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__  :				Any   ,			UpperCAmelCase__  :				Optional[Any]   ,			UpperCAmelCase__  :				List[Any]   ,			UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				Tuple	):
					SCREAMING_SNAKE_CASE           =							np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase__	)]	)
					SCREAMING_SNAKE_CASE           =							np.array(UpperCAmelCase__	)
					SCREAMING_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[str]   ,			UpperCAmelCase__  :				List[str]   ,			UpperCAmelCase__  :				Any	):
					SCREAMING_SNAKE_CASE           =							(1, 2, 1)
					SCREAMING_SNAKE_CASE           =							(1, 1, 0, 7)
					SCREAMING_SNAKE_CASE           =							SARIMAX(
					    UpperCAmelCase__   ,			exog=UpperCAmelCase__   ,			order=UpperCAmelCase__   ,			seasonal_order=UpperCAmelCase__	)
					SCREAMING_SNAKE_CASE           =							model.fit(disp=UpperCAmelCase__   ,			maxiter=6_0_0   ,			method="nm"	)
					SCREAMING_SNAKE_CASE           =							model_fit.predict(1   ,			len(UpperCAmelCase__	)   ,			exog=[test_match]	)
					return result[0]





def 						__lowerCamelCase				(UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				Dict   ,			UpperCAmelCase__  :				Any	):
					SCREAMING_SNAKE_CASE           =							SVR(kernel="rbf"   ,			C=1   ,			gamma=0.1   ,			epsilon=0.1	)
					regressor.fit(UpperCAmelCase__   ,			UpperCAmelCase__	)
					SCREAMING_SNAKE_CASE           =							regressor.predict(UpperCAmelCase__	)
					return y_pred[0]





def 						__lowerCamelCase				(UpperCAmelCase__  :				Tuple	):
					train_user.sort()
					SCREAMING_SNAKE_CASE           =							np.percentile(UpperCAmelCase__   ,			2_5	)
					SCREAMING_SNAKE_CASE           =							np.percentile(UpperCAmelCase__   ,			7_5	)
					SCREAMING_SNAKE_CASE           =							qa - qa
					SCREAMING_SNAKE_CASE           =							qa - (iqr * 0.1)
					return low_lim





def 						__lowerCamelCase				(UpperCAmelCase__  :				str   ,			UpperCAmelCase__  :				Dict	):
					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(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)
							_lowerCamelCase       : Tuple      					=		[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
							_lowerCamelCase       : str      					=		pd.DataFrame(
							    data_input, columns=['''total_user''', '''total_even''', '''days''']
							)
							_lowerCamelCase       : Tuple      					=		Normalizer().fit_transform(data_input_df.values)
							# split data
							_lowerCamelCase       : Dict      					=		normalize_df[:, 2].tolist()
							_lowerCamelCase       : List[str]      					=		normalize_df[:, 0].tolist()
							_lowerCamelCase       : List[Any]      					=		normalize_df[:, 1].tolist()

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

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

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

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










from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		__A   :
						UpperCamelCase      =       42
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""Translation"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def __call__(		self :Union[str, Any]			):
										'''simple docstring'''

										return pa.struct({lang: pa.string() for lang in sorted(self.languages			)}			)


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Value

										return {k: Value("""string"""			) for k in sorted(self.languages			)}




@dataclass
class 		__A   :
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""TranslationVariableLanguages"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=sorted(set(self.languages			)			) if self.languages else None
										__magic_name__						:						Optional[int]										=len(self.languages			) if self.languages else None

						def __call__(		self :List[str]			):
										'''simple docstring'''

										return pa.struct({"""language""": pa.list_(pa.string()			), """translation""": pa.list_(pa.string()			)}			)

						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=set(self.languages			)
										if self.languages and set(__snake_case			) - lang_set:
														raise ValueError(
														    f"Some languages in example ({', '.join(sorted(set(__snake_case			) - lang_set			)			)}) are not in valid set ({', '.join(__snake_case			)})."			)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										__magic_name__						:						Any										=[]
										for lang, text in translation_dict.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		translation_tuples.append((lang, text)			)
														else:
																		translation_tuples.extend([(lang, el) for el in text]			)

        # Ensure translations are in ascending order by language code.
										__magic_name__					,			__magic_name__						:						List[str]										=zip(*sorted(__snake_case			)			)

										return {"language": languages, "translation": translations}


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Sequence, Value

										return {
										    "language": Sequence(Value("""string"""			)			),
										    "translation": Sequence(Value("""string"""			)			),
										}




