



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





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





def   __snake_case			(    _UpperCAmelCase       ):
    train_user.sort()
    __a		 =       np.percentile(_lowerCamelCase     ,				25       )
    __a		 =       np.percentile(_lowerCamelCase     ,				75       )
    __a		 =       qa - qa
    __a		 =       qa - (iqr * 0.1)
    return low_lim





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

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

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

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

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










from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class 					_A (			__UpperCAmelCase					):



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
           return Dataset.from_dict(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(dset.column_names  ,	['''col_1''', '''col_2'''])
           for i, r in enumerate(__SCREAMING_SNAKE_CASE):
               self.assertDictEqual(__SCREAMING_SNAKE_CASE  ,	example_records[i])



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           __a		 =       Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
           self.assertEqual(dset.info  ,	dset_from_dict.info)



       def 	_lowerCamelCase    (	self							:  int):  # checks what happens with missing columns


           '''simple docstring'''

           __a		 =       [{'''col_1''': 1}, {'''col_2''': '''x'''}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertDictEqual(dset[0]  ,	{'''col_1''': 1})
           self.assertDictEqual(dset[1]  ,	{'''col_1''': None})  # NB: first record is used for columns



       def 	_lowerCamelCase    (	self							:  Optional[Any]):  # checks if the type can be inferred from the second record


           '''simple docstring'''

           __a		 =       [{'''col_1''': []}, {'''col_1''': [1, 2]}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertEqual(dset.info.features['''col_1''']  ,	Sequence(Value('''int64''')))





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       Dataset.from_list([])
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	0)
           self.assertListEqual(dset.column_names  ,	[])





