




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      (							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	list					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	list     ,							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   0
      lowerCamelCase_							=   0
      for i in list_vote:
            if i > actual_result:
                  lowerCamelCase_							=   not_safe + 1
            else:
                  if abs(abs(lowercase					) - abs(lowercase					)					) <= 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)
 lowerCamelCase    :		Any			   =       [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
 lowerCamelCase    :		List[Any]			   =       pd.DataFrame(
     data_input, columns=["total_user", "total_even", "days"]
 )
 lowerCamelCase    :		List[Any]			   =       Normalizer().fit_transform(data_input_df.values)
 # split data
 lowerCamelCase    :		str			   =       normalize_df[:, 2].tolist()
 lowerCamelCase    :		List[str]			   =       normalize_df[:, 0].tolist()
 lowerCamelCase    :		Tuple			   =       normalize_df[:, 1].tolist()

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

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

 lowerCamelCase    :		Dict			   =       total_date[len(total_date) - 1 :]
 lowerCamelCase    :		Optional[Any]			   =       total_user[len(total_user) - 1 :]
 lowerCamelCase    :		Dict			   =       total_match[len(total_match) - 1 :]

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









from __future__ import annotations

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	dict     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   set(lowercase					), [start]

      while stack:
            lowerCamelCase_							=   stack.pop()
            explored.add(lowercase					)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]					):
                  if adj not in explored:
                        stack.append(lowercase					)
      return explored


lowerCamelCase    :		int			   =       {
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(depth_first_search(G, "A"))



