
"""simple docstring"""




from collections import deque




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			str       =     len(_UpperCamelCase						)
  _lowercase:			List[Any]       =     deque()
  _lowercase:			List[Any]       =     [False for _ in range(_UpperCamelCase						)]
  _lowercase:			Any       =     [-1 for _ in range(_UpperCamelCase						)]
  _lowercase:			Optional[int]       =     index_of[:]

  def strong_connect(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):
    _lowercase:			Tuple       =     index  # the number when this node is seen
    _lowercase:			Union[str, Any]       =     index  # lowest rank node reachable from here
    index += 1
    stack.append(_UpperCamelCase						)
    _lowercase:			Dict       =     True

    for w in g[v]:
      if index_of[w] == -1:
        _lowercase:			List[Any]       =     strong_connect(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)
        _lowercase:			Tuple       =     (
            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
        )
      elif on_stack[w]:
        _lowercase:			List[str]       =     (
            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
        )

    if lowlink_of[v] == index_of[v]:
      _lowercase:			Tuple       =     []
      _lowercase:			Dict       =     stack.pop()
      _lowercase:			Optional[int]       =     False
      component.append(_UpperCamelCase						)
      while w != v:
        _lowercase:			Optional[Any]       =     stack.pop()
        _lowercase:			Union[str, Any]       =     False
        component.append(_UpperCamelCase						)
      components.append(_UpperCamelCase						)
    return index

  _lowercase:			List[Any]       =     []
  for v in range(_UpperCamelCase						):
    if index_of[v] == -1:
      strong_connect(_UpperCamelCase ,				0 ,				_UpperCamelCase						)

  return components




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     [[] for _ in range(_UpperCamelCase						)]
  for u, v in edges:
    g[u].append(_UpperCamelCase						)
  return g


if __name__ == "__main__":
  # Test
  A__   :					List[str]							=   7
  A__   :					int							=   [0, 0, 1, 2, 3, 3, 4, 4, 6]
  A__   :					str							=   [1, 3, 2, 0, 1, 4, 5, 6, 5]
  A__   :					int							=   [(u, v) for u, v in zip(source, target)]
  A__   :					int							=   create_graph(n_vertices, edges)

  assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


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


A__							, A__							, A__							, A__   :					Dict							=   train_test_split(X, y, test_size=0.2, random_state=0)


A__   :					Union[str, Any]							=   PolynomialFeatures(degree=4)
A__   :					List[Any]							=   poly_reg.fit_transform(X)
A__   :					Dict							=   LinearRegression()
pol_reg.fit(X_poly, y)




def  _lowerCAmelCase		(		):






  """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
