Pythonでツリーを実装するにはどうすればいいですか？ JavaのようにPythonに組み込まれたデータ構造はありますか？
私は一般的な木を作ろうとしています。ツリーを実装するためにPythonに組み込まれたデータ構造はありますか？
エニーツリー
私はお勧めします https://pypi.python.org/pypi/anytree （私は作者です）
例
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'))
特徴
anytree は強力なAPIも持っています。
- 簡単なツリー作成
- 単純なツリー修正
- 事前順序木反復
- ポストオーダーツリーの反復
- 相対および絶対ノードパスを解決する
- あるノードから別のノードへと歩く。
- ツリーレンダリング（上記の例を参照）
- ノード接続/接続解除
Pythonには、Javaのように広範囲の「組み込み」データ構造はありません。しかし、Pythonは動的であるため、一般的なツリーは簡単に作成できます。たとえば、二分木は次のようになります。
class Tree:
def __init__(self):
self.left = None
self.right = None
self.data = None
あなたはこのようにそれを使うことができます：
root = Tree()
root.data = "root"
root.left = Tree()
root.left.data = "left"
root.right = Tree()
root.right.data = "right"
総称ツリーは、ゼロ個以上の子を持つノードで、それぞれが適切な（ツリー）ノードです。それは二分木と同じではありません、それらはいくつかの用語を共有しますが、それらは異なるデータ構造です。
Pythonにはジェネリックツリーのための組み込みデータ構造はありませんが、クラスを使って簡単に実装できます。
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')])])
あなたが試すことができます：
from collections import defaultdict
def tree(): return defaultdict(tree)
users = tree()
users['harold']['username'] = 'hrldcpr'
users['handler']['username'] = 'matthandlersux'
ここで示唆されるように： https://Gist.github.com/201225
class Node:
"""
Class Node
"""
def __init__(self, value):
self.left = None
self.data = value
self.right = None
class Tree:
"""
Class tree will provide a tree as well as utility functions.
"""
def createNode(self, data):
"""
Utility function to create a node.
"""
return Node(data)
def insert(self, node , data):
"""
Insert function will insert a node into tree.
Duplicate keys are not allowed.
"""
#if tree is empty , return a root node
if node is None:
return self.createNode(data)
# if data is smaller than parent , insert it into left side
if data < node.data:
node.left = self.insert(node.left, data)
Elif data > node.data:
node.right = self.insert(node.right, data)
return node
def search(self, node, data):
"""
Search function will search a node into tree.
"""
# if root is None or root is the search data.
if node is None or node.data == data:
return node
if node.data < data:
return self.search(node.right, data)
else:
return self.search(node.left, data)
def deleteNode(self,node,data):
"""
Delete function will delete a node into tree.
Not complete , may need some more scenarion that we can handle
Now it is handling only leaf.
"""
# Check if tree is empty.
if node is None:
return None
# searching key into BST.
if data < node.data:
node.left = self.deleteNode(node.left, data)
Elif data > node.data:
node.right = self.deleteNode(node.right, data)
else: # reach to the node that need to delete from BST.
if node.left is None and node.right is None:
del node
if node.left == None:
temp = node.right
del node
return temp
Elif node.right == None:
temp = node.left
del node
return temp
return node
def traverseInorder(self, root):
"""
traverse function will print all the node in the tree.
"""
if root is not None:
self.traverseInorder(root.left)
print root.data
self.traverseInorder(root.right)
def traversePreorder(self, root):
"""
traverse function will print all the node in the tree.
"""
if root is not None:
print root.data
self.traversePreorder(root.left)
self.traversePreorder(root.right)
def traversePostorder(self, root):
"""
traverse function will print all the node in the tree.
