





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__:      Optional[int]									=		pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
a__:      List[Any]									=		dataset.iloc[:, 1:2].values
a__:      Union[str, Any]									=		dataset.iloc[:, 2].values


a__					,			a__					,			a__					,			a__:      List[Any]									=		train_test_split(X, y, test_size=0.2, random_state=0)


a__:      Dict									=		PolynomialFeatures(degree=4)
a__:      str									=		poly_reg.fit_transform(X)
a__:      Tuple									=		LinearRegression()
pol_reg.fit(X_poly, y)


def 	UpperCamelCase__(       )->List[str]:
       plt.scatter(UpperCamelCase__ ,     UpperCamelCase__ ,     color='''red'''  )
       plt.plot(UpperCamelCase__ ,     pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__  )  ) ,     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












a__:      Dict									=		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

a__:      str									=		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[]

       for neighbour in graph[vert]:
              if not visited[neighbour]:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       order.append(UpperCamelCase__  )

       return order


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[vert]

       for neighbour in reversed_graph[vert]:
              if not visited[neighbour]:
                     component += find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       return component


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]]  )->list[list[int]]:
       A__											=						len(UpperCamelCase__  ) * [False]
       A__											=						{vert: [] for vert in range(len(UpperCamelCase__  )  )}

       for vert, neighbours in graph.items():
              for neighbour in neighbours:
                     reversed_graph[neighbour].append(UpperCamelCase__  )

       A__											=						[]
       for i, was_visited in enumerate(UpperCamelCase__  ):
              if not was_visited:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       A__											=						[]
       A__											=						len(UpperCamelCase__  ) * [False]

       for i in range(len(UpperCamelCase__  )  ):
              A__											=						order[len(UpperCamelCase__  ) - i - 1]
              if not visited[vert]:
                     A__											=						find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
                     components_list.append(UpperCamelCase__  )

       return components_list





