


"""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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				float:
  '''simple docstring'''







  a						:		Any											=      np.array([[1, item, train_mtch[i]] for i, item in enumerate(A_	)]	)
  a						:		Tuple											=      np.array(A_	)
  a						:		Union[str, Any]											=      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 					lowercase      (    A_  ,       A_  ,       A_	)->				float:
  '''simple docstring'''







  a						:		Optional[Any]											=      (1, 2, 1)
  a						:		int											=      (1, 1, 0, 7)
  a						:		Tuple											=      SARIMAX(
      A_  ,       exog=A_  ,       order=A_  ,       seasonal_order=A_	)
  a						:		Optional[Any]											=      model.fit(disp=A_  ,       maxiter=600  ,       method="nm"	)
  a						:		List[str]											=      model_fit.predict(1  ,       len(A_	)  ,       exog=[test_match]	)
  return result[0]


def 					lowercase      (    A_  ,       A_  ,       A_	)->				float:
  '''simple docstring'''







  a						:		Tuple											=      SVR(kernel="rbf"  ,       C=1  ,       gamma=0.1  ,       epsilon=0.1	)
  regressor.fit(A_  ,       A_	)
  a						:		str											=      regressor.predict(A_	)
  return y_pred[0]


def 					lowercase      (    A_	)->				float:
  '''simple docstring'''







  train_user.sort()
  a						:		Optional[int]											=      np.percentile(A_  ,       25	)
  a						:		str											=      np.percentile(A_  ,       75	)
  a						:		Optional[Any]											=      qa - qa
  a						:		Tuple											=      qa - (iqr * 0.1)
  return low_lim


def 					lowercase      (    A_  ,       A_	)->				bool:
  '''simple docstring'''







  a						:		Optional[Any]											=      0
  a						:		List[str]											=      0
  for i in list_vote:
    if i > actual_result:
      a						:		Optional[int]											=      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)
 __lowercase    =				[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 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 warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


__lowercase    =				logging.get_logger(__name__)






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   List[Any]    ,			*__UpperCAmelCase    :   int    ,			**__UpperCAmelCase    :   List[Any]):
      warnings.warn(
          "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use DPTImageProcessor instead."    ,			__UpperCAmelCase    ,			)
      super().__init__(*__UpperCAmelCase    ,			**__UpperCAmelCase)






