text: from petersburg import Graph
  
  __author__ = 'willmcginnis'
  
  if __name__ == '__main__':
      g = Graph()
  
      # decide or research then decide (w/o switching)
      g.from_dict({
            1: {'payoff': 0, 'after': []},
            2: {'payoff': 0, 'after': [{'node_id': 1, 'cost': 10, 'weight': 2}]},
            3: {'payoff': 0, 'after': [{'node_id': 1, 'cost': 10}]},
            4: {'payoff': 0, 'after': [{'node_id': 1, 'cost': 10, 'weight': 1.5}]},
            5: {'payoff': 0, 'after': [{'node_id': 2, 'cost': 5}, {'node_id': 3, 'cost': 10}]},
            6: {'payoff': 0, 'after': [{'node_id': 2, 'cost': 5}, {'node_id': 4, 'cost': 10}]},
            7: {'payoff': 25, 'after': [{'node_id': 5, 'cost': 0}]},
            8: {'payoff': 15, 'after': [{'node_id': 5, 'cost': 0}]},
            9: {'payoff': 25, 'after': [{'node_id': 6, 'cost': 0}]},
            10: {'payoff': 15, 'after': [{'node_id': 6, 'cost': 0}]},
       })
  
      print(g.adjacency_matrix())
      #
      # outcomes = []
      # for _ in range(100000):
      #     outcomes.append(g.get_outcome())
      #
      # print('\n\nSimulated Output With Random Start')
      # print(float(sum(outcomes))/len(outcomes))
      #
      # print('\n\nSimulated Profit of Each Starting Move')
      # print(g.get_options(iters=10))