





from __future__ import annotations






def lowerCamelCase__       (				snake_case_      :    List[Any]			, snake_case_      :    Dict			) ->							Union[str, Any]:
   print(f"""Vertex\tShortest Distance from vertex {src}"""			)
   for i, d in enumerate(snake_case__			):
      print(f"""{i}\t\t{d}"""			)






def lowerCamelCase__       (				snake_case_      :    Optional[Any]			, snake_case_      :    Dict			, snake_case_      :    List[Any]			) ->							Tuple:


   for j in range(snake_case__			):
      __snake_case			,      __snake_case			,      __snake_case							=  (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
      if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
         return True
   return False






def lowerCamelCase__       (				snake_case_      :    Dict			, snake_case_      :    Optional[Any]			, snake_case_      :    Tuple			, snake_case_      :    str			) ->							str:
   __snake_case							=  [float('''inf'''			)] * vertex_count
   __snake_case							=  0.0

   for _ in range(vertex_count - 1			):
      for j in range(snake_case__			):
         __snake_case			,      __snake_case			,      __snake_case							=  (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

         if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
            __snake_case							=  distance[u] + w

   __snake_case							=  check_negative_cycle(snake_case__			, snake_case__			, snake_case__			)
   if negative_cycle_exists:
      raise Exception('''Negative cycle found'''			)

   return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    snake_case_           =     int(input('Enter number of vertices: ').strip())
    snake_case_           =     int(input('Enter number of edges: ').strip())

    snake_case_           =     [{} for _ in range(E)]

    for i in range(E):
        print('Edge ', i + 1)
        snake_case_           =     (
            int(x)
            for x in input('Enter source, destination, weight: ').strip().split(' ')
        )
        snake_case_           =     {"src": src, "dst": dest, "weight": weight}

    snake_case_           =     int(input('\nEnter shortest path source:').strip())
    snake_case_           =     bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)




"""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


