





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 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list						)			-> float:



							'''simple docstring'''




							_snake_case									=				np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCamelCase__						)]						)
							_snake_case									=				np.array(UpperCamelCase__						)
							_snake_case									=				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 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list						)			-> float:



							'''simple docstring'''




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





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list       ,    UpperCamelCase__			:       list						)			-> float:



							'''simple docstring'''




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





def 						lowerCamelCase__     (     UpperCamelCase__			:       list						)			-> float:



							'''simple docstring'''




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





def 						lowerCamelCase__     (     UpperCamelCase__			:       list       ,    UpperCamelCase__			:       float						)			-> bool:



							'''simple docstring'''




							_snake_case									=				0
							_snake_case									=				0
							for i in list_vote:
														if i > actual_result:
																					_snake_case									=				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_      							=     [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					UpperCAmelCase_      							=     pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					UpperCAmelCase_      							=     Normalizer().fit_transform(data_input_df.values)
					# split data
					UpperCAmelCase_      							=     normalize_df[:, 2].tolist()
					UpperCAmelCase_      							=     normalize_df[:, 0].tolist()
					UpperCAmelCase_      							=     normalize_df[:, 1].tolist()

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

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

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

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










def 						lowerCamelCase__     (     UpperCamelCase__			:       list[int]       ,    UpperCamelCase__			:       int						)			-> bool:



					'''simple docstring'''




					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

					# for each arr value, a sum of zero(0) can be formed by not taking any element
					# hence True/1
					for i in range(arr_len + 1						):
										_snake_case									=				True

					# sum is not zero and set is empty then false
					for i in range(1       ,    required_sum + 1						):
										_snake_case									=				False

					for i in range(1       ,    arr_len + 1						):
										for j in range(1       ,    required_sum + 1						):
															if arr[i - 1] > j:
																				_snake_case									=				subset[i - 1][j]
															if arr[i - 1] <= j:
																				_snake_case									=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

					return subset[arr_len][required_sum]


if __name__ == "__main__":
					import doctest

					doctest.testmod()



