





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__       (    SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list						)  ->  float:






   """simple docstring"""







   _UpperCAmelCase           =   np.array([[1, item, train_mtch[i]] for i, item in enumerate(SCREAMING_SNAKE_CASE_						)]						)
   _UpperCAmelCase           =   np.array(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   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      A__       (    SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list						)  ->  float:






   """simple docstring"""







   _UpperCAmelCase           =   (1, 2, 1)
   _UpperCAmelCase           =   (1, 1, 0, 7)
   _UpperCAmelCase           =   SARIMAX(
       SCREAMING_SNAKE_CASE_				,	exog=SCREAMING_SNAKE_CASE_				,	order=SCREAMING_SNAKE_CASE_				,	seasonal_order=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   model.fit(disp=SCREAMING_SNAKE_CASE_				,	maxiter=6_00				,	method='''nm'''						)
   _UpperCAmelCase           =   model_fit.predict(1				,	len(SCREAMING_SNAKE_CASE_						)				,	exog=[test_match]						)
   return result[0]
def      A__       (    SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list						)  ->  float:






   """simple docstring"""







   _UpperCAmelCase           =   SVR(kernel='''rbf'''				,	C=1				,	gamma=0.1				,	epsilon=0.1						)
   regressor.fit(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   regressor.predict(SCREAMING_SNAKE_CASE_						)
   return y_pred[0]
def      A__       (    SCREAMING_SNAKE_CASE_   :      list						)  ->  float:






   """simple docstring"""







   train_user.sort()
   _UpperCAmelCase           =   np.percentile(SCREAMING_SNAKE_CASE_				,	25						)
   _UpperCAmelCase           =   np.percentile(SCREAMING_SNAKE_CASE_				,	75						)
   _UpperCAmelCase           =   qa - qa
   _UpperCAmelCase           =   qa - (iqr * 0.1)
   return low_lim
def      A__       (    SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  bool:






   """simple docstring"""







   _UpperCAmelCase           =   0
   _UpperCAmelCase           =   0
   for i in list_vote:
      if i > actual_result:
         _UpperCAmelCase           =   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)
   UpperCAmelCase_           =	[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 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.")






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=A__    )
class 			__UpperCamelCase    (							A__    ):
     __A	:  str							  =   field(default="""language-modeling"""	, metadata={"""include_in_asdict_even_if_is_default""": True}    )

     __A	:  ClassVar[Features]							  =   Features({"""text""": Value("""string"""    )}    )
     __A	:  ClassVar[Features]							  =   Features({}    )
     __A	:  str							  =   "text"





     @property
     def 		UpperCamelCase(     self					):
        return {self.text_column: "text"}