





'''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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCAmelCase_							)]							)
				lowercase         = np.array(lowerCAmelCase_							)
				lowercase         = 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_							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = 0
				lowercase         = 0
				for i in list_vote:
								if i > actual_result:
												lowercase         = 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 : Dict											=			[[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
			__lowerCamelCase : Dict											=			pd.DataFrame(
			    data_input, columns=["total_user", "total_even", "days"]
			)
			__lowerCamelCase : Tuple											=			Normalizer().fit_transform(data_input_df.values)
			# split data
			__lowerCamelCase : Optional[Any]											=			normalize_df[:, 2].tolist()
			__lowerCamelCase : Union[str, Any]											=			normalize_df[:, 0].tolist()
			__lowerCamelCase : List[str]											=			normalize_df[:, 1].tolist()

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

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

			__lowerCamelCase : Optional[int]											=			total_date[len(total_date) - 1 :]
			__lowerCamelCase : Optional[int]											=			total_user[len(total_user) - 1 :]
			__lowerCamelCase : Union[str, Any]											=			total_match[len(total_match) - 1 :]

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











'''simple docstring'''

# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__lowerCamelCase : Any											=			open  # noqa: we just need to have a builtin inside this module to test it properly