"""
if root is not None:
self.traversePreorder(root.left)
self.traversePreorder(root.right)
print root.data
def main():
root = None
tree = Tree()
root = tree.insert(root, 10)
print root
tree.insert(root, 20)
tree.insert(root, 30)
tree.insert(root, 40)
tree.insert(root, 70)
tree.insert(root, 60)
tree.insert(root, 80)
print "Traverse Inorder"
tree.traverseInorder(root)
print "Traverse Preorder"
tree.traversePreorder(root)
print "Traverse Postorder"
tree.traversePostorder(root)
if __== "__main__":
main()
Greg Hewgillの答えは素晴らしいですが、もしあなたがレベルごとにより多くのノードを必要とするなら、あなたはそれらを作成するためにリスト|辞書を使うことができます。
class node(object):
def __init__(self):
self.name=None
self.node=[]
self.otherInfo = None
self.prev=None
def nex(self,child):
"Gets a node by number"
return self.node[child]
def prev(self):
return self.prev
def goto(self,data):
"Gets the node by name"
for child in range(0,len(self.node)):
if(self.node[child].name==data):
return self.node[child]
def add(self):
node1=node()
self.node.append(node1)
node1.prev=self
return node1
それでは、ルートを作成してビルドしてください。ex：
tree=node() #create a node
tree.name="root" #name it root
tree.otherInfo="blue" #or what ever
tree=tree.add() #add a node to the root
tree.name="node1" #name it
root
/
child1
tree=tree.add()
tree.name="grandchild1"
root
/
child1
/
grandchild1
tree=tree.prev()
tree=tree.add()
tree.name="gchild2"
root
/
child1
/ \
grandchild1 gchild2
tree=tree.prev()
tree=tree.prev()
tree=tree.add()
tree=tree.name="child2"
root
/ \
child1 child2
/ \
grandchild1 gchild2
tree=tree.prev()
tree=tree.goto("child1") or tree=tree.nex(0)
tree.name="changed"
root
/ \
changed child2
/ \
grandchild1 gchild2
これを機能させる方法を考え出すためには、これで十分なはずです。
私は、ルートツリーを辞書{child:parent}として実装しました。たとえば、ルートノードが0の場合、ツリーは次のようになります。
tree={1:0, 2:0, 3:1, 4:2, 5:3}
この構造により、任意のノードからルートへのパスに沿って上向きに移動することが非常に簡単になりました。これは、私が取り組んでいた問題に関連していました。
class Tree(dict):
"""A tree implementation using python's autovivification feature."""
def __missing__(self, key):
value = self[key] = type(self)()
return value
#cast a (nested) dict to a (nested) Tree class
def __init__(self, data={}):
for k, data in data.items():
if isinstance(data, dict):
self[k] = type(self)(data)
else:
self[k] = data
辞書として機能しますが、あなたが望むように多くのネストされた辞書を提供します。以下を試してください。
your_tree = Tree()
your_tree['a']['1']['x'] = '@'
your_tree['a']['1']['y'] = '#'
your_tree['a']['2']['x'] = '$'
your_tree['a']['3'] = '%'
your_tree['b'] = '*'
入れ子になった辞書を配信します...これは確かにツリーとして機能します。
{'a': {'1': {'x': '@', 'y': '#'}, '2': {'x': '$'}, '3': '%'}, 'b': '*'}
...すでに辞書がある場合は、各レベルを木にキャストします。
d = {'foo': {'amy': {'what': 'runs'} } }
tree = Tree(d)
print(d['foo']['amy']['what']) # returns 'runs'
d['foo']['amy']['when'] = 'now' # add new branch
このようにして、あなたは望むように各辞書レベルを編集/追加/削除し続けることができます。走査などのためのすべてのdictメソッドがまだ適用されます。
入れ子になった辞書を使ってツリーを実装しました。それは非常に簡単で、そしてそれは私にとってはかなり大きいデータセットでうまくいきました。以下にサンプルを投稿しましたが、もっと詳しく見ることができます Google code
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)
私は私のサイトでPython [3]ツリーの実装を公開しました： http://www.quesucede.com/page/show/id/python_3_tree_implementation 。
それが役に立つことを願って、
わかりました、これはコードです：
import uuid
def sanitize_id(id):
return id.strip().replace(" ", "")
(_ADD, _DELETE, _INSERT) = range(3)
(_ROOT, _DEPTH, _WIDTH) = range(3)
class Node:
def __init__(self, name, identifier=None, expanded=True):
self.__identifier = (str(uuid.uuid1()) if identifier is None else
sanitize_id(str(identifier)))
self.name = name
self.expanded = expanded
self.__bpointer = None
self.__fpointer = []
@property
def identifier(self):
return self.__identifier
@property
def bpointer(self):
return self.__bpointer
@bpointer.setter
def bpointer(self, value):
if value is not None:
self.__bpointer = sanitize_id(value)
@property
def fpointer(self):
return self.__fpointer
def update_fpointer(self, identifier, mode=_ADD):
if mode is _ADD:
self.__fpointer.append(sanitize_id(identifier))
Elif mode is _DELETE:
self.__fpointer.remove(sanitize_id(identifier))
Elif mode is _INSERT:
