


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					lowercase_				,       lowercase_					=      analyze_text(_UpperCAmelCase	)
					lowercase_					=      list(""" """ + ascii_lowercase	)
					# what is our total sum of probabilities.
					lowercase_					=      sum(single_char_strings.values()	)

					# one length string
					lowercase_					=      0
					# for each alpha we go in our dict and if it is in it we calculate entropy
					for ch in my_alphas:
										if ch in single_char_strings:
															lowercase_					=      single_char_strings[ch]
															lowercase_					=      my_str / all_sum
															my_fir_sum += prob * math.loga(_UpperCAmelCase	)  # entropy formula.

															 # print entropy
					print(F'''{round(-1 * my_fir_sum	):.1f}'''	)

					# two len string
					lowercase_					=      sum(two_char_strings.values()	)
					lowercase_					=      0
					# for each alpha (two in size) calculate entropy.
					for cha in my_alphas:
										for cha in my_alphas:
															lowercase_					=      cha + cha
															if sequence in two_char_strings:
																				lowercase_					=      two_char_strings[sequence]
																				lowercase_					=      int(_UpperCAmelCase	) / all_sum
																				my_sec_sum += prob * math.loga(_UpperCAmelCase	)

																				# print second entropy
					print(F'''{round(-1 * my_sec_sum	):.1f}'''	)

					# print the difference between them
					print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)	):.1f}'''	)


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					lowercase_					=      Counter()  # type: ignore
					lowercase_					=      Counter()  # type: ignore
					single_char_strings[text[-1]] += 1

					# first case when we have space at start.
					two_char_strings[" " + text[0]] += 1
					for i in range(0    ,				len(_UpperCAmelCase	) - 1	):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings


def       UpperCAmelCase_					(   ):
					import doctest

					doctest.testmod()
					# text = (
					#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
					#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
					#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
					#     "beloved evident through no service elderly is. Blind there if every no so "
					#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
					#     "of message cottage windows do besides against uncivil.  Delightful "
					#     "unreserved impossible few estimating men favourable see entreaties. She "
					#     "propriety immediate was improving. He or entrance humoured likewise "
					#     "moderate. Much nor game son say feel. Fat make met can must form into "
					#     "gate. Me we offending prevailed discovery. "
					# )

					# calculate_prob(text)


if __name__ == "__main__":
							main()









"""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.''')





