
from statistics import mean

import numpy as np






def 						__magic_name__			(   __a      :	list	,		__a      :	list	,		__a      :	list	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    0
     # Number of processes finished
     UpperCamelCase__				=    0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     UpperCamelCase__				=    [0] * no_of_process
     # List to include calculation results
     UpperCamelCase__				=    [0] * no_of_process

     # Sort by arrival time.
     UpperCamelCase__				=    [burst_time[i] for i in np.argsort(__a						)]
     UpperCamelCase__				=    [process_name[i] for i in np.argsort(__a						)]
     arrival_time.sort()

     while no_of_process > finished_process_count:
          UpperCamelCase__				=    0
          while finished_process[i] == 1:
               i += 1
          if current_time < arrival_time[i]:
               UpperCamelCase__				=    arrival_time[i]

          UpperCamelCase__				=    0
          # Index showing the location of the process being performed
          UpperCamelCase__				=    0
          # Saves the current response ratio.
          UpperCamelCase__				=    0
          for i in range(0	,		__a						):
               if finished_process[i] == 0 and arrival_time[i] <= current_time:
                    UpperCamelCase__				=    (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                        i
                    ]
               if response_ratio < temp:
                    UpperCamelCase__				=    temp
                    UpperCamelCase__				=    i

        # Calculate the turn around time
          UpperCamelCase__				=    current_time + burst_time[loc] - arrival_time[loc]
          current_time += burst_time[loc]
          # Indicates that the process has been performed.
          UpperCamelCase__				=    1
          # Increase finished_process_count by 1
          finished_process_count += 1

     return turn_around_time






def 						__magic_name__			(   __a      :	list	,		__a      :	list	,		__a      :	list	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    [0] * no_of_process
     for i in range(0	,		__a						):
          UpperCamelCase__				=    turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
    lowerCamelCase_						     =					5
    lowerCamelCase_						     =					['''A''', '''B''', '''C''', '''D''', '''E''']
    lowerCamelCase_						     =					[1, 2, 3, 4, 5]
    lowerCamelCase_						     =					[1, 2, 3, 4, 5]

    lowerCamelCase_						     =					calculate_turn_around_time(
        process_name, arrival_time, burst_time, no_of_process
    )
    lowerCamelCase_						     =					calculate_waiting_time(
        process_name, turn_around_time, burst_time, no_of_process
    )

    print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
    for i in range(0, no_of_process):
        print(
            f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
            f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
        )

    print(f'average waiting time : {mean(waiting_time):.5f}')
    print(f'average turn around time : {mean(turn_around_time):.5f}')







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()





