

from __future__ import annotations


def 					__a				(      SCREAMING_SNAKE_CASE					)					->   bool:






    '''simple docstring'''
    __UpperCAmelCase     =	len(_SCREAMING_SNAKE_CASE					)
    # We need to create solution object to save path.
    __UpperCAmelCase     =	[[0 for _ in range(_SCREAMING_SNAKE_CASE					)] for _ in range(_SCREAMING_SNAKE_CASE					)]
    __UpperCAmelCase     =	run_maze(_SCREAMING_SNAKE_CASE				,      0				,      0				,      _SCREAMING_SNAKE_CASE					)
    if solved:
        print('''\n'''.join(str(_SCREAMING_SNAKE_CASE					) for row in solutions					)					)
    else:
        print('''No solution exists!'''					)
    return solved


def 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE					)					->   bool:






    '''simple docstring'''
    __UpperCAmelCase     =	len(_SCREAMING_SNAKE_CASE					)
    # Final check point.
    if i == j == (size - 1):
        __UpperCAmelCase     =	1
        return True

    __UpperCAmelCase     =	(not i < 0) and (not j < 0)  # Check lower bounds
    __UpperCAmelCase     =	(i < size) and (j < size)  # Check upper bounds

    if lower_flag and upper_flag:
        # check for already visited and block points.
        __UpperCAmelCase     =	(not solutions[i][j]) and (not maze[i][j])
        if block_flag:
            # check visited
            __UpperCAmelCase     =	1

            # check for directions
            if (
                run_maze(_SCREAMING_SNAKE_CASE				,      i + 1				,      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE					)
                or run_maze(_SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE				,      j + 1				,      _SCREAMING_SNAKE_CASE					)
                or run_maze(_SCREAMING_SNAKE_CASE				,      i - 1				,      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE					)
                or run_maze(_SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE				,      j - 1				,      _SCREAMING_SNAKE_CASE					)
            ):
                return True

            __UpperCAmelCase     =	0
            return False
    return False


if __name__ == "__main__":
       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      A      (      _SCREAMING_SNAKE_CASE    )	-> tuple:
       return (data["data"], data["target"])

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> np.ndarray:
       lowerCamelCase   :      List[str]			 =					XGBRegressor(verbosity=0 ,random_state=42    )
       xgb.fit(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Predict target for test data
       lowerCamelCase   :      List[Any]			 =					xgb.predict(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					predictions.reshape(len(_SCREAMING_SNAKE_CASE    ) ,1    )
       return predictions

def      A      (      )	-> None:
       lowerCamelCase   :      Dict			 =					fetch_california_housing()
       lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					data_handling(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					train_test_split(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,test_size=0.25 ,random_state=1    )
       lowerCamelCase   :      Any			 =					xgboost(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Error printing
       print(f'''Mean Absolute Error : {mean_absolute_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )
       print(f'''Mean Square Error  : {mean_squared_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()






