





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list     )      ->						float:
  _lowercase							= np.array([[1, item, train_mtch[i]] for i, item in enumerate(snake_case__     )]     )
  _lowercase							= np.array(snake_case__     )
  _lowercase							= 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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list     )      ->						float:
  _lowercase							= (1, 2, 1)
  _lowercase							= (1, 1, 0, 7)
  _lowercase							= SARIMAX(
      snake_case__			,							exog=snake_case__			,							order=snake_case__			,							seasonal_order=snake_case__     )
  _lowercase							= model.fit(disp=snake_case__			,							maxiter=600			,							method='nm'     )
  _lowercase							= model_fit.predict(1			,							len(snake_case__     )			,							exog=[test_match]     )
  return result[0]

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

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :list			,							snake_case__  :float     )      ->						bool:
  _lowercase							= 0
  _lowercase							= 0
  for i in list_vote:
    if i > actual_result:
      _lowercase							= 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)
      snake_case 				=       [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 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.""")












from __future__ import annotations

from collections.abc import Generator

def 							SCREAMING_SNAKE_CASE__       (  )      ->						Generator[int, None, None]:
  _lowercase							= {}
  _lowercase							= 2
  while True:
    _lowercase							= factor_map.pop(snake_case__			,							snake_case__     )
    if factor:
      _lowercase							= factor + prime
      while x in factor_map:
        x += factor
      _lowercase							= factor
    else:
      _lowercase							= prime
      yield prime
    prime += 1

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :float = 1E10     )      ->						int:
  _lowercase							= sieve()

  _lowercase							= 1
  while True:
    _lowercase							= next(snake_case__     )
    if (2 * prime * n) > limit:
      return n
    # Ignore the next prime as the reminder will be 2.
    next(snake_case__     )
    n += 2


if __name__ == "__main__":
      print(solution())