



"""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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						float:







  '''simple docstring'''


  lowercase_								= np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase							)]							)
  lowercase_								= np.array(__lowerCAmelCase							)
  lowercase_								= 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						float:







  '''simple docstring'''


  lowercase_								= (1, 2, 1)
  lowercase_								= (1, 1, 0, 7)
  lowercase_								= SARIMAX(
      __lowerCAmelCase ,  exog=__lowerCAmelCase ,  order=__lowerCAmelCase ,  seasonal_order=__lowerCAmelCase							)
  lowercase_								= model.fit(disp=__lowerCAmelCase ,  maxiter=6_00 ,  method="""nm"""							)
  lowercase_								= model_fit.predict(1 ,  len(__lowerCAmelCase							) ,  exog=[test_match]							)
  return result[0]


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						float:







  '''simple docstring'''


  lowercase_								= SVR(kernel="""rbf""" ,  C=1 ,  gamma=0.1 ,  epsilon=0.1							)
  regressor.fit(__lowerCAmelCase ,  __lowerCAmelCase							)
  lowercase_								= regressor.predict(__lowerCAmelCase							)
  return y_pred[0]


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						float:







  '''simple docstring'''


  train_user.sort()
  lowercase_								= np.percentile(__lowerCAmelCase ,  25							)
  lowercase_								= np.percentile(__lowerCAmelCase ,  75							)
  lowercase_								= qa - qa
  lowercase_								= qa - (iqr * 0.1)
  return low_lim


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						bool:







  '''simple docstring'''


  lowercase_								= 0
  lowercase_								= 0
  for i in list_vote:
    if i > actual_result:
      lowercase_								= 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						:		int			= [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
       UpperCAmelCase						:		str			= pd.DataFrame(
           data_input, columns=["total_user", "total_even", "days"]
       )
       UpperCAmelCase						:		Tuple			= Normalizer().fit_transform(data_input_df.values)
       # split data
       UpperCAmelCase						:		Union[str, Any]			= normalize_df[:, 2].tolist()
       UpperCAmelCase						:		Any			= normalize_df[:, 0].tolist()
       UpperCAmelCase						:		List[Any]			= normalize_df[:, 1].tolist()

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

       # for linear regression & sarimax
       UpperCAmelCase						:		List[Any]			= total_date[: len(total_date) - 1]
       UpperCAmelCase						:		Dict			= total_user[: len(total_user) - 1]
       UpperCAmelCase						:		Optional[Any]			= total_match[: len(total_match) - 1]

       UpperCAmelCase						:		Dict			= total_date[len(total_date) - 1 :]
       UpperCAmelCase						:		List[Any]			= total_user[len(total_user) - 1 :]
       UpperCAmelCase						:		int			= total_match[len(total_match) - 1 :]

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






"""simple docstring"""
import os
from collections.abc import Iterator


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = "."							) ->						Iterator[str]:







  '''simple docstring'''


  for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase							):
    lowercase_								= [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
    for filename in filenames:
      if filename == "__init__.py":
        continue
      if os.path.splitext(__lowerCAmelCase							)[1] in (".py", ".ipynb"):
        yield os.path.join(__lowerCAmelCase ,  __lowerCAmelCase							).lstrip("""./"""							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  return F'''{i * "  "}*''' if i else "\n##"


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						str:







  '''simple docstring'''


  lowercase_								= old_path.split(os.sep							)
  for i, new_part in enumerate(new_path.split(os.sep							)							):
    if (i + 1 > len(__lowerCAmelCase							) or old_parts[i] != new_part) and new_part:
      print(F'''{md_prefix(__lowerCAmelCase							)} {new_part.replace("_" ,  " "							).title()}'''							)
  return new_path


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = "."							) ->						None:







  '''simple docstring'''


  lowercase_								= """"""
  for filepath in sorted(good_file_paths(__lowerCAmelCase							)							):
    lowercase_					,    lowercase_								= os.path.split(__lowerCAmelCase							)
    if filepath != old_path:
      lowercase_								= print_path(__lowerCAmelCase ,  __lowerCAmelCase							)
    lowercase_								= (filepath.count(os.sep							) + 1) if filepath else 0
    lowercase_								= F'''{filepath}/{filename}'''.replace(""" """ ,  """%20"""							)
    lowercase_								= os.path.splitext(filename.replace("""_""" ,  """ """							).title()							)[0]
    print(F'''{md_prefix(__lowerCAmelCase							)} [{filename}]({url})'''							)


if __name__ == "__main__":
       print_directory_md(".")

