



'''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 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> float:
  __lowerCamelCase			:			List[Any]       			=					np.array([[1, item, train_mtch[i]] for i, item in enumerate(_lowerCAmelCase			)]			)
  __lowerCamelCase			:			Optional[Any]       			=					np.array(_lowerCAmelCase			)
  __lowerCamelCase			:			List[Any]       			=					np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,_lowerCAmelCase			)			)			,x.transpose()			)			,_lowerCAmelCase			)
  return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]			)

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> float:
  __lowerCamelCase			:			Optional[int]       			=					(1, 2, 1)
  __lowerCamelCase			:			Tuple       			=					(1, 1, 0, 7)
  __lowerCamelCase			:			Union[str, Any]       			=					SARIMAX(
      _lowerCAmelCase			,exog=_lowerCAmelCase			,order=_lowerCAmelCase			,seasonal_order=_lowerCAmelCase			)
  __lowerCamelCase			:			Optional[int]       			=					model.fit(disp=_lowerCAmelCase			,maxiter=600			,method='nm'			)
  __lowerCamelCase			:			List[str]       			=					model_fit.predict(1			,len(_lowerCAmelCase			)			,exog=[test_match]			)
  return result[0]

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> float:
  __lowerCamelCase			:			List[Any]       			=					SVR(kernel='rbf'			,C=1			,gamma=0.1			,epsilon=0.1			)
  regressor.fit(_lowerCAmelCase			,_lowerCAmelCase			)
  __lowerCamelCase			:			List[Any]       			=					regressor.predict(_lowerCAmelCase			)
  return y_pred[0]

def 			a_	(				_lowerCAmelCase			) -> float:
  train_user.sort()
  __lowerCamelCase			:			str       			=					np.percentile(_lowerCAmelCase			,25			)
  __lowerCamelCase			:			Optional[Any]       			=					np.percentile(_lowerCAmelCase			,75			)
  __lowerCamelCase			:			Optional[Any]       			=					qa - qa
  __lowerCamelCase			:			List[Any]       			=					qa - (iqr * 0.1)
  return low_lim

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> bool:
  __lowerCamelCase			:			int       			=					0
  __lowerCamelCase			:			Optional[Any]       			=					0
  for i in list_vote:
    if i > actual_result:
      __lowerCamelCase			:			Optional[Any]       			=					not_safe + 1
    else:
      if abs(abs(_lowerCAmelCase			) - abs(_lowerCAmelCase			)			) <= 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											=		[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 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.')











'''simple docstring'''





def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> tuple[float, float]:

  # Check if the input is valid
  if not len(_lowerCAmelCase			) == len(_lowerCAmelCase			) == 3:
    raise ValueError('Please enter a valid equation.'			)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError('Both a & b of two equations can\'t be zero.'			)

  # Extract the coefficients
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					equationa
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Any       			=					equationa

  # Calculate the determinants of the matrices
  __lowerCamelCase			:			Union[str, Any]       			=					aa * ba - aa * ba
  __lowerCamelCase			:			Dict       			=					ca * ba - ca * ba
  __lowerCamelCase			:			List[str]       			=					aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError('Infinite solutions. (Consistent system)'			)
    else:
      raise ValueError('No solution. (Inconsistent system)'			)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      __lowerCamelCase			:			Tuple       			=					determinant_x / determinant
      __lowerCamelCase			:			Union[str, Any]       			=					determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)






