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_			(					__A,  __A,  __A,  __A,  __A							)					->    float:







      '''simple docstring'''


      UpperCAmelCase__								=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__A							)]							)
      UpperCAmelCase__								=  np.array(__A							)
      UpperCAmelCase__								=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(),  __A							)							),  x.transpose()							),  __A							)
      return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]							)




def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    float:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    float:







      '''simple docstring'''


      UpperCAmelCase__								=  SVR(kernel="rbf",  C=1,  gamma=0.1,  epsilon=0.1							)
      regressor.fit(__A,  __A							)
      UpperCAmelCase__								=  regressor.predict(__A							)
      return y_pred[0]




def        lowerCAmelCase_			(					__A							)					->    float:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A							)					->    bool:







      '''simple docstring'''


      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  0
      for i in list_vote:
            if i > actual_result:
                  UpperCAmelCase__								=  not_safe + 1
            else:
                  if abs(abs(__A							) - abs(__A							)							) <= 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_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]]
      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.')

from __future__ import annotations




def        lowerCAmelCase_			(					__A							)					->    list[int]:







      '''simple docstring'''


      if len(__A							) == 0:
            return array

      UpperCAmelCase__		,				UpperCAmelCase__								=  min(__A							), max(__A							)

      # Compute the variables
      UpperCAmelCase__								=  _max - _min + 1
      UpperCAmelCase__		,				UpperCAmelCase__								=  [0] * holes_range, [0] * holes_range

      # Make the sorting.
      for i in array:
            UpperCAmelCase__								=  i - _min
            UpperCAmelCase__								=  i
            holes_repeat[index] += 1

      # Makes the array back by replacing the numbers.
      UpperCAmelCase__								=  0
      for i in range(__A							):
            while holes_repeat[i] > 0:
                  UpperCAmelCase__								=  holes[i]
                  index += 1
                  holes_repeat[i] -= 1

    # Returns the sorted array.
      return array


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCamelCase__    							= input('Enter numbers separated by comma:\n')
      UpperCamelCase__    							= [int(x) for x in user_input.split(',')]
      print(pigeon_sort(unsorted))



