我已经在我的网站https://web.archive.org/web/20120723175438/www.quesucede.com/page/show/id/python_3_tree_implementation上发布了一个Python 3树的实现

代码如下:

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 __name__ == "__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)

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

作为一个字典，但提供尽可能多的嵌套字典。 试试下面的方法:

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']            = '*'

将传递一个嵌套的字典…就像树一样。

{'a': {'1': {'x': '@', 'y': '#'}, '2': {'x': '$'}, '3': '%'}, 'b': '*'}

．.． 如果你已经有字典了，它会把每一层都投射到一棵树上:

d = {'foo': {'amy': {'what': 'runs'} } }
tree = Tree(d)

print(d['foo']['amy']['what']) # returns 'runs'
d['foo']['amy']['when'] = 'now' # add new branch

这样，你就可以随心所欲地编辑/添加/删除每个词典级别。 遍历等所有dict方法仍然适用。

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.traversePostorder(root.left)
            self.traversePostorder(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 __name__ == "__main__":
    main()

如果您想要创建树数据结构，那么首先必须创建treeElement对象。如果您创建了treeElement对象，那么您可以决定树的行为。

下面是TreeElement类:

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

现在，我们必须使用这个元素来创建树，在这个例子中我使用的是A*树。

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)

你可以从对象中添加/删除任何元素，但要使结构为完整的。

并没有内置树，但是可以通过从List继承Node类型并编写遍历方法来轻松地构造一个树。如果你这样做，我发现平分法很有用。

您还可以浏览PyPi上的许多实现。

如果我没记错的话，Python标准库不包含树数据结构，原因和。net基类库不包含树数据结构是一样的:内存的局部性降低了，导致缓存丢失更多。在现代处理器上，将大量内存放入缓存通常会更快，而“指针丰富”的数据结构会抵消这种好处。

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')])])