






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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							float:
  UpperCamelCase_:   Any        =							np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCAmelCase_    )]    )
  UpperCamelCase_:   int        =							np.array(lowerCAmelCase_    )
  UpperCamelCase_:   List[str]        =							np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()      ,       lowerCAmelCase_    )    )      ,       x.transpose()    )      ,       lowerCAmelCase_    )
  return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							float:
  UpperCamelCase_:   Optional[int]        =							(1, 2, 1)
  UpperCamelCase_:   Tuple        =							(1, 1, 0, 7)
  UpperCamelCase_:   Optional[int]        =							SARIMAX(
      lowerCAmelCase_      ,       exog=lowerCAmelCase_      ,       order=lowerCAmelCase_      ,       seasonal_order=lowerCAmelCase_    )
  UpperCamelCase_:   Optional[int]        =							model.fit(disp=lowerCAmelCase_      ,       maxiter=6_0_0      ,       method='nm'    )
  UpperCamelCase_:   Any        =							model_fit.predict(1      ,       len(lowerCAmelCase_    )      ,       exog=[test_match]    )
  return result[0]

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							float:
  UpperCamelCase_:   int        =							SVR(kernel='rbf'      ,       C=1      ,       gamma=0.1      ,       epsilon=0.1    )
  regressor.fit(lowerCAmelCase_      ,       lowerCAmelCase_    )
  UpperCamelCase_:   Union[str, Any]        =							regressor.predict(lowerCAmelCase_    )
  return y_pred[0]

def     snake_case   (UpperCAmelCase__    )			->							float:
  train_user.sort()
  UpperCamelCase_:   int        =							np.percentile(lowerCAmelCase_      ,       2_5    )
  UpperCamelCase_:   List[str]        =							np.percentile(lowerCAmelCase_      ,       7_5    )
  UpperCamelCase_:   Dict        =							qa - qa
  UpperCamelCase_:   str        =							qa - (iqr * 0.1)
  return low_lim

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							bool:
  UpperCamelCase_:   Union[str, Any]        =							0
  UpperCamelCase_:   Optional[int]        =							0
  for i in list_vote:
    if i > actual_result:
      UpperCamelCase_:   Any        =							not_safe + 1
    else:
      if abs(abs(lowerCAmelCase_    ) - abs(lowerCAmelCase_    )    ) <= 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_						:						Tuple						      =					[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
   A_						:						str						      =					pd.DataFrame(
       data_input, columns=['total_user', 'total_even', 'days']
   )
   A_						:						Optional[int]						      =					Normalizer().fit_transform(data_input_df.values)
   # split data
   A_						:						Union[str, Any]						      =					normalize_df[:, 2].tolist()
   A_						:						Optional[int]						      =					normalize_df[:, 0].tolist()
   A_						:						Any						      =					normalize_df[:, 1].tolist()

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

   # for linear regression & sarimax
   A_						:						Any						      =					total_date[: len(total_date) - 1]
   A_						:						int						      =					total_user[: len(total_user) - 1]
   A_						:						List[str]						      =					total_match[: len(total_match) - 1]

   A_						:						Any						      =					total_date[len(total_date) - 1 :]
   A_						:						str						      =					total_user[len(total_user) - 1 :]
   A_						:						Dict						      =					total_match[len(total_match) - 1 :]

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






import operator as op


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			lambda lowerCAmelCase_ ,							lowerCAmelCase_	: int(x / y	)  # noqa: E731 integer division operation
    SCREAMING_SNAKE_CASE__											=			{
        '''^''': op.pow,
        '''*''': op.mul,
        '''/''': div,
        '''+''': op.add,
        '''-''': op.sub,
    }  # operators & their respective operation

    # print table header
    print('''Symbol'''.center(8	) ,							'''Action'''.center(1_2	) ,							'''Stack''' ,							sep=''' | '''	)
    print('''-''' * (3_0 + len(lowerCAmelCase_	))	)

    for x in post_fix:
        if x.isdigit():  # if x in digit
            stack.append(lowerCAmelCase_	)  # append x to stack
            # output in tabular format
            print(x.rjust(8	) ,							('''push(''' + x + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)
        else:
            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + a + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            stack.append(
                str(opr[x](int(lowerCAmelCase_	) ,							int(lowerCAmelCase_	)	)	)	)  # evaluate the 2 values popped from stack & push result to stack
            # output in tabular format
            print(
                x.rjust(8	) ,							('''push(''' + a + x + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | ''' ,							)

    return int(stack[0]	)


if __name__ == "__main__":
  _A					:				Dict				=			input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
  print("""\n\tResult = """, solve(Postfix))
