

'''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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     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__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     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__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     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__			)     ->     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__,    UpperCAmelCase__			)     ->     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)
      __lowerCamelCase						    =      [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
      __lowerCamelCase						    =      pd.DataFrame(
          data_input, columns=['''total_user''', '''total_even''', '''days''']
      )
      __lowerCamelCase						    =      Normalizer().fit_transform(data_input_df.values)
      # split data
      __lowerCamelCase						    =      normalize_df[:, 2].tolist()
      __lowerCamelCase						    =      normalize_df[:, 0].tolist()
      __lowerCamelCase						    =      normalize_df[:, 1].tolist()

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

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

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

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



'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__ = 1_00			)     ->     int:
       A_		   = n * (n + 1) * (2 * n + 1) / 6
       A_		   = (n * (n + 1) / 2) ** 2
       return int(square_of_sum - sum_of_squares			)


if __name__ == "__main__":
      print(f"""{solution() = }""")
