


'''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 __snake_case   ( UpperCAmelCase_		:		int   ,							UpperCAmelCase_		:		int   ,							UpperCAmelCase_		:		str   ,							UpperCAmelCase_		:		List[Any]   ,							UpperCAmelCase_		:		str						):
 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 __snake_case   ( UpperCAmelCase_		:		Any   ,							UpperCAmelCase_		:		List[str]   ,							UpperCAmelCase_		:		str						):
 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 __snake_case   ( UpperCAmelCase_		:		int   ,							UpperCAmelCase_		:		Union[str, Any]   ,							UpperCAmelCase_		:		Union[str, Any]						):
 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 __snake_case   ( UpperCAmelCase_		:		str						):
 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 __snake_case   ( UpperCAmelCase_		:		Union[str, Any]   ,							UpperCAmelCase_		:		Any						):
 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)
     a_    :							Union[str, Any]         =					[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
     a_    :							Optional[int]         =					pd.DataFrame(
         data_input, columns=["""total_user""", """total_even""", """days"""]
     )
     a_    :							Any         =					Normalizer().fit_transform(data_input_df.values)
     # split data
     a_    :							List[Any]         =					normalize_df[:, 2].tolist()
     a_    :							List[str]         =					normalize_df[:, 0].tolist()
     a_    :							Tuple         =					normalize_df[:, 1].tolist()

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

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

     a_    :							Dict         =					total_date[len(total_date) - 1 :]
     a_    :							Tuple         =					total_user[len(total_user) - 1 :]
     a_    :							Union[str, Any]         =					total_match[len(total_match) - 1 :]

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










from __future__ import annotations

import requests

UpperCamelCase_												=	set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)






def 				_UpperCAmelCase			(    A				,							A = 1				,							A = "new"				,							A = None     ):




   '''simple docstring'''

   UpperCAmelCase__						=wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(A     ) - valid_terms     )     ):
      UpperCAmelCase__						=F"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(A     )
   UpperCAmelCase__						=requests.get(
       F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""				,							headers={"User-agent": "A random string"}				,							)
   if response.status_code == 429:
      raise requests.HTTPError

   UpperCAmelCase__						=response.json()
   if not wanted_data:
      return {id_: data["data"]["children"][id_] for id_ in range(A     )}

   UpperCAmelCase__						={}
   for id_ in range(A     ):
      UpperCAmelCase__						={
          item: data["data"]["children"][id_]["data"][item] for item in wanted_data
      }
   return data_dict


if __name__ == "__main__":
    # If you get Error 429, that means you are rate limited.Try after some time
    print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))



