我正在编写一个dijkstraa实现，但是遇到了附加问题（我想我现在一点都不知道）。当我尝试运行下面的代码并选择一个位置来运行dijkstra时，对于本示例，最后一个索引就是我作为返回值得到的：
[3.6, 10.8, 7.3999999999999995, 7.2, 5.5, 7.1, 12.4, 9.9, 10.8, 6.0, 6.2, 6.4, 10.8, 8.3, 7.4, 6.800000000000001, 6.9, 5.2, 6.800000000000001, 9.8, 4.1, 4.7, 3.0, 6.6000000000000005, 1.3, 8.3, 0]
因此，以某种方式，python在添加最大精度为3的浮点数时，会返回诸如'6.800000000000001'之类的数字，有人知道这是什么问题吗？
这是我的dijkstra以及我的数据集的一个小例子：
import sys
class DistanceTable:
def __init__(self, size, labels):
self.table = [[0 for column in range(size)]
for row in range(size)]
self.labels = labels
self.size = size
def get_minimum_distance(self, distances, unvisited):
prev_min = sys.maxsize
min_distance_idx = 0
for i in range(self.size):
if distances[i] < prev_min and i in unvisited:
prev_min = distances[i]
min_distance_idx = i
return min_distance_idx
def find_shortest(self, start_val):
distances = [sys.maxsize] * self.size
distances[start_val] = 0
unvisited = []
for i in range(self.size):
unvisited.append(i)
while len(unvisited) > 0:
min_distance_idx = self.get_minimum_distance(distances, unvisited)
unvisited.remove(min_distance_idx)
for i in range(self.size):
if i in unvisited and (distances[i] > distances[min_distance_idx] + self.table[min_distance_idx][i]):
distances[i] = distances[min_distance_idx] + self.table[min_distance_idx][i]
return distances
g = DistanceTable(27, [''] * 27)
g.table = [[0.0, 7.2, 3.8, 11.0, 2.2, 3.5, 10.9, 8.6, 7.6, 2.8, 6.4, 3.2, 7.6, 5.2, 4.4, 3.7, 7.6, 2.0, 3.6, 6.5, 1.9, 3.4, 2.4, 6.4, 2.4, 5.0, 3.6]
,[7.2, 0.0, 7.1, 6.4, 6.0, 4.8, 1.6, 2.8, 4.8, 6.3, 7.3, 5.3, 4.8, 3.0, 4.6, 4.5, 7.4, 6.0, 5.0, 4.8, 9.5, 10.9, 8.3, 6.9, 10.0, 4.4, 13.0]
,[3.8, 7.1, 0.0, 9.2, 4.4, 2.8, 8.6, 6.3, 5.3, 1.6, 10.4, 3.0, 5.3, 6.5, 5.6, 5.8, 5.7, 4.1, 3.6, 4.3, 3.3, 5.0, 6.1, 9.7, 6.1, 2.8, 7.4]
,[11.0, 6.4, 9.2, 0.0, 5.6, 6.9, 8.6, 4.0, 11.1, 7.3, 1.0, 6.4, 11.1, 3.9, 4.3, 4.4, 7.2, 5.3, 6.0, 10.6, 5.9, 7.4, 4.7, 0.6, 6.4, 10.1, 10.1]
,[2.2, 6.0, 4.4, 5.6, 0.0, 1.9, 7.9, 5.1, 7.5, 2.6, 6.5, 1.5, 7.5, 3.2, 2.4, 2.7, 1.4, 0.5, 1.7, 6.5, 3.2, 5.2, 2.5, 6.0, 4.2, 5.4, 5.5]
,[3.5, 4.8, 2.8, 6.9, 1.9, 0.0, 6.3, 4.3, 4.5, 1.5, 8.7, 0.8, 4.5, 3.9, 3.0, 3.8, 5.7, 1.9, 1.1, 3.5, 4.9, 6.9, 4.2, 9.0, 5.9, 3.5, 7.2]
,[10.9, 1.6, 8.6, 8.6, 7.9, 6.3, 0.0, 4.0, 4.2, 8.0, 8.6, 6.9, 4.2, 4.2, 8.0, 5.8, 7.2, 7.7, 6.6, 3.2, 11.2, 12.7, 10.0, 8.2, 11.7, 5.1, 14.2]
,[8.6, 2.8, 6.3, 4.0, 5.1, 4.3, 4.0, 0.0, 7.7, 9.3, 4.6, 4.8, 7.7, 1.6, 3.3, 3.4, 3.1, 5.1, 4.6, 6.7, 8.1, 10.4, 7.8, 4.2, 9.5, 6.2, 10.7]
,[7.6, 4.8, 5.3, 11.1, 7.5, 4.5, 4.2, 7.7, 0.0, 4.8, 11.9, 4.7, 0.6, 7.6, 7.8, 6.6, 7.2, 5.9, 5.4, 1.0, 8.5, 10.3, 7.8, 11.5, 9.5, 2.8, 14.1]
