






"""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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __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_0_0		,       method='''nm''' )
  __A									= model_fit.predict(1		,       len(__UpperCamelCase )		,       exog=[test_match] )
  return result[0]



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __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 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  train_user.sort()
  __A									= np.percentile(__UpperCamelCase		,       2_5 )
  __A									= np.percentile(__UpperCamelCase		,       7_5 )
  __A									= qa - qa
  __A									= qa - (iqr * 0.1)
  return low_lim



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







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

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

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

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

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












"""simple docstring"""


import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
       import ctypes
       import msvcrt  # noqa


       class 					snake_case		(							ctypes.Structure						):


          '''simple docstring'''

          A_						:	List[str]						 = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= False
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25l''' )
    sys.stdout.flush()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  if os.name == "nt":
    __A									= CursorInfo()
    __A									= ctypes.windll.kernelaa.GetStdHandle(-1_1 )
    ctypes.windll.kernelaa.GetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
    __A									= True
    ctypes.windll.kernelaa.SetConsoleCursorInfo(__UpperCamelCase		,       ctypes.byref(__UpperCamelCase ) )
  elif os.name == "posix":
    sys.stdout.write('''\033[?25h''' )
    sys.stdout.flush()





@contextmanager
def 					lowerCAmelCase				(    ):




  """simple docstring"""







  try:
    hide_cursor()
    yield
  finally:
    show_cursor()




