





"""simple docstring"""
from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__UpperCamelCase     :						int						       =  TypeVar('''T''')

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 return (position - 1) // 2

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 return (2 * position) + 1

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 return (2 * position) + 2







class       a							(   Generic[T]  ):



 def __init__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= []
  lowerCAmelCase    				= {}
  lowerCAmelCase    				= 0



 def __len__(     self     ):







  """simple docstring"""






  return self.elements



 def __repr__(     self     ):







  """simple docstring"""






  return str(self.heap     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self.elements == 0



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.heap.append((elem, weight)     )
  lowerCAmelCase    				= self.elements
  self.elements += 1
  self._bubble_up(_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if self.elements > 1:
   self._swap_nodes(0				,       self.elements - 1     )
  lowerCAmelCase				,lowerCAmelCase    				= self.heap.pop()
  del self.position_map[elem]
  self.elements -= 1
  if self.elements > 0:
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[0]
   self._bubble_down(_snake_case     )
  return elem



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.position_map[elem]
  lowerCAmelCase    				= (elem, weight)
  if position > 0:
   lowerCAmelCase    				= get_parent_position(_snake_case     )
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[parent_position]
   if parent_weight > weight:
    self._bubble_up(_snake_case     )
   else:
    self._bubble_down(_snake_case     )
  else:
   self._bubble_down(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.position_map[elem]
  if curr_pos == 0:
   return None
  lowerCAmelCase    				= get_parent_position(_snake_case     )
  lowerCAmelCase				,lowerCAmelCase    				= self.heap[curr_pos]
  lowerCAmelCase				,lowerCAmelCase    				= self.heap[parent_position]
  if parent_weight > weight:
   self._swap_nodes(_snake_case				,       _snake_case     )
   return self._bubble_up(_snake_case     )
  return None



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.position_map[elem]
  lowerCAmelCase				,lowerCAmelCase    				= self.heap[curr_pos]
  lowerCAmelCase    				= get_child_left_position(_snake_case     )
  lowerCAmelCase    				= get_child_right_position(_snake_case     )
  if child_left_position < self.elements and child_right_position < self.elements:
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[child_left_position]
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[child_right_position]
   if child_right_weight < child_left_weight and child_right_weight < weight:
    self._swap_nodes(_snake_case				,       _snake_case     )
    return self._bubble_down(_snake_case     )
  if child_left_position < self.elements:
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[child_left_position]
   if child_left_weight < weight:
    self._swap_nodes(_snake_case				,       _snake_case     )
    return self._bubble_down(_snake_case     )
  else:
   return None
  if child_right_position < self.elements:
   lowerCAmelCase				,lowerCAmelCase    				= self.heap[child_right_position]
   if child_right_weight < weight:
    self._swap_nodes(_snake_case				,       _snake_case     )
    return self._bubble_down(_snake_case     )
  return None



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.heap[nodea_pos][0]
  lowerCAmelCase    				= self.heap[nodea_pos][0]
  lowerCAmelCase				,lowerCAmelCase    				= (
      self.heap[nodea_pos],
      self.heap[nodea_pos],
  )
  lowerCAmelCase    				= nodea_pos
  lowerCAmelCase    				= nodea_pos







class       a							(   Generic[T]  ):



 def __init__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= {}
  lowerCAmelCase    				= 0



 def __repr__(     self     ):







  """simple docstring"""






  return str(self.connections     )



 def __len__(     self     ):







  """simple docstring"""






  return self.nodes



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if node not in self.connections:
   lowerCAmelCase    				= {}
   self.nodes += 1



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.add_node(_snake_case     )
  self.add_node(_snake_case     )
  lowerCAmelCase    				= weight
  lowerCAmelCase    				= weight


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    GraphUndirectedWeighted[T]  ,     ):
 lowerCAmelCase    				= {node: maxsize for node in graph.connections}
 lowerCAmelCase    				= {node: None for node in graph.connections}

 lowerCAmelCase    				= MinPriorityQueue()
 for node, weight in dist.items():
  priority_queue.push(_UpperCAmelCase  ,     _UpperCAmelCase			)

 if priority_queue.is_empty():
  return dist, parent

 # initialization
 lowerCAmelCase    				= priority_queue.extract_min()
 lowerCAmelCase    				= 0
 for neighbour in graph.connections[node]:
  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
   lowerCAmelCase    				= dist[node] + graph.connections[node][neighbour]
   priority_queue.update_key(_UpperCAmelCase  ,     dist[neighbour]			)
   lowerCAmelCase    				= node

    # running prim's algorithm
 while not priority_queue.is_empty():
  lowerCAmelCase    				= priority_queue.extract_min()
  for neighbour in graph.connections[node]:
   if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
    lowerCAmelCase    				= dist[node] + graph.connections[node][neighbour]
    priority_queue.update_key(_UpperCAmelCase  ,     dist[neighbour]			)
    lowerCAmelCase    				= node
 return dist, parent












"""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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase			)]			)
 lowerCAmelCase    				= np.array(_UpperCAmelCase			)
 lowerCAmelCase    				= np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()  ,     _UpperCAmelCase			)			)  ,     x.transpose()			)  ,     _UpperCAmelCase			)
 return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]			)

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

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= SVR(kernel='rbf'  ,     C=1  ,     gamma=0.1  ,     epsilon=0.1			)
 regressor.fit(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= regressor.predict(_UpperCAmelCase			)
 return y_pred[0]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list			):
 train_user.sort()
 lowerCAmelCase    				= np.percentile(_UpperCAmelCase  ,     25			)
 lowerCAmelCase    				= np.percentile(_UpperCAmelCase  ,     75			)
 lowerCAmelCase    				= qa - qa
 lowerCAmelCase    				= qa - (iqr * 0.1)
 return low_lim

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    float			):
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 0
 for i in list_vote:
  if i > actual_result:
   lowerCAmelCase    				= not_safe + 1
  else:
   if abs(abs(_UpperCAmelCase			) - abs(_UpperCAmelCase			)			) <= 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     :						Optional[Any]						       =  [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
       __UpperCamelCase     :						Any						       =  pd.DataFrame(
           data_input, columns=['''total_user''', '''total_even''', '''days''']
       )
       __UpperCamelCase     :						Dict						       =  Normalizer().fit_transform(data_input_df.values)
       # split data
       __UpperCamelCase     :						Dict						       =  normalize_df[:, 2].tolist()
       __UpperCamelCase     :						Union[str, Any]						       =  normalize_df[:, 0].tolist()
       __UpperCamelCase     :						List[str]						       =  normalize_df[:, 1].tolist()

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

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

       __UpperCamelCase     :						Optional[Any]						       =  total_date[len(total_date) - 1 :]
       __UpperCamelCase     :						str						       =  total_user[len(total_user) - 1 :]
       __UpperCamelCase     :						str						       =  total_match[len(total_match) - 1 :]

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