,[2.8, 6.3, 1.6, 7.3, 2.6, 1.5, 8.0, 9.3, 4.8, 0.0, 9.4, 1.1, 5.1, 4.6, 3.7, 4.0, 6.7, 2.3, 1.8, 4.1, 3.8, 5.8, 4.3, 7.8, 4.8, 3.2, 6.0]
,[6.4, 7.3, 10.4, 1.0, 6.5, 8.7, 8.6, 4.6, 11.9, 9.4, 0.0, 7.3, 12.0, 4.9, 5.2, 5.4, 8.1, 6.2, 6.9, 11.5, 6.9, 8.3, 4.1, 0.4, 4.9, 11.0, 6.8]
,[3.2, 5.3, 3.0, 6.4, 1.5, 0.8, 6.9, 4.8, 4.7, 1.1, 7.3, 0.0, 4.7, 3.5, 2.6, 2.9, 6.3, 1.2, 1.0, 3.7, 4.1, 6.2, 3.4, 6.9, 5.2, 3.7, 6.4]
,[7.6, 4.8, 5.3, 11.1, 7.5, 4.5, 4.2, 7.7, 0.6, 5.1, 12.0, 4.7, 0.0, 7.3, 7.8, 6.6, 7.2, 5.9, 5.4, 1.0, 8.5, 10.3, 7.8, 11.5, 9.5, 2.8, 14.1]
,[5.2, 3.0, 6.5, 3.9, 3.2, 3.9, 4.2, 1.6, 7.6, 4.6, 4.9, 3.5, 7.3, 0.0, 1.3, 1.5, 4.0, 3.2, 3.0, 6.9, 6.2, 8.2, 5.5, 4.4, 7.2, 6.4, 10.5]
,[4.4, 4.6, 5.6, 4.3, 2.4, 3.0, 8.0, 3.3, 7.8, 3.7, 5.2, 2.6, 7.8, 1.3, 0.0, 0.6, 6.4, 2.4, 2.2, 6.8, 5.3, 7.4, 4.6, 4.8, 6.3, 6.5, 8.8]
,[3.7, 4.5, 5.8, 4.4, 2.7, 3.8, 5.8, 3.4, 6.6, 4.0, 5.4, 2.9, 6.6, 1.5, 0.6, 0.0, 5.6, 1.6, 1.7, 6.4, 4.9, 6.9, 4.2, 5.6, 5.9, 5.7, 8.4]
,[7.6, 7.4, 5.7, 7.2, 1.4, 5.7, 7.2, 3.1, 7.2, 6.7, 8.1, 6.3, 7.2, 4.0, 6.4, 5.6, 0.0, 7.1, 6.1, 7.2, 10.6, 12.0, 9.4, 7.5, 11.1, 6.2, 13.6]
,[2.0, 6.0, 4.1, 5.3, 0.5, 1.9, 7.7, 5.1, 5.9, 2.3, 6.2, 1.2, 5.9, 3.2, 2.4, 1.6, 7.1, 0.0, 1.6, 4.9, 3.0, 5.0, 2.3, 5.5, 4.0, 5.1, 5.2]
,[3.6, 5.0, 3.6, 6.0, 1.7, 1.1, 6.6, 4.6, 5.4, 1.8, 6.9, 1.0, 5.4, 3.0, 2.2, 1.7, 6.1, 1.6, 0.0, 4.4, 4.6, 6.6, 3.9, 6.5, 5.6, 4.3, 6.9]
,[6.5, 4.8, 4.3, 10.6, 6.5, 3.5, 3.2, 6.7, 1.0, 4.1, 11.5, 3.7, 1.0, 6.9, 6.8, 6.4, 7.2, 4.9, 4.4, 0.0, 7.5, 9.3, 6.8, 11.4, 8.5, 1.8, 13.1]
,[1.9, 9.5, 3.3, 5.9, 3.2, 4.9, 11.2, 8.1, 8.5, 3.8, 6.9, 4.1, 8.5, 6.2, 5.3, 4.9, 10.6, 3.0, 4.6, 7.5, 0.0, 2.0, 2.9, 6.4, 2.8, 6.0, 4.1]
,[3.4, 10.9, 5.0, 7.4, 5.2, 6.9, 12.7, 10.4, 10.3, 5.8, 8.3, 6.2, 10.3, 8.2, 7.4, 6.9, 12.0, 5.0, 6.6, 9.3, 2.0, 0.0, 4.4, 7.9, 3.4, 7.9, 4.7]
,[2.4, 8.3, 6.1, 4.7, 2.5, 4.2, 10.0, 7.8, 7.8, 4.3, 4.1, 3.4, 7.8, 5.5, 4.6, 4.2, 9.4, 2.3, 3.9, 6.8, 2.9, 4.4, 0.0, 4.5, 1.7, 6.8, 3.1]
,[6.4, 6.9, 9.7, 0.6, 6.0, 9.0, 8.2, 4.2, 11.5, 7.8, 0.4, 6.9, 11.5, 4.4, 4.8, 5.6, 7.5, 5.5, 6.5, 11.4, 6.4, 7.9, 4.5, 0.0, 5.4, 10.6, 7.8]
,[2.4, 10.0, 6.1, 6.4, 4.2, 5.9, 11.7, 9.5, 9.5, 4.8, 4.9, 5.2, 9.5, 7.2, 6.3, 5.9, 11.1, 4.0, 5.6, 8.5, 2.8, 3.4, 1.7, 5.4, 0.0, 7.0, 1.3]
,[5.0, 4.4, 2.8, 10.1, 5.4, 3.5, 5.1, 6.2, 2.8, 3.2, 11.0, 3.7, 2.8, 6.4, 6.5, 5.7, 6.2, 5.1, 4.3, 1.8, 6.0, 7.9, 6.8, 10.6, 7.0, 0.0, 8.3]
,[3.6, 13.0, 7.4, 10.1, 5.5, 7.2, 14.2, 10.7, 14.1, 6.0, 6.8, 6.4, 14.1, 10.5, 8.8, 8.4, 13.6, 5.2, 6.9, 13.1, 4.1, 4.7, 3.1, 7.8, 1.3, 8.3, 0.0]]
dist = [[0 for column in range(labels_size)]
for row in range(labels_size)]
for i in range(labels_size):
dist[i] = g.find_shortest(i)
print(dist[i])