


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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  float:




      """simple docstring"""

      snake_case_	=				np.array([[1, item, train_mtch[i]] for i, item in enumerate(SCREAMING_SNAKE_CASE )] )
      snake_case_	=				np.array(SCREAMING_SNAKE_CASE )
      snake_case_	=				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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  float:




      """simple docstring"""

      snake_case_	=				(1, 2, 1)
      snake_case_	=				(1, 1, 0, 7)
      snake_case_	=				SARIMAX(
          SCREAMING_SNAKE_CASE , exog=SCREAMING_SNAKE_CASE , order=SCREAMING_SNAKE_CASE , seasonal_order=SCREAMING_SNAKE_CASE )
      snake_case_	=				model.fit(disp=SCREAMING_SNAKE_CASE , maxiter=600 , method='''nm''' )
      snake_case_	=				model_fit.predict(1 , len(SCREAMING_SNAKE_CASE ) , exog=[test_match] )
      return result[0]






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  float:




      """simple docstring"""

      snake_case_	=				SVR(kernel='''rbf''' , C=1 , gamma=0.1 , epsilon=0.1 )
      regressor.fit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      snake_case_	=				regressor.predict(SCREAMING_SNAKE_CASE )
      return y_pred[0]






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  float:




      """simple docstring"""

      train_user.sort()
      snake_case_	=				np.percentile(SCREAMING_SNAKE_CASE , 25 )
      snake_case_	=				np.percentile(SCREAMING_SNAKE_CASE , 75 )
      snake_case_	=				qa - qa
      snake_case_	=				qa - (iqr * 0.1)
      return low_lim






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  bool:




      """simple docstring"""

      snake_case_	=				0
      snake_case_	=				0
      for i in list_vote:
            if i > actual_result:
                  snake_case_	=				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)
   UpperCAmelCase      =   [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
   UpperCAmelCase      =   pd.DataFrame(
       data_input, columns=["""total_user""", """total_even""", """days"""]
   )
   UpperCAmelCase      =   Normalizer().fit_transform(data_input_df.values)
   # split data
   UpperCAmelCase      =   normalize_df[:, 2].tolist()
   UpperCAmelCase      =   normalize_df[:, 0].tolist()
   UpperCAmelCase      =   normalize_df[:, 1].tolist()

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

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

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

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



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
            snake_case_	=				f'''Input value of [number={number}] must be an integer'''
            raise TypeError(SCREAMING_SNAKE_CASE )

      if number < 1:
            snake_case_	=				f'''Input value of [number={number}] must be > 0'''
            raise ValueError(SCREAMING_SNAKE_CASE )

      snake_case_	=				1

      for i in range(1 , SCREAMING_SNAKE_CASE ):
            current_number *= 4 * i - 2
            current_number //= i + 1

      return current_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()