





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 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       list ,     UpperCamelCase__			:       list ,     UpperCamelCase__			:       list ,     UpperCamelCase__			:       list  )->float:
       A__											=						np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCamelCase__  )]  )
       A__											=						np.array(UpperCamelCase__  )
       A__											=						np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() ,     UpperCamelCase__  )  ) ,     x.transpose()  ) ,     UpperCamelCase__  )
       return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]  )


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       list ,     UpperCamelCase__			:       list  )->float:
       A__											=						(1, 2, 1)
       A__											=						(1, 1, 0, 7)
       A__											=						SARIMAX(
           UpperCamelCase__ ,     exog=UpperCamelCase__ ,     order=UpperCamelCase__ ,     seasonal_order=UpperCamelCase__  )
       A__											=						model.fit(disp=UpperCamelCase__ ,     maxiter=6_00 ,     method='''nm'''  )
       A__											=						model_fit.predict(1 ,     len(UpperCamelCase__  ) ,     exog=[test_match]  )
       return result[0]


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       list ,     UpperCamelCase__			:       list  )->float:
       A__											=						SVR(kernel='''rbf''' ,     C=1 ,     gamma=0.1 ,     epsilon=0.1  )
       regressor.fit(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						regressor.predict(UpperCamelCase__  )
       return y_pred[0]


def 	UpperCamelCase__(       UpperCamelCase__			:       list  )->float:
       train_user.sort()
       A__											=						np.percentile(UpperCamelCase__ ,     25  )
       A__											=						np.percentile(UpperCamelCase__ ,     75  )
       A__											=						qa - qa
       A__											=						qa - (iqr * 0.1)
       return low_lim


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       float  )->bool:
       A__											=						0
       A__											=						0
       for i in list_vote:
              if i > actual_result:
                     A__											=						not_safe + 1
              else:
                     if abs(abs(UpperCamelCase__  ) - abs(UpperCamelCase__  )  ) <= 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)
       a__:      List[Any]									=		[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
       a__:      int									=		pd.DataFrame(
           data_input, columns=['total_user', 'total_even', 'days']
       )
       a__:      Optional[int]									=		Normalizer().fit_transform(data_input_df.values)
       # split data
       a__:      int									=		normalize_df[:, 2].tolist()
       a__:      int									=		normalize_df[:, 0].tolist()
       a__:      Union[str, Any]									=		normalize_df[:, 1].tolist()

       # for svr (input variable = total date and total match)
       a__:      Any									=		normalize_df[:, [1, 2]].tolist()
       a__:      Optional[Any]									=		x[: len(x) - 1]
       a__:      Optional[int]									=		x[len(x) - 1 :]

       # for linear regression & sarimax
       a__:      Optional[Any]									=		total_date[: len(total_date) - 1]
       a__:      Any									=		total_user[: len(total_user) - 1]
       a__:      Union[str, Any]									=		total_match[: len(total_match) - 1]

       a__:      str									=		total_date[len(total_date) - 1 :]
       a__:      int									=		total_user[len(total_user) - 1 :]
       a__:      Any									=		total_match[len(total_match) - 1 :]

       # voting system with forecasting
       a__:      List[Any]									=		[
           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
       a__:      List[Any]									=		'' if data_safety_checker(res_vote, tst_user) else 'not '
       print('Today\'s data is {not_str}safe.')












def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Dict:
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       A__											=						len(UpperCamelCase__  )
       A__											=						max(UpperCamelCase__  )
       A__											=						min(UpperCamelCase__  )

       # create the counting array
       A__											=						coll_max + 1 - coll_min
       A__											=						[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,     UpperCamelCase__  ):
              A__											=						counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       A__											=						[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,     UpperCamelCase__  )  ):
              A__											=						collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int]  )->Tuple:
       return "".join([chr(UpperCamelCase__  ) for i in counting_sort([ord(UpperCamelCase__  ) for c in string]  )]  )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       a__:      Dict									=		input('Enter numbers separated by a comma:\n').strip()
       a__:      Any									=		[int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))





