'''simple docstring'''





import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : list					,    snake_case_       : list					,    snake_case_       : list					,    snake_case_       : list			)    ->      float:


     '''simple docstring'''




     UpperCAmelCase_           =   np.array([[1, item, train_mtch[i]] for i, item in enumerate(snake_case_			)]			)
     UpperCAmelCase_           =   np.array(snake_case_			)
     UpperCAmelCase_           =   np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()					,    snake_case_			)			)					,    x.transpose()			)					,    snake_case_			)
     return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]			)





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : list					,    snake_case_       : list			)    ->      float:


     '''simple docstring'''




     UpperCAmelCase_           =   (1, 2, 1)
     UpperCAmelCase_           =   (1, 1, 0, 7)
     UpperCAmelCase_           =   SARIMAX(
         snake_case_					,    exog=snake_case_					,    order=snake_case_					,    seasonal_order=snake_case_			)
     UpperCAmelCase_           =   model.fit(disp=snake_case_					,    maxiter=6_00					,    method="nm"			)
     UpperCAmelCase_           =   model_fit.predict(1					,    len(snake_case_			)					,    exog=[test_match]			)
     return result[0]





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : list					,    snake_case_       : list			)    ->      float:


     '''simple docstring'''




     UpperCAmelCase_           =   SVR(kernel="rbf"					,    C=1					,    gamma=0.1					,    epsilon=0.1			)
     regressor.fit(snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   regressor.predict(snake_case_			)
     return y_pred[0]





def 	lowerCAmelCase_				(					snake_case_       : list			)    ->      float:


     '''simple docstring'''




     train_user.sort()
     UpperCAmelCase_           =   np.percentile(snake_case_					,    25			)
     UpperCAmelCase_           =   np.percentile(snake_case_					,    75			)
     UpperCAmelCase_           =   qa - qa
     UpperCAmelCase_           =   qa - (iqr * 0.1)
     return low_lim





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : float			)    ->      bool:


     '''simple docstring'''




     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0
     for i in list_vote:
          if i > actual_result:
               UpperCAmelCase_           =   not_safe + 1
          else:
               if abs(abs(snake_case_			) - abs(snake_case_			)			) <= 0.1:
                    safe += 1
               else:
                    not_safe += 1
     return safe > not_safe


if __name__ == "__main__":
    # data_input_df = pd.read_csv("ex_data.csv", header=None)
    SCREAMING_SNAKE_CASE_:     List[Any]  =[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
    SCREAMING_SNAKE_CASE_:     Dict  =pd.DataFrame(
        data_input, columns=['total_user', 'total_even', 'days']
    )
    SCREAMING_SNAKE_CASE_:     Any  =Normalizer().fit_transform(data_input_df.values)
    # split data
    SCREAMING_SNAKE_CASE_:     List[str]  =normalize_df[:, 2].tolist()
    SCREAMING_SNAKE_CASE_:     str  =normalize_df[:, 0].tolist()
    SCREAMING_SNAKE_CASE_:     str  =normalize_df[:, 1].tolist()

    # for svr (input variable = total date and total match)
    SCREAMING_SNAKE_CASE_:     Dict  =normalize_df[:, [1, 2]].tolist()
    SCREAMING_SNAKE_CASE_:     Tuple  =x[: len(x) - 1]
    SCREAMING_SNAKE_CASE_:     List[Any]  =x[len(x) - 1 :]

    # for linear regression & sarimax
    SCREAMING_SNAKE_CASE_:     int  =total_date[: len(total_date) - 1]
    SCREAMING_SNAKE_CASE_:     Dict  =total_user[: len(total_user) - 1]
    SCREAMING_SNAKE_CASE_:     Tuple  =total_match[: len(total_match) - 1]

    SCREAMING_SNAKE_CASE_:     Optional[int]  =total_date[len(total_date) - 1 :]
    SCREAMING_SNAKE_CASE_:     str  =total_user[len(total_user) - 1 :]
    SCREAMING_SNAKE_CASE_:     int  =total_match[len(total_match) - 1 :]

