

"""simple docstring"""




A_				:      int           =			"Alexander Joslin"

import operator as op

from .stack import Stack

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

			SCREAMING_SNAKE_CASE__										=   Stack()
			SCREAMING_SNAKE_CASE__										=   Stack()

			for i in equation:
						if i.isdigit():
									# RULE 1
									operand_stack.push(int(snake_case__     )     )
						elif i in operators:
									# RULE 2
									operator_stack.push(snake_case__     )
						elif i == ")":
									# RULE 4
									SCREAMING_SNAKE_CASE__										=   operator_stack.peek()
									operator_stack.pop()
									SCREAMING_SNAKE_CASE__										=   operand_stack.peek()
									operand_stack.pop()
									SCREAMING_SNAKE_CASE__										=   operand_stack.peek()
									operand_stack.pop()

									SCREAMING_SNAKE_CASE__										=   operators[opr](snake_case__		,					snake_case__     )
									operand_stack.push(snake_case__     )

    # RULE 5
			return operand_stack.peek()


if __name__ == "__main__":
						A_				:      Tuple           =			"(5 + ((4 * 2) * (2 + 3)))"
						# answer = 45
						print(F'{equation} = {dijkstras_two_stack_algorithm(equation)}')





"""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


