


"""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  ):
				SCREAMING_SNAKE_CASE__     							=				np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCamelCase_  )]  )
				SCREAMING_SNAKE_CASE__     							=				np.array(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				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  ):
				SCREAMING_SNAKE_CASE__     							=				(1, 2, 1)
				SCREAMING_SNAKE_CASE__     							=				(1, 1, 0, 7)
				SCREAMING_SNAKE_CASE__     							=				SARIMAX(
				    lowerCamelCase_						,					exog=lowerCamelCase_						,					order=lowerCamelCase_						,					seasonal_order=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				model.fit(disp=lowerCamelCase_						,					maxiter=600						,					method="nm"  )
				SCREAMING_SNAKE_CASE__     							=				model_fit.predict(1						,					len(lowerCamelCase_  )						,					exog=[test_match]  )
				return result[0]


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      list						,					lowerCamelCase_  :      list  ):
				SCREAMING_SNAKE_CASE__     							=				SVR(kernel="rbf"						,					C=1						,					gamma=0.1						,					epsilon=0.1  )
				regressor.fit(lowerCamelCase_						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				regressor.predict(lowerCamelCase_  )
				return y_pred[0]


def __lowercase     (      lowerCamelCase_  :      list  ):
				train_user.sort()
				SCREAMING_SNAKE_CASE__     							=				np.percentile(lowerCamelCase_						,					25  )
				SCREAMING_SNAKE_CASE__     							=				np.percentile(lowerCamelCase_						,					75  )
				SCREAMING_SNAKE_CASE__     							=				qa - qa
				SCREAMING_SNAKE_CASE__     							=				qa - (iqr * 0.1)
				return low_lim


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      float  ):
				SCREAMING_SNAKE_CASE__     							=				0
				SCREAMING_SNAKE_CASE__     							=				0
				for i in list_vote:
								if i > actual_result:
												SCREAMING_SNAKE_CASE__     							=				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)
	_lowerCamelCase     =  [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
	_lowerCamelCase     =  pd.DataFrame(
	    data_input, columns=['total_user', 'total_even', 'days']
	)
	_lowerCamelCase     =  Normalizer().fit_transform(data_input_df.values)
	# split data
	_lowerCamelCase     =  normalize_df[:, 2].tolist()
	_lowerCamelCase     =  normalize_df[:, 0].tolist()
	_lowerCamelCase     =  normalize_df[:, 1].tolist()

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

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

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

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




"""simple docstring"""






import heapq


def __lowercase     (      lowerCamelCase_  :      dict  ):
				SCREAMING_SNAKE_CASE__     							=				[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase_						,					[-1 * len(lowerCamelCase_  ), (key, value)]  )

				# chosen_vertices = set of chosen vertices
				SCREAMING_SNAKE_CASE__     							=				set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								SCREAMING_SNAKE_CASE__     							=				heapq.heappop(lowerCamelCase_  )[1][0]
								chosen_vertices.add(lowerCamelCase_  )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																SCREAMING_SNAKE_CASE__     							=				elem[1][1].index(lowerCamelCase_  )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase_  )
				return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCamelCase     =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