    # voting system with forecasting
    SCREAMING_SNAKE_CASE_:     Optional[int]  =[
        linear_regression_prediction(
            trn_date, trn_user, trn_match, tst_date, tst_match
        ),
        sarimax_predictor(trn_user, trn_match, tst_match),
        support_vector_regressor(x_train, x_test, trn_user),
    ]

    # check the safety of today's data
    SCREAMING_SNAKE_CASE_:     Optional[int]  ='' if data_safety_checker(res_vote, tst_user) else 'not '
    print('Today\'s data is {not_str}safe.')





'''simple docstring'''





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_:     Optional[int]  =Lock()





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Dict					,    snake_case_       : Optional[int]					,    snake_case_       : Tuple					,    snake_case_       : Any					,    snake_case_       : Optional[Any]					,    snake_case_       : Union[str, Any]			)    ->      Optional[Any]:


     '''simple docstring'''




     global process_lock

     # we perform n swaps since after n swaps we know we are sorted
     # we *could* stop early if we are sorted already, but it takes as long to
     # find out we are sorted as it does to sort the list with this algorithm
     for i in range(0					,    10			):
          if (i + position) % 2 == 0 and r_send is not None:
               # send your value to your right neighbor
               process_lock.acquire()
               r_send[1].send(snake_case_			)
               process_lock.release()

               # receive your right neighbor's value
               process_lock.acquire()
               UpperCAmelCase_           =   rr_cv[0].recv()
               process_lock.release()

               # take the lower value since you are on the left
               UpperCAmelCase_           =   min(snake_case_					,    snake_case_			)
          elif (i + position) % 2 != 0 and l_send is not None:
               # send your value to your left neighbor
               process_lock.acquire()
               l_send[1].send(snake_case_			)
               process_lock.release()

               # receive your left neighbor's value
               process_lock.acquire()
               UpperCAmelCase_           =   lr_cv[0].recv()
               process_lock.release()

               # take the higher value since you are on the right
               UpperCAmelCase_           =   max(snake_case_					,    snake_case_			)
    # after all swaps are performed, send the values back to main
     result_pipe[1].send(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   []
     # initialize the list of pipes where the values will be retrieved
     for _ in arr:
          result_pipe.append(Pipe()			)
     # creates the processes
     # the first and last process only have one neighbor so they are made outside
     # of the loop
     UpperCAmelCase_           =   Pipe()
     UpperCAmelCase_           =   Pipe()
     process_array_.append(
         Process(
             target=snake_case_					,    args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,    )			)
     UpperCAmelCase_           =   temp_rs
     UpperCAmelCase_           =   temp_rr

     for i in range(1					,    len(snake_case_			) - 1			):
          UpperCAmelCase_           =   Pipe()
          UpperCAmelCase_           =   Pipe()
          process_array_.append(
              Process(
                  target=snake_case_					,    args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,    )			)
          UpperCAmelCase_           =   temp_rs
          UpperCAmelCase_           =   temp_rr

     process_array_.append(
         Process(
             target=snake_case_					,    args=(
                 len(snake_case_			) - 1,
                 arr[len(snake_case_			) - 1],
                 temp_ls,
                 None,
                 temp_lr,
                 None,
                 result_pipe[len(snake_case_			) - 1],
             )					,    )			)

     # start the processes
     for p in process_array_:
          p.start()

     # wait for the processes to end and write their values to the list
     for p in range(0					,    len(snake_case_			)			):
          UpperCAmelCase_           =   result_pipe[p][0].recv()
          process_array_[p].join()
     return arr





def 	lowerCAmelCase_				(					)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   list(range(10					,    0					,    -1			)			)
     print("Initial List"			)
     print(*snake_case_			)
     UpperCAmelCase_           =   odd_even_transposition(snake_case_			)
     print("Sorted List\n"			)
     print(*snake_case_			)


if __name__ == "__main__":
    main()




