

"""simple docstring"""




from __future__ import annotations

A_				:      List[str]           =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A_				:      str           =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   []
			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )

			for i in range(snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   -1
						for j in range(i + 1		,					snake_case__     ):
									if arr[i] < arr[j]:
												SCREAMING_SNAKE_CASE__										=   arr[j]
												break
						result.append(snake_case__     )
			return result

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   []
			for i, outer in enumerate(snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												SCREAMING_SNAKE_CASE__										=   inner
												break
						result.append(snake_case__     )
			return result

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   []
			SCREAMING_SNAKE_CASE__										=   [-1] * arr_size

			for index in reversed(range(snake_case__     )     ):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									SCREAMING_SNAKE_CASE__										=   stack[-1]
						stack.append(arr[index]     )
			return result


if __name__ == "__main__":
						from doctest import testmod
						from timeit import timeit

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						A_				:      Union[str, Any]           =			(
						    "from __main__ import arr, next_greatest_element_slow, "
						    "next_greatest_element_fast, next_greatest_element"
						)
						print(
						    "next_greatest_element_slow():",
						    timeit("next_greatest_element_slow(arr)", setup=setup),
						)
						print(
						    "next_greatest_element_fast():",
						    timeit("next_greatest_element_fast(arr)", setup=setup),
						)
						print(
						    "     next_greatest_element():",
						    timeit("next_greatest_element(arr)", setup=setup),
						)





"""simple docstring"""




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_				:      int           =			pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
A_				:      int           =			dataset.iloc[:, 1:2].values
A_				:      Optional[Any]           =			dataset.iloc[:, 2].values


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


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

def 						A							(  ):




			'''simple docstring'''


			plt.scatter(snake_case__		,					snake_case__		,					color="""red"""     )
			plt.plot(snake_case__		,					pol_reg.predict(poly_reg.fit_transform(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


