





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




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     float:
  lowercase__			    =						SVR(kernel='rbf'	,		C=1	,		gamma=0.1	,		epsilon=0.1	)
  regressor.fit(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						regressor.predict(_SCREAMING_SNAKE_CASE	)
  return y_pred[0]




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     float:
  train_user.sort()
  lowercase__			    =						np.percentile(_SCREAMING_SNAKE_CASE	,		25	)
  lowercase__			    =						np.percentile(_SCREAMING_SNAKE_CASE	,		75	)
  lowercase__			    =						qa - qa
  lowercase__			    =						qa - (iqr * 0.1)
  return low_lim




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     bool:
  lowercase__			    =						0
  lowercase__			    =						0
  for i in list_vote:
    if i > actual_result:
      lowercase__			    =						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)
      lowercase_					     =	[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 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.""")









class        SCREAMING_SNAKE_CASE     :  # Public class to implement a graph


   def __init__(					self    :       int    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						row
     lowercase__			    =						col
     lowercase__			    =						graph


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     bool:





     """simple docstring"""







     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     lowercase__			    =						[-1, 0, 1, -1, 1, -1, 0, 1]
     lowercase__			    =						True  # Make those cells visited
     for k in range(8 ):
       if self.is_safe(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a ):
         self.diffs(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     int:  # And finally, count all islands.





     """simple docstring"""







     lowercase__			    =						[[False for j in range(self.COL )] for i in range(self.ROW )]
     lowercase__			    =						0
     for i in range(self.ROW ):
       for j in range(self.COL ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(a    ,  a    ,  a )
           count += 1
     return count