self.__fpointer = [sanitize_id(identifier)]
class Tree:
def __init__(self):
self.nodes = []
def get_index(self, position):
for index, node in enumerate(self.nodes):
if node.identifier == position:
break
return index
def create_node(self, name, identifier=None, parent=None):
node = Node(name, identifier)
self.nodes.append(node)
self.__update_fpointer(parent, node.identifier, _ADD)
node.bpointer = parent
return node
def show(self, position, level=_ROOT):
queue = self[position].fpointer
if level == _ROOT:
print("{0} [{1}]".format(self[position].name,
self[position].identifier))
else:
print("\t"*level, "{0} [{1}]".format(self[position].name,
self[position].identifier))
if self[position].expanded:
level += 1
for element in queue:
self.show(element, level) # recursive call
def expand_tree(self, position, mode=_DEPTH):
# Python generator. Loosly based on an algorithm from 'Essential LISP' by
# John R. Anderson, Albert T. Corbett, and Brian J. Reiser, page 239-241
yield position
queue = self[position].fpointer
while queue:
yield queue[0]
expansion = self[queue[0]].fpointer
if mode is _DEPTH:
queue = expansion + queue[1:] # depth-first
Elif mode is _WIDTH:
queue = queue[1:] + expansion # width-first
def is_branch(self, position):
return self[position].fpointer
def __update_fpointer(self, position, identifier, mode):
if position is None:
return
else:
self[position].update_fpointer(identifier, mode)
def __update_bpointer(self, position, identifier):
self[position].bpointer = identifier
def __getitem__(self, key):
return self.nodes[self.get_index(key)]
def __setitem__(self, key, item):
self.nodes[self.get_index(key)] = item
def __len__(self):
return len(self.nodes)
def __contains__(self, identifier):
return [node.identifier for node in self.nodes
if node.identifier is identifier]
if __== "__main__":
tree = Tree()
tree.create_node("Harry", "harry") # root node
tree.create_node("Jane", "jane", parent = "harry")
tree.create_node("Bill", "bill", parent = "harry")
tree.create_node("Joe", "joe", parent = "jane")
tree.create_node("Diane", "diane", parent = "jane")
tree.create_node("George", "george", parent = "diane")
tree.create_node("Mary", "mary", parent = "diane")
tree.create_node("Jill", "jill", parent = "george")
tree.create_node("Carol", "carol", parent = "jill")
tree.create_node("Grace", "grace", parent = "bill")
tree.create_node("Mark", "mark", parent = "jane")
print("="*80)
tree.show("harry")
print("="*80)
for node in tree.expand_tree("harry", mode=_WIDTH):
print(node)
print("="*80)
もっと簡単な方法が必要な場合、ツリーは再帰的にネストされたリストにすぎません（setはハッシュ可能ではないため）。
[root, [child_1, [[child_11, []], [child_12, []]], [child_2, []]]]
各枝が対になっている場所：[object、[children]]
そしてそれぞれの葉はペアです：[object、[]]
しかし、メソッドを持つクラスが必要な場合は、任意のツリーを使用できます。
どのような操作が必要ですか？多くの場合、Pythonには辞書またはbisectモジュールを含むリストを使用した優れた解決策があります。
PyPI にはたくさんのたくさんのツリーの実装があります、そして多くのツリー型は純粋なPythonで自分自身を実装するのにはささいなことです。しかし、これはほとんど必要ありません。
ツリーデータ構造を作成したい場合は、まずtreeElementオブジェクトを作成する必要があります。 treeElementオブジェクトを作成すると、ツリーの動作を決定できます。
これを行うには、TreeElementクラスを使用します。
class TreeElement (object):
def __init__(self):
self.elementName = None
self.element = []
self.previous = None
self.elementScore = None
self.elementParent = None
self.elementPath = []
self.treeLevel = 0
def goto(self, data):
for child in range(0, len(self.element)):
if (self.element[child].elementName == data):
return self.element[child]
def add(self):
single_element = TreeElement()
single_element.elementName = self.elementName
single_element.previous = self.elementParent
single_element.elementScore = self.elementScore
single_element.elementPath = self.elementPath
single_element.treeLevel = self.treeLevel
self.element.append(single_element)
return single_element
さて、この要素を使ってツリーを作成する必要があります。この例ではA *ツリーを使っています。
class AStarAgent(Agent):
# Initialization Function: Called one time when the game starts
def registerInitialState(self, state):
return;
# GetAction Function: Called with every frame
def getAction(self, state):
