





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_	(		A__		,      A__		,      A__		,      A__		,      A__				)  ->		float:
       a__						:	Optional[Any] =				np.array([[1, item, train_mtch[i]] for i, item in enumerate(A__				)]				)
       a__						:	Dict =				np.array(A__				)
       a__						:	List[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 A_	(		A__		,      A__		,      A__				)  ->		float:
       a__						:	List[Any] =				(1, 2, 1)
       a__						:	Union[str, Any] =				(1, 1, 0, 7)
       a__						:	str =				SARIMAX(
           A__		,      exog=A__		,      order=A__		,      seasonal_order=A__				)
       a__						:	List[str] =				model.fit(disp=A__		,      maxiter=600		,      method='nm'				)
       a__						:	List[Any] =				model_fit.predict(1		,      len(A__				)		,      exog=[test_match]				)
       return result[0]



def A_	(		A__		,      A__		,      A__				)  ->		float:
       a__						:	Optional[Any] =				SVR(kernel='rbf'		,      C=1		,      gamma=0.1		,      epsilon=0.1				)
       regressor.fit(A__		,      A__				)
       a__						:	Optional[int] =				regressor.predict(A__				)
       return y_pred[0]



def A_	(		A__				)  ->		float:
       train_user.sort()
       a__						:	Union[str, Any] =				np.percentile(A__		,      25				)
       a__						:	Dict =				np.percentile(A__		,      75				)
       a__						:	Union[str, Any] =				qa - qa
       a__						:	str =				qa - (iqr * 0.1)
       return low_lim



def A_	(		A__		,      A__				)  ->		bool:
       a__						:	Optional[Any] =				0
       a__						:	List[Any] =				0
       for i in list_vote:
              if i > actual_result:
                     a__						:	Tuple =				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      :       Optional[int]									=  [[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]]
    lowercase      :       List[str]									=  pd.DataFrame(
        data_input, columns=["""total_user""", """total_even""", """days"""]
    )
    lowercase      :       str									=  Normalizer().fit_transform(data_input_df.values)
    # split data
    lowercase      :       List[str]									=  normalize_df[:, 2].tolist()
    lowercase      :       int									=  normalize_df[:, 0].tolist()
    lowercase      :       int									=  normalize_df[:, 1].tolist()

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

    # for linear regression & sarimax
    lowercase      :       Union[str, Any]									=  total_date[: len(total_date) - 1]
    lowercase      :       Optional[Any]									=  total_user[: len(total_user) - 1]
    lowercase      :       List[Any]									=  total_match[: len(total_match) - 1]

    lowercase      :       List[Any]									=  total_date[len(total_date) - 1 :]
    lowercase      :       Optional[Any]									=  total_user[len(total_user) - 1 :]
    lowercase      :       Union[str, Any]									=  total_match[len(total_match) - 1 :]

    # voting system with forecasting
    lowercase      :       int									=  [
        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      :       Dict									=  """""" if data_safety_checker(res_vote, tst_user) else """not """
    print("""Today's data is {not_str}safe.""")






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase		       =							{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''EncodecModel''',
     '''EncodecPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 __lowerCAmelCase		       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


