






from __future__ import annotations






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			[]
			SCREAMING_SNAKE_CASE							=			[]
			SCREAMING_SNAKE_CASE							=			0
			SCREAMING_SNAKE_CASE							=			sum(_UpperCAmelCase)
			create_state_space_tree(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)
			return result






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							):
			if sum(_UpperCAmelCase) > max_sum or (remaining_nums_sum + sum(_UpperCAmelCase)) < max_sum:
						return
			if sum(_UpperCAmelCase) == max_sum:
						result.append(_UpperCAmelCase)
						return
			for index in range(_UpperCAmelCase				,							len(_UpperCAmelCase)):
						create_state_space_tree(
						    _UpperCAmelCase				,							_UpperCAmelCase				,							index + 1				,							[*path, nums[index]]				,							_UpperCAmelCase				,							remaining_nums_sum - nums[index]				,							)
a_		:    Tuple   							=     [3, 34, 4, 12, 5, 2]
a_		:    str   							=     9
a_		:    Dict   							=     generate_sum_of_subsets_soln(nums, max_sum)







print(*result)






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A		: Union[str, Any]								=     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
A		: Union[str, Any]								=     dataset.iloc[:, 1:2].values
A		: Dict								=     dataset.iloc[:, 2].values


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


A		: Union[str, Any]								=     PolynomialFeatures(degree=4)
A		: str								=     poly_reg.fit_transform(X)
A		: Union[str, Any]								=     LinearRegression()
pol_reg.fit(X_poly, y)




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_SNAKE_CASE_       )       )		,   color="""blue"""       )
						plt.title("""Truth or Bluff (Linear Regression)"""       )
						plt.xlabel("""Position level"""       )
						plt.ylabel("""Salary"""       )
						plt.show()


if __name__ == "__main__":
		viz_polymonial()

		# Predicting a new result with Polymonial Regression
		pol_reg.predict(poly_reg.fit_transform([[5.5]]))
		# output should be 132148.43750003