






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     _A  (							SCREAMING_SNAKE_CASE__    :      str	,    SCREAMING_SNAKE_CASE__    :      Optional[Any]	,    SCREAMING_SNAKE_CASE__    :      List[str]	,    SCREAMING_SNAKE_CASE__    :      Dict	,    SCREAMING_SNAKE_CASE__    :      List[Any]			):
    UpperCamelCase			:Dict      				=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(SCREAMING_SNAKE_CASE__			)]			)
    UpperCamelCase			:Tuple      				=  np.array(SCREAMING_SNAKE_CASE__			)
    UpperCamelCase			:List[Any]      				=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()	,    SCREAMING_SNAKE_CASE__			)			)	,    x.transpose()			)	,    SCREAMING_SNAKE_CASE__			)
    return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]			)


def     _A  (							SCREAMING_SNAKE_CASE__    :      Any	,    SCREAMING_SNAKE_CASE__    :      List[str]	,    SCREAMING_SNAKE_CASE__    :      Any			):
    UpperCamelCase			:Optional[int]      				=  (1, 2, 1)
    UpperCamelCase			:List[Any]      				=  (1, 1, 0, 7)
    UpperCamelCase			:int      				=  SARIMAX(
        SCREAMING_SNAKE_CASE__	,    exog=SCREAMING_SNAKE_CASE__	,    order=SCREAMING_SNAKE_CASE__	,    seasonal_order=SCREAMING_SNAKE_CASE__			)
    UpperCamelCase			:Optional[Any]      				=  model.fit(disp=SCREAMING_SNAKE_CASE__	,    maxiter=600	,    method='''nm'''			)
    UpperCamelCase			:Any      				=  model_fit.predict(1	,    len(SCREAMING_SNAKE_CASE__			)	,    exog=[test_match]			)
    return result[0]


def     _A  (							SCREAMING_SNAKE_CASE__    :      Dict	,    SCREAMING_SNAKE_CASE__    :      List[str]	,    SCREAMING_SNAKE_CASE__    :      str			):
    UpperCamelCase			:int      				=  SVR(kernel='''rbf'''	,    C=1	,    gamma=0.1	,    epsilon=0.1			)
    regressor.fit(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__			)
    UpperCamelCase			:Any      				=  regressor.predict(SCREAMING_SNAKE_CASE__			)
    return y_pred[0]


def     _A  (							SCREAMING_SNAKE_CASE__    :      List[Any]			):
    train_user.sort()
    UpperCamelCase			:List[Any]      				=  np.percentile(SCREAMING_SNAKE_CASE__	,    25			)
    UpperCamelCase			:Union[str, Any]      				=  np.percentile(SCREAMING_SNAKE_CASE__	,    75			)
    UpperCamelCase			:Optional[Any]      				=  qa - qa
    UpperCamelCase			:Union[str, Any]      				=  qa - (iqr * 0.1)
    return low_lim


def     _A  (							SCREAMING_SNAKE_CASE__    :      Union[str, Any]	,    SCREAMING_SNAKE_CASE__    :      Dict			):
    UpperCamelCase			:Optional[Any]      				=  0
    UpperCamelCase			:Optional[Any]      				=  0
    for i in list_vote:
        if i > actual_result:
            UpperCamelCase			:List[str]      				=  not_safe + 1
        else:
            if abs(abs(SCREAMING_SNAKE_CASE__			) - abs(SCREAMING_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)
 __snake_case				   =    [[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
 __snake_case				   =    pd.DataFrame(
     data_input, columns=["""total_user""", """total_even""", """days"""]
 )
 __snake_case				   =    Normalizer().fit_transform(data_input_df.values)
 # split data
 __snake_case				   =    normalize_df[:, 2].tolist()
 __snake_case				   =    normalize_df[:, 0].tolist()
 __snake_case				   =    normalize_df[:, 1].tolist()

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

 # for linear regression & sarimax
 __snake_case				   =    total_date[: len(total_date) - 1]
 __snake_case				   =    total_user[: len(total_user) - 1]
 __snake_case				   =    total_match[: len(total_match) - 1]

 __snake_case				   =    total_date[len(total_date) - 1 :]
 __snake_case				   =    total_user[len(total_user) - 1 :]
 __snake_case				   =    total_match[len(total_match) - 1 :]

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












def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	[]
   a__						:    str   		=	set({"(", "[", "{"} )
   a__						:    List[str]   		=	set({")", "]", "}"} )
   a__						:    int   		=	{"{": "}", "[": "]", "(": ")"}

   for i in range(len(lowerCamelCase ) ):
      if s[i] in open_brackets:
         stack.append(s[i] )

      elif s[i] in closed_brackets and (
          len(lowerCamelCase ) == 0 or (len(lowerCamelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
      ):
         return False

   return len(lowerCamelCase ) == 0






def    _A	(   ):
   a__						:    Optional[int]   		=	input("Enter sequence of brackets: " )
   if is_balanced(lowerCamelCase ):
      print(lowerCamelCase				,			"is balanced" )
   else:
      print(lowerCamelCase				,			"is not balanced" )


if __name__ == "__main__":
  main()

