如何在Python中实现通用树?这有内置的数据结构吗?
当前回答
我将根树实现为字典{child:parent}。比如根节点为0,树可能是这样的:
tree={1:0, 2:0, 3:1, 4:2, 5:3}
这种结构使得沿着一条路径从任意节点向上到根结点非常容易,这与我正在处理的问题有关。
其他回答
泛型树是一个具有零个或多个子节点的节点,每个子节点都是一个合适的(树)节点。它与二叉树不同,它们是不同的数据结构,尽管它们都有一些相同的术语。
Python中没有任何用于泛型树的内置数据结构,但很容易通过类实现。
class Tree(object):
"Generic tree node."
def __init__(self, name='root', children=None):
self.name = name
self.children = []
if children is not None:
for child in children:
self.add_child(child)
def __repr__(self):
return self.name
def add_child(self, node):
assert isinstance(node, Tree)
self.children.append(node)
# *
# /|\
# 1 2 +
# / \
# 3 4
t = Tree('*', [Tree('1'),
Tree('2'),
Tree('+', [Tree('3'),
Tree('4')])])
Treelib也很方便完成这项任务。文档可以在treelib找到。
from treelib import Node, Tree
tree = Tree() # creating an object
tree.create_node("Harry", "harry") # root node
tree.create_node("Jane", "jane", parent="harry") #adding nodes
tree.create_node("Bill", "bill", parent="harry")
tree.create_node("Diane", "diane", parent="jane")
tree.create_node("Mary", "mary", parent="diane")
tree.create_node("Mark", "mark", parent="jane")
tree.show()
Harry
├── Bill
└── Jane
├── Diane
│ └── Mary
└── Mark
我推荐任何树(我是作者)。
例子:
from anytree import Node, RenderTree
udo = Node("Udo")
marc = Node("Marc", parent=udo)
lian = Node("Lian", parent=marc)
dan = Node("Dan", parent=udo)
jet = Node("Jet", parent=dan)
jan = Node("Jan", parent=dan)
joe = Node("Joe", parent=dan)
print(udo)
Node('/Udo')
print(joe)
Node('/Udo/Dan/Joe')
for pre, fill, node in RenderTree(udo):
print("%s%s" % (pre, node.name))
Udo
├── Marc
│ └── Lian
└── Dan
├── Jet
├── Jan
└── Joe
print(dan.children)
(Node('/Udo/Dan/Jet'), Node('/Udo/Dan/Jan'), Node('/Udo/Dan/Joe'))
anytree也有一个强大的API:
简单的树创建 简单树修改 预序树迭代 后序树迭代 解析相对节点路径和绝对节点路径 从一个节点移动到另一个节点。 树渲染(参见上面的例子) 节点连接/分离连接
我使用嵌套字典实现了树。这很容易做到,而且对我来说,它在相当大的数据集上很有效。我在下面发布了一个示例,你可以在谷歌代码中看到更多
def addBallotToTree(self, tree, ballotIndex, ballot=""):
"""Add one ballot to the tree.
The root of the tree is a dictionary that has as keys the indicies of all
continuing and winning candidates. For each candidate, the value is also
a dictionary, and the keys of that dictionary include "n" and "bi".
tree[c]["n"] is the number of ballots that rank candidate c first.
tree[c]["bi"] is a list of ballot indices where the ballots rank c first.
If candidate c is a winning candidate, then that portion of the tree is
expanded to indicate the breakdown of the subsequently ranked candidates.
In this situation, additional keys are added to the tree[c] dictionary
corresponding to subsequently ranked candidates.
tree[c]["n"] is the number of ballots that rank candidate c first.
tree[c]["bi"] is a list of ballot indices where the ballots rank c first.
tree[c][d]["n"] is the number of ballots that rank c first and d second.
tree[c][d]["bi"] is a list of the corresponding ballot indices.
Where the second ranked candidates is also a winner, then the tree is
expanded to the next level.
Losing candidates are ignored and treated as if they do not appear on the
ballots. For example, tree[c][d]["n"] is the total number of ballots
where candidate c is the first non-losing candidate, c is a winner, and
d is the next non-losing candidate. This will include the following
ballots, where x represents a losing candidate:
[c d]
[x c d]
[c x d]
[x c x x d]
During the count, the tree is dynamically updated as candidates change
their status. The parameter "tree" to this method may be the root of the
tree or may be a sub-tree.
"""
if ballot == "":
# Add the complete ballot to the tree
weight, ballot = self.b.getWeightedBallot(ballotIndex)
else:
# When ballot is not "", we are adding a truncated ballot to the tree,
# because a higher-ranked candidate is a winner.
weight = self.b.getWeight(ballotIndex)
# Get the top choice among candidates still in the running
# Note that we can't use Ballots.getTopChoiceFromWeightedBallot since
# we are looking for the top choice over a truncated ballot.
for c in ballot:
if c in self.continuing | self.winners:
break # c is the top choice so stop
else:
c = None # no candidates left on this ballot
if c is None:
# This will happen if the ballot contains only winning and losing
# candidates. The ballot index will not need to be transferred
# again so it can be thrown away.
return
# Create space if necessary.
if not tree.has_key(c):
tree[c] = {}
tree[c]["n"] = 0
tree[c]["bi"] = []
tree[c]["n"] += weight
if c in self.winners:
# Because candidate is a winner, a portion of the ballot goes to
# the next candidate. Pass on a truncated ballot so that the same
# candidate doesn't get counted twice.
i = ballot.index(c)
ballot2 = ballot[i+1:]
self.addBallotToTree(tree[c], ballotIndex, ballot2)
else:
# Candidate is in continuing so we stop here.
tree[c]["bi"].append(ballotIndex)
您可以使用Python中的dataclasses模块创建Tree数据结构。
iter方法可用于使树可迭代,允许您通过改变yield语句的顺序来遍历树。
contains方法可用于检查树中是否存在特定值。
from dataclasses import dataclass
# A
# / \
# B C
# / \ \
# D E F
# / \
# G H
@dataclass
class Node:
data: str
left: Node = None
right: Node = None
def __iter__(self):
if self.left:
yield from self.left
yield self
if self.right:
yield from self.right
def __contains__(self, other):
for node in self:
if node.data == other:
return True
return False
t = Node(
'A',
Node(
'B',
Node(
'D',
Node('G'),
Node('H'),
),
Node('E'),
),
Node(
'C',
right=Node('F'),
),
)
assert ('A' in t) is True
assert ('I' in t) is not True
for node in t:
print(node.data, ' -> ', end='')
# G -> D -> H -> B -> E -> A -> C -> F ->
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