
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	return (data["data"], data["target"])






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       XGBRegressor(verbosity=0					,				random_state=42				)
	xgb.fit(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	# Predict target for test data
	lowercase	    =       xgb.predict(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       predictions.reshape(len(__SCREAMING_SNAKE_CASE				)					,				1				)
	return predictions






def 				UpperCAmelCase_						(				):
	lowercase	    =       fetch_california_housing()
	lowercase    ,   lowercase	    =       data_handling(__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       train_test_split(
	    __SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				test_size=0.25					,				random_state=1				)
	lowercase	    =       xgboost(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	# Error printing
	print(F'''Mean Absolute Error : {mean_absolute_error(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)}'''				)
	print(F'''Mean Square Error  : {mean_squared_error(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)}'''				)


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()






UpperCAmelCase_		 =					2_5_6
# Modulus to hash a string
UpperCAmelCase_		 =					1_0_0_0_0_0_3

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     bool:
   _A      =       len(_snake_case				)
   _A      =       len(_snake_case				)
   if p_len > t_len:
      return False

   _A      =       0
   _A      =       0
   _A      =       1

   # Calculating the hash of pattern and substring of text
   for i in range(_snake_case				):
      _A      =       (ord(pattern[i]				) + p_hash * alphabet_size) % modulus
      _A      =       (ord(text[i]				) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      _A      =       (modulus_power * alphabet_size) % modulus

   for i in range(0	,   t_len - p_len + 1				):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      _A      =       (
          (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
          + ord(text[i + p_len]				)
      ) % modulus
   return False

def 	SCREAMING_SNAKE_CASE_							(	)  ->     None:
   _A      =       '''abc1abc12'''
   _A      =       '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   _A      =       '''alskfjaldsk23adsfabcabc'''
   assert rabin_karp(_snake_case	,   _snake_case				) and not rabin_karp(_snake_case	,   _snake_case				)

   # Test 2)
   _A      =       '''ABABX'''
   _A      =       '''ABABZABABYABABX'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 3)
   _A      =       '''AAAB'''
   _A      =       '''ABAAAAAB'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 4)
   _A      =       '''abcdabcy'''
   _A      =       '''abcxabcdabxabcdabcdabcy'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 5)
   _A      =       '''Lü'''
   _A      =       '''Lüsai'''
   assert rabin_karp(_snake_case	,   _snake_case				)
   _A      =       '''Lue'''
   assert not rabin_karp(_snake_case	,   _snake_case				)
   print('''Success.'''				)


if __name__ == "__main__":
   test_rabin_karp()



