
"""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__						(					lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ) ->      float:






    """simple docstring"""




    UpperCAmelCase									=					np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCAmelCase )] )
    UpperCAmelCase									=					np.array(lowerCAmelCase )
    UpperCAmelCase									=					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     lowercase__						(					lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ) ->      float:






    """simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ) ->      float:






    """simple docstring"""




    UpperCAmelCase									=					SVR(kernel='rbf' ,				C=1 ,				gamma=0.1 ,				epsilon=0.1 )
    regressor.fit(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					regressor.predict(lowerCAmelCase )
    return y_pred[0]




def     lowercase__						(					lowerCAmelCase				:					list ) ->      float:






    """simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					list ,				lowerCAmelCase				:					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(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)
   SCREAMING_SNAKE_CASE_    					=						[[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
   SCREAMING_SNAKE_CASE_    					=						pd.DataFrame(
       data_input, columns=['''total_user''', '''total_even''', '''days''']
   )
   SCREAMING_SNAKE_CASE_    					=						Normalizer().fit_transform(data_input_df.values)
   # split data
   SCREAMING_SNAKE_CASE_    					=						normalize_df[:, 2].tolist()
   SCREAMING_SNAKE_CASE_    					=						normalize_df[:, 0].tolist()
   SCREAMING_SNAKE_CASE_    					=						normalize_df[:, 1].tolist()

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

   # for linear regression & sarimax
   SCREAMING_SNAKE_CASE_    					=						total_date[: len(total_date) - 1]
   SCREAMING_SNAKE_CASE_    					=						total_user[: len(total_user) - 1]
   SCREAMING_SNAKE_CASE_    					=						total_match[: len(total_match) - 1]

   SCREAMING_SNAKE_CASE_    					=						total_date[len(total_date) - 1 :]
   SCREAMING_SNAKE_CASE_    					=						total_user[len(total_user) - 1 :]
   SCREAMING_SNAKE_CASE_    					=						total_match[len(total_match) - 1 :]

   # voting system with forecasting
   SCREAMING_SNAKE_CASE_    					=						[
       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
   SCREAMING_SNAKE_CASE_    					=						'''''' 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     lowercase__						(					lowerCAmelCase				:					str = "." ) ->      Iterator[str]:






    """simple docstring"""




    for dir_path, dir_names, filenames in os.walk(lowerCAmelCase ):
        UpperCAmelCase									=					[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     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      Any:






    """simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					str ) ->      str:






    """simple docstring"""




    UpperCAmelCase									=					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     lowercase__						(					lowerCAmelCase				:					str = "." ) ->      None:






    """simple docstring"""




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


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