# Sorting function for the queue
def sortByHeuristic(each_element):
if each_element.elementScore:
individual_score = each_element.elementScore[0][0] + each_element.treeLevel
else:
individual_score = admissibleHeuristic(each_element)
return individual_score
# check the game is over or not
if state.isWin():
print('Job is done')
return Directions.STOP
Elif state.isLose():
print('you lost')
return Directions.STOP
# Create empty list for the next states
astar_queue = []
astar_leaf_queue = []
astar_tree_level = 0
parent_tree_level = 0
# Create Tree from the give node element
astar_tree = TreeElement()
astar_tree.elementName = state
astar_tree.treeLevel = astar_tree_level
astar_tree = astar_tree.add()
# Add first element into the queue
astar_queue.append(astar_tree)
# Traverse all the elements of the queue
while astar_queue:
# Sort the element from the queue
if len(astar_queue) > 1:
astar_queue.sort(key=lambda x: sortByHeuristic(x))
# Get the first node from the queue
astar_child_object = astar_queue.pop(0)
astar_child_state = astar_child_object.elementName
# get all legal actions for the current node
current_actions = astar_child_state.getLegalPacmanActions()
if current_actions:
# get all the successor state for these actions
for action in current_actions:
# Get the successor of the current node
next_state = astar_child_state.generatePacmanSuccessor(action)
if next_state:
# evaluate the successor states using scoreEvaluation heuristic
element_scored = [(admissibleHeuristic(next_state), action)]
# Increase the level for the child
parent_tree_level = astar_tree.goto(astar_child_state)
if parent_tree_level:
astar_tree_level = parent_tree_level.treeLevel + 1
else:
astar_tree_level += 1
# create tree for the finding the data
astar_tree.elementName = next_state
astar_tree.elementParent = astar_child_state
astar_tree.elementScore = element_scored
astar_tree.elementPath.append(astar_child_state)
astar_tree.treeLevel = astar_tree_level
astar_object = astar_tree.add()
# If the state exists then add that to the queue
astar_queue.append(astar_object)
else:
# Update the value leaf into the queue
astar_leaf_state = astar_tree.goto(astar_child_state)
astar_leaf_queue.append(astar_leaf_state)
オブジェクトに任意の要素を追加/削除することはできますが、構造体を相互作用させることができます。
Bruno's answer に大まかに基づくもう1つのツリー実装。
class Node:
def __init__(self):
self.name: str = ''
self.children: List[Node] = []
self.parent: Node = self
def __getitem__(self, i: int) -> 'Node':
return self.children[i]
def add_child(self):
child = Node()
self.children.append(child)
child.parent = self
return child
def __str__(self) -> str:
def _get_character(x, left, right) -> str:
if x < left:
return '/'
Elif x >= right:
return '\\'
else:
return '|'
if len(self.children):
children_lines: Sequence[List[str]] = list(map(lambda child: str(child).split('\n'), self.children))
widths: Sequence[int] = list(map(lambda child_lines: len(child_lines[0]), children_lines))
max_height: int = max(map(len, children_lines))
total_width: int = sum(widths) + len(widths) - 1
left: int = (total_width - len(self.name) + 1) // 2
right: int = left + len(self.name)
return '\n'.join((
self.name.center(total_width),
' '.join(map(lambda width, position: _get_character(position - width // 2, left, right).center(width),
widths, accumulate(widths, add))),
*map(
lambda row: ' '.join(map(
lambda child_lines: child_lines[row] if row < len(child_lines) else ' ' * len(child_lines[0]),
children_lines)),
range(max_height))))
else:
return self.name
そしてそれを使用する方法の例：
tree = Node()
tree.name = 'Root node'
tree.add_child()
tree[0].name = 'Child node 0'
tree.add_child()
tree[1].name = 'Child node 1'
tree.add_child()
tree[2].name = 'Child node 2'
tree[1].add_child()
tree[1][0].name = 'Grandchild 1.0'
tree[2].add_child()
tree[2][0].name = 'Grandchild 2.0'
tree[2].add_child()
tree[2][1].name = 'Grandchild 2.1'
print(tree)
どちらを出力する必要があります：
ルートノード //\ 子ノード0子ノード1子ノード2 |/\ 孫1.0孫2.0孫2.1