


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					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->float:
   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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->float:
   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=6_0_0	,    method="""nm""" )
   UpperCAmelCase      					=  model_fit.predict(1	,    len(lowerCAmelCase_ )	,    exog=[test_match] )
   return result[0]
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->float:
   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 	_UpperCamelCase					(    lowerCAmelCase_ )    ->float:
   train_user.sort()
   UpperCAmelCase      					=  np.percentile(lowerCAmelCase_	,    2_5 )
   UpperCAmelCase      					=  np.percentile(lowerCAmelCase_	,    7_5 )
   UpperCAmelCase      					=  qa - qa
   UpperCAmelCase      					=  qa - (iqr * 0.1)
   return low_lim
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   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)
 __a	 =      [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
 __a	 =      pd.DataFrame(
     data_input, columns=["""total_user""", """total_even""", """days"""]
 )
 __a	 =      Normalizer().fit_transform(data_input_df.values)
 # split data
 __a	 =      normalize_df[:, 2].tolist()
 __a	 =      normalize_df[:, 0].tolist()
 __a	 =      normalize_df[:, 1].tolist()

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

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

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

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







class 	__lowercase					:





      def __init__(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  {}





      def 							_lowercase					(		self    :       str							)     -> None:






         """simple docstring"""







         print(self.vertex							)
         for i in self.vertex:
            print(__lowerCamelCase       ,							""" -> """       ,							""" -> """.join([str(__lowerCamelCase							) for j in self.vertex[i]]							)							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       int							)     -> None:






         """simple docstring"""







         if from_vertex in self.vertex:
            self.vertex[from_vertex].append(__lowerCamelCase							)
         else:
            # else make a new vertex
            UpperCAmelCase      					=  [to_vertex]





      def 							_lowercase					(		self    :       str							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  [False] * len(self.vertex							)

         # call the recursive helper function
         for i in range(len(self.vertex							)							):
            if not visited[i]:
               self.dfs_recursive(__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       list							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  True

         print(__lowerCamelCase       ,							end=""" """							)

         # Recur for all the vertices that are adjacent to this node
         for i in self.vertex:
            if not visited[i]:
               self.dfs_recursive(__lowerCamelCase       ,							__lowerCamelCase							)


if __name__ == "__main__":
 __a	 =      Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 print("""DFS:""")
 g.dfs()

 # OUTPUT:
 # 0  ->  1 -> 2
 # 1  ->  2
 # 2  ->  0 -> 3
 # 3  ->  3
 # DFS:
 #  0 1 2 3



