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 			__UpperCamelCase     (    A			,				A			,				A			,				A			,				A				):
     UpperCamelCase__					    =   np.array([[1, item, train_mtch[i]] for i, item in enumerate(__snake_case				)]				)
     UpperCamelCase__					    =   np.array(__snake_case				)
     UpperCamelCase__					    =   np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,				__snake_case				)				)			,				x.transpose()				)			,				__snake_case				)
     return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]				)







def 			__UpperCamelCase     (    A			,				A			,				A				):
     UpperCamelCase__					    =   (1, 2, 1)
     UpperCamelCase__					    =   (1, 1, 0, 7)
     UpperCamelCase__					    =   SARIMAX(
         __snake_case			,				exog=__snake_case			,				order=__snake_case			,				seasonal_order=__snake_case				)
     UpperCamelCase__					    =   model.fit(disp=__snake_case			,				maxiter=600			,				method='''nm'''				)
     UpperCamelCase__					    =   model_fit.predict(1			,				len(__snake_case				)			,				exog=[test_match]				)
     return result[0]







def 			__UpperCamelCase     (    A			,				A			,				A				):
     UpperCamelCase__					    =   SVR(kernel='''rbf'''			,				C=1			,				gamma=0.1			,				epsilon=0.1				)
     regressor.fit(__snake_case			,				__snake_case				)
     UpperCamelCase__					    =   regressor.predict(__snake_case				)
     return y_pred[0]







def 			__UpperCamelCase     (    A				):
     train_user.sort()
     UpperCamelCase__					    =   np.percentile(__snake_case			,				25				)
     UpperCamelCase__					    =   np.percentile(__snake_case			,				75				)
     UpperCamelCase__					    =   qa - qa
     UpperCamelCase__					    =   qa - (iqr * 0.1)
     return low_lim







def 			__UpperCamelCase     (    A			,				A				):
     UpperCamelCase__					    =   0
     UpperCamelCase__					    =   0
     for i in list_vote:
          if i > actual_result:
               UpperCamelCase__					    =   not_safe + 1
          else:
               if abs(abs(__snake_case				) - abs(__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)
     __magic_name__    		=[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
     __magic_name__    		=pd.DataFrame(
         data_input, columns=['''total_user''', '''total_even''', '''days''']
     )
     __magic_name__    		=Normalizer().fit_transform(data_input_df.values)
     # split data
     __magic_name__    		=normalize_df[:, 2].tolist()
     __magic_name__    		=normalize_df[:, 0].tolist()
     __magic_name__    		=normalize_df[:, 1].tolist()

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

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

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

     # voting system with forecasting
     __magic_name__    		=[
         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
     __magic_name__    		='''''' if data_safety_checker(res_vote, tst_user) else '''not '''
     print('''Today\'s data is {not_str}safe.''')











import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase      :       str    =						[int(0.5 * n * (n + 1)) for n in range(1, 101)]





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       os.path.dirname(os.path.realpath(__snake_case							)							)
     __lowerCAmelCase             =       os.path.join(__snake_case       ,			"words.txt"							)

     __lowerCAmelCase             =       ""
     with open(__snake_case							) as f:
          __lowerCAmelCase             =       f.readline()

     __lowerCAmelCase             =       [word.strip("\""							) for word in words.strip("\r\n"							).split(","							)]
     __lowerCAmelCase             =       [
         word
         for word in [sum(ord(__snake_case							) - 64 for x in word							) for word in words]
         if word in TRIANGULAR_NUMBERS
     ]
     return len(__snake_case							)


if __name__ == "__main__":
 print(solution())






