






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									SCREAMING_SNAKE_CASE							=			mf_knapsack(i - 1				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)
						else:
									SCREAMING_SNAKE_CASE							=			max(
									    mf_knapsack(i - 1				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)				,							mf_knapsack(i - 1				,							_UpperCAmelCase				,							_UpperCAmelCase				,							j - wt[i - 1]) + val[i - 1]				,							)
						SCREAMING_SNAKE_CASE							=			val
			return f[i][j]






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			[[0] * (w + 1) for _ in range(n + 1)]

			for i in range(1				,							n + 1):
						for w_ in range(1				,							w + 1):
									if wt[i - 1] <= w_:
												SCREAMING_SNAKE_CASE							=			max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,							dp[i - 1][w_])
									else:
												SCREAMING_SNAKE_CASE							=			dp[i - 1][w_]

			return dp[n][w_], dp






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			if not (isinstance(_UpperCAmelCase				,							(list, tuple)) and isinstance(_UpperCAmelCase				,							(list, tuple))):
						raise ValueError(
						    'Both the weights and values vectors must be either lists or tuples')

			SCREAMING_SNAKE_CASE							=			len(_UpperCAmelCase)
			if num_items != len(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE							=			(
						    'The number of weights must be the same as the number of values.\n'
						    F'''But got {num_items} weights and {len(_UpperCAmelCase)} values'''
						)
						raise ValueError(_UpperCAmelCase)
			for i in range(_UpperCAmelCase):
						if not isinstance(wt[i]				,							_UpperCAmelCase):
									SCREAMING_SNAKE_CASE							=			(
									    'All weights must be integers but got weight of '
									    F'''type {type(wt[i])} at index {i}'''
									)
									raise TypeError(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			knapsack(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			set()
			_construct_solution(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)

			return optimal_val, example_optional_set






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(_UpperCAmelCase				,							_UpperCAmelCase				,							i - 1				,							_UpperCAmelCase				,							_UpperCAmelCase)
						else:
									optimal_set.add(_UpperCAmelCase)
									_construct_solution(_UpperCAmelCase				,							_UpperCAmelCase				,							i - 1				,							j - wt[i - 1]				,							_UpperCAmelCase)


if __name__ == "__main__":
				a_		:    int   							=     [3, 2, 4, 4]
				a_		:    Union[str, Any]   							=     [4, 3, 2, 3]
				a_		:    Tuple   							=     4
				a_		:    Optional[int]   							=     6
				a_		:    Dict   							=     [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
				a_							,       a_		:    str   							=     knapsack(w, wt, val, n)
				print(optimal_solution)
				print(mf_knapsack(n, wt, val, w))  # switched the n and w

				# testing the dynamic programming problem with example
				# the optimal subset for the above example are items 3 and 4
				a_							,       a_		:    Union[str, Any]   							=     knapsack_with_example_solution(w, wt, val)
				assert optimal_solution == 8
				assert optimal_subset == {3, 4}
				print('optimal_value = ', optimal_solution)
				print('An optimal subset corresponding to the optimal value', optimal_subset)










'''simple docstring'''


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 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							return (data["data"], data["target"])
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray ):





							'''simple docstring'''


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





							'''simple docstring'''


							_a      	=							fetch_california_housing()
							_a	,				_a      	=							data_handling(UpperCamelCase )
							_a	,				_a	,				_a	,				_a      	=							train_test_split(
							    UpperCamelCase					,		UpperCamelCase					,		test_size=0.25					,		random_state=1 )
							_a      	=							xgboost(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							# Error printing
							print(f'Mean Absolute Error : {mean_absolute_error(UpperCamelCase					,		UpperCamelCase )}' )
							print(f'Mean Square Error  : {mean_squared_error(UpperCamelCase					,		UpperCamelCase )}' )


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

