完成无向图的BFS和拓扑排序

Author: ssjssh

src/ssj/graph/__init__.py

@@ -1,3 +1,7 @@
 #!/usr/bin/env python
 # -*- coding:UTF-8
 __author__ = 'shenshijun'
+
+
+class GraphError(StandardError):
+    pass

src/ssj/graph/digraph.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+# -*- coding:UTF-8
+
+__author__ = 'shenshijun'
+from ssj.lib.queue import Queue
+from ssj.graph import GraphError
+
+
+class Vertex(object):
+    def __init__(self, key, weight=None):
+        """
+        adjust_list 表示邻接节点
+        """
+        self.key = key
+        self.weight_list = []
+        self.adjust_list = []
+        self.in_degree = 0
+        self.backup_in_degree = self.in_degree
+        self.out_degree = 0
+        self.backup_out_degree = self.out_degree
+        self.dist = 0
+
+    def add_adjust(self, to_vertex):
+        self.adjust_list.append(to_vertex)
+        self.out_degree += 1
+        self.backup_out_degree += 1
+
+
+class Digraph(object):
+    def __init__(self):
+        self._vertexes = {}
+
+    def add_vertex(self, key):
+        """
+        Args:
+        :param key:顶点关键字
+        Returns:
+        :rtype: bool
+        """
+        if key in self._vertexes:
+            return False
+        else:
+            self._vertexes[key] = Vertex(key)
+            return True
+
+    def add_edge(self, from_key, to_key):
+        # 首先把两个节点查入到图中
+        self.add_vertex(from_key)
+        self.add_vertex(to_key)
+        self._vertexes[from_key].add_adjust(self._vertexes[to_key])
+        self._vertexes[to_key].in_degree += 1
+        self._vertexes[to_key].backup_in_degree += 1
+
+    def top_sort(self, func):
+        zero_in_degree_queue = Queue()
+
+        # 首先找到所有入度为0的顶点,遍历从这里开始
+        for key, vertex in self._vertexes.iteritems():
+            vertex.backup_in_degree = vertex.in_degree
+            if vertex.in_degree is 0:
+                zero_in_degree_queue.enter(vertex)
+
+        if zero_in_degree_queue.empty():
+            raise GraphError('图中有环,无法执行图的拓扑排序')
+
+        result = []
+        while not zero_in_degree_queue.empty():
+            zero_vertex = zero_in_degree_queue.exit()
+            result.append(func(zero_vertex.key))
+            for vertex in zero_vertex.adjust_list:
+                vertex.in_degree -= 1
+                if vertex.in_degree is 0:
+                    zero_in_degree_queue.enter(vertex)
+
+        # 恢复
+        for key, vertex in self._vertexes.iteritems():
+            vertex.in_degree = vertex.backup_in_degree
+        return result
+
+    def bfs(self, key, func):
+        next_vertex_queue = Queue()
+        for k, vertex in self._vertexes.iteritems():
+            vertex.dist = -1
+
+        result = []
+        self._vertexes[key].dist = 0
+        next_vertex_queue.enter(self._vertexes[key])
+
+        while not next_vertex_queue.empty():
+            vertex = next_vertex_queue.exit()
+            result.append(func(vertex.key, vertex.dist))
+            for adjust_vertex in vertex.adjust_list:
+                if adjust_vertex.dist is -1:
+                    adjust_vertex.dist = vertex.dist + 1
+                    next_vertex_queue.enter(adjust_vertex)
+
+        for k, vertex in self._vertexes.iteritems():
+            if vertex.dist is -1:
+                result.extend(self.bfs(vertex.key, func))
+        return result
+
+
+def main():
+    graph = Digraph()
+    graph.add_edge('v1', 'v2')
+    graph.add_edge('v1', 'v3')
+    graph.add_edge('v1', 'v4')
+    graph.add_edge('v2', 'v4')
+    graph.add_edge('v4', 'v3')
+    graph.add_edge('v4', 'v7')
+    graph.add_edge('v4', 'v6')
+    graph.add_edge('v3', 'v6')
+    graph.add_edge('v2', 'v5')
+    graph.add_edge('v5', 'v4')
+    graph.add_edge('v5', 'v7')
+    graph.add_edge('v7', 'v6')
+    print graph.top_sort(lambda key: key)
+    print graph.bfs('v1', lambda key, dist: (key, dist))
+
+
+if __name__ == "__main__":
+    main()

src/ssj/graph/general_graph.py

@@ -2,11 +2,36 @@
 # -*- coding:UTF-8
 __author__ = 'shenshijun'
 
+"""
+定义:
+AVL树是高度平衡的二叉搜索树,平衡的方式是每一个节点左右子树高度差顶多为1.
+每一个节点中会存储这个节点的高度。节点高度是指从这个节点到叶节点的最大距离。
+
+高度:
+AVL树的高度大约是1.44log(N)。
+高度为n的AVL树的最小节点数：s(h)=s(h-1)+s(h-2)+1
+"""
+
+
+class Node(object):
+    """"""
+
+    def __init__(self, value, parent, left, right, height=0):
+        """Constructor for Node"""
+        self.value = value
+        self.height = height
+        self.parent = parent
+        self.left = left
+        self.right = right
+
 
-# TODO 实现
 class AVLTree(object):
     """"""
 
     def __init__(self):
         """Constructor for """
+        self.__root = None
+        self.__size = 0
+
+    def insert(self, value):
         pass