




def 							_UpperCAmelCase  (UpperCamelCase_ : Dict       ):
    '''simple docstring'''
    return [
        {
            0: [1, 2],
            1: [0, 2],
            2: [0, 1, 3, 5],
            3: [2, 4],
            4: [3],
            5: [2, 6, 8],
            6: [5, 7],
            7: [6, 8],
            8: [5, 7],
        },
        {
            0: [6],
            1: [9],
            2: [4, 5],
            3: [4],
            4: [2, 3],
            5: [2],
            6: [0, 7],
            7: [6],
            8: [],
            9: [1],
        },
        {
            0: [4],
            1: [6],
            2: [],
            3: [5, 6, 7],
            4: [0, 6],
            5: [3, 8, 9],
            6: [1, 3, 4, 7],
            7: [3, 6, 8, 9],
            8: [5, 7],
            9: [5, 7],
        },
        {
            0: [1, 3],
            1: [0, 2, 4],
            2: [1, 3, 4],
            3: [0, 2, 4],
            4: [1, 2, 3],
        },
    ][index]




def 							_UpperCAmelCase  (UpperCamelCase_ : dict[int, list[int]]       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Optional[int]										=      0
    _lowerCAmelCase    :    Tuple										=      len(UpperCamelCase_       )  # No of vertices in graph
    _lowerCAmelCase    :    Tuple										=      [0] * n
    _lowerCAmelCase    :    Tuple										=      [False] * n

    def dfs(UpperCamelCase_ : Dict      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : List[Any]       ):
        _lowerCAmelCase    :    Optional[Any]										=      True
        _lowerCAmelCase    :    Union[str, Any]										=      id_
        id_ += 1
        for to in graph[at]:
            if to == parent:
                pass
            elif not visited[to]:
                dfs(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    id_       )
                _lowerCAmelCase    :    int										=      min(low[at]      ,    low[to]       )
                if id_ <= low[to]:
                    bridges.append((at, to) if at < to else (to, at)       )
            else:
                # This edge is a back edge and cannot be a bridge
                _lowerCAmelCase    :    int										=      min(low[at]      ,    low[to]       )

    _lowerCAmelCase    :    list[tuple[int, int]]										=      []
    for i in range(UpperCamelCase_       ):
        if not visited[i]:
            dfs(UpperCamelCase_      ,    -1      ,    UpperCamelCase_      ,    id_       )
    return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()







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


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


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




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				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

