbosity=0	,		random_state=42						)
     xgb.fit(__a	,		__a						)
     # Predict target for test data
     UpperCamelCase__				=    xgb.predict(__a						)
     UpperCamelCase__				=    predictions.reshape(len(__a						)	,		1						)
     return predictions






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    fetch_california_housing()
     UpperCamelCase__		,	UpperCamelCase__				=    data_handling(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    train_test_split(
         __a	,		__a	,		test_size=0.25	,		random_state=1						)
     UpperCamelCase__				=    xgboost(__a	,		__a	,		__a						)
     # Error printing
     print(f"Mean Absolute Error : {mean_absolute_error(__a	,		__a						)}"						)
     print(f"Mean Square Error  : {mean_squared_error(__a	,		__a						)}"						)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()





