





"""simple docstring"""
import argparse
from collections import defaultdict

import yaml


__UpperCamelCase     :						Union[str, Any]						       =  '''docs/source/en/_toctree.yml'''

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 lowerCAmelCase    				= defaultdict(_UpperCAmelCase			)
 for doc in model_doc:
  counts[doc["local"]] += 1
 lowerCAmelCase    				= [key for key, value in counts.items() if value > 1]

 lowerCAmelCase    				= []
 for duplicate_key in duplicates:
  lowerCAmelCase    				= list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}			)
  if len(_UpperCAmelCase			) > 1:
   raise ValueError(
       F'{duplicate_key} is present several times in the documentation table of content at '
       '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
       'others.'			)
  # Only add this once
  new_doc.append({'local': duplicate_key, 'title': titles[0]}			)

 # Add none duplicate-keys
 new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]			)

 # Sort
 return sorted(_UpperCAmelCase  ,     key=lambda _UpperCAmelCase			: s["title"].lower()			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any=False			):
 with open(_UpperCAmelCase  ,     encoding='utf-8'			) as f:
  lowerCAmelCase    				= yaml.safe_load(f.read()			)

 # Get to the API doc
 lowerCAmelCase    				= 0
 while content[api_idx]["title"] != "API":
  api_idx += 1
 lowerCAmelCase    				= content[api_idx]['sections']

 # Then to the model doc
 lowerCAmelCase    				= 0
 while api_doc[model_idx]["title"] != "Models":
  model_idx += 1

 lowerCAmelCase    				= api_doc[model_idx]['sections']

 lowerCAmelCase    				= [(idx, section) for idx, section in enumerate(_UpperCAmelCase			) if 'sections' in section]
 lowerCAmelCase    				= False
 for idx, modality_doc in modalities_docs:
  lowerCAmelCase    				= modality_doc['sections']
  lowerCAmelCase    				= clean_model_doc_toc(_UpperCAmelCase			)

  if old_modality_doc != new_modality_doc:
   lowerCAmelCase    				= True
   if overwrite:
    lowerCAmelCase    				= new_modality_doc

 if diff:
  if overwrite:
   lowerCAmelCase    				= model_doc
   lowerCAmelCase    				= api_doc
   with open(_UpperCAmelCase  ,     'w'  ,     encoding='utf-8'			) as f:
    f.write(yaml.dump(_UpperCAmelCase  ,     allow_unicode=_UpperCAmelCase			)			)
  else:
   raise ValueError(
       'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'			)


if __name__ == "__main__":
       __UpperCamelCase     :						Optional[Any]						       =  argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       __UpperCamelCase     :						int						       =  parser.parse_args()

       check_model_doc(args.fix_and_overwrite)












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

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

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list			):
 train_user.sort()
 lowerCAmelCase    				= np.percentile(_UpperCAmelCase  ,     25			)
 lowerCAmelCase    				= np.percentile(_UpperCAmelCase  ,     75			)
 lowerCAmelCase    				= qa - qa
 lowerCAmelCase    				= qa - (iqr * 0.1)
 return low_lim

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

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

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

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

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





