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        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A							)					->    float:







      '''simple docstring'''


      UpperCAmelCase__								=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__A							)]							)
      UpperCAmelCase__								=  np.array(__A							)
      UpperCAmelCase__								=  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        lowerCAmelCase_			(					__A,  __A,  __A							)					->    float:







      '''simple docstring'''


      UpperCAmelCase__								=  (1, 2, 1)
      UpperCAmelCase__								=  (1, 1, 0, 7)
      UpperCAmelCase__								=  SARIMAX(
          __A,  exog=__A,  order=__A,  seasonal_order=__A							)
      UpperCAmelCase__								=  model.fit(disp=__A,  maxiter=600,  method="nm"							)
      UpperCAmelCase__								=  model_fit.predict(1,  len(__A							),  exog=[test_match]							)
      return result[0]




def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    float:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A							)					->    float:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A							)					->    bool:







      '''simple docstring'''


      UpperCAmelCase__								=  0
      UpperCAmelCase__								=  0
      for i in list_vote:
            if i > actual_result:
                  UpperCAmelCase__								=  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)
      UpperCamelCase__    							= [[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]]
      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.')




# Lint as: python3
import itertools
import os
import re


UpperCamelCase__    							= re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__    							= re.compile(R'([a-z\d])([A-Z])')

UpperCamelCase__    							= re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__    							= re.compile(R'(_{2,})')

UpperCamelCase__    							= R'^\w+(\.\w+)*$'

UpperCamelCase__    							= R'<>:/\|?*'




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  _uppercase_uppercase_re.sub(r"\1_\2",  __A							)
      UpperCAmelCase__								=  _lowercase_uppercase_re.sub(r"\1_\2",  __A							)
      return name.lower()




def        lowerCAmelCase_			(					__A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  _single_underscore_re.split(__A							)
      UpperCAmelCase__								=  [_multiple_underscores_re.split(__A							) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(__A							) if n != ""							)




def        lowerCAmelCase_			(					__A							)					->    List[str]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      return camelcase_to_snakecase(__A							)




def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[int]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      if not re.match(_split_re,  __A							):
            raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'."""							)
      return f"""{filename_prefix_for_name(__A							)}-{split}"""




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      if filetype_suffix:
            prefix += f""".{filetype_suffix}"""
      UpperCAmelCase__								=  os.path.join(__A,  __A							)
      return f"""{filepath}*"""







def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None,  __A=None							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      UpperCAmelCase__								=  os.path.join(__A,  __A							)

      if shard_lengths:
            UpperCAmelCase__								=  len(__A							)
            UpperCAmelCase__								=  [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(__A							)]
            if filetype_suffix:
                  UpperCAmelCase__								=  [filename + f""".{filetype_suffix}""" for filename in filenames]
            return filenames
      else:
            UpperCAmelCase__								=  prefix
            if filetype_suffix:
                  filename += f""".{filetype_suffix}"""
            return [filename]



