

"""simple docstring"""




class  lowerCamelCase    :

			def __init__(       self					:    List[str]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   {}

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     None:
						print(self.vertex							)
						for i in self.vertex:
									print(__UpperCAmelCase     ,					""" -> """     ,					""" -> """.join([str(__UpperCAmelCase							) for j in self.vertex[i]]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int							) ->     None:
						# check if vertex is already present,
						if from_vertex in self.vertex:
									self.vertex[from_vertex].append(__UpperCAmelCase							)
						else:
									# else make a new vertex
									SCREAMING_SNAKE_CASE__										=   [to_vertex]

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     None:
						# visited array for storing already visited nodes
						SCREAMING_SNAKE_CASE__										=   [False] * len(self.vertex							)

						# call the recursive helper function
						for i in range(len(self.vertex							)							):
									if not visited[i]:
												self.dfs_recursive(__UpperCAmelCase     ,					__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    list							) ->     None:
						# mark start vertex as visited
						SCREAMING_SNAKE_CASE__										=   True

						print(__UpperCAmelCase     ,					end=""" """							)

						# Recur for all the vertices that are adjacent to this node
						for i in self.vertex:
									if not visited[i]:
												self.dfs_recursive(__UpperCAmelCase     ,					__UpperCAmelCase							)


if __name__ == "__main__":
						A_				:      List[Any]           =			Graph()
						g.add_edge(0, 1)
						g.add_edge(0, 2)
						g.add_edge(1, 2)
						g.add_edge(2, 0)
						g.add_edge(2, 3)
						g.add_edge(3, 3)

						g.print_graph()
						print("DFS:")
						g.dfs()

						# OUTPUT:
						# 0  ->  1 -> 2
						# 1  ->  2
						# 2  ->  0 -> 3
						# 3  ->  3
						# DFS:
						#  0 1 2 3





"""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


