NO.66——人工智能學習：python實現一致代價搜索算法

目的：

               在廣度優先算法上進行進化。一致代價搜索算法每次擴展的是當前路徑消耗g(n)最小的節點n。

源碼：

數據結構： 

• frontier : 邊緣，存儲未擴展的節點。通過維護一個優先級隊列，按路徑損耗來排列。
• explored ：探索集，保存已訪問的節點。

算法流程：

• 如果邊緣爲空，則返回失敗。操作：EMPTY?(frontier)
• 否則從邊緣中選擇一個葉子節點。操作：POP(frontier)
• 目標測試：通過返回，否則將葉子節點的狀態放在探索集
• 遍歷葉子節點的所有動作

                    每個動作產生子節點

                    如果子節點的狀態不在探索集或者邊緣，則插入到邊緣集合。操作：INSERT(child, frontier)

                    否則如果邊緣集合中如果存在此狀態且有更高的路徑消耗，則用子節點替代邊緣集合中的狀態

算法性能分析：

            當所有的單步消耗都相等時，一致代價搜索與廣度優先搜索類似。在終止條件上，廣度優先搜索在找到解時終止，而一致代價搜索會檢查目標深度的所有節點，看誰的代價最小。在這種情況下，一致代價搜索在深度d無意義的做了更多工作。

示例代碼：（參考http://blog.csdn.net/jdh99）

import pandas as pd
from pandas import Series, DataFrame

# 城市信息：city1 city2 path_cost
_city_info = None

# 按照路徑消耗進行排序的FIFO,低路徑消耗在前面
_frontier_priority = []


# 節點數據結構
class Node:
    def __init__(self, state, parent, action, path_cost):
        self.state = state
        self.parent = parent
        self.action = action
        self.path_cost = path_cost


def main():
    global _city_info
    import_city_info()
    
    while True:
        src_city = input('input src city\n')
        dst_city = input('input dst city\n')
        # result = breadth_first_search(src_city, dst_city)
        result = uniform_cost_search(src_city, dst_city)
        if not result:
            print('from city: %s to city %s search failure' % (src_city, dst_city))
        else:
            print('from city: %s to city %s search success' % (src_city, dst_city))
            path = []
            while True:
                path.append(result.state)
                if result.parent is None:
                    break
                result = result.parent
            size = len(path)
            for i in range(size):
                if i < size - 1:
                    print('%s->' % path.pop(), end='')
                else:
                    print(path.pop())


def import_city_info():
    global _city_info
    data = [{'city1': 'Oradea', 'city2': 'Zerind', 'path_cost': 71},
            {'city1': 'Oradea', 'city2': 'Sibiu', 'path_cost': 151},
            {'city1': 'Zerind', 'city2': 'Arad', 'path_cost': 75},
            {'city1': 'Arad', 'city2': 'Sibiu', 'path_cost': 140},
            {'city1': 'Arad', 'city2': 'Timisoara', 'path_cost': 118},
            {'city1': 'Timisoara', 'city2': 'Lugoj', 'path_cost': 111},
            {'city1': 'Lugoj', 'city2': 'Mehadia', 'path_cost': 70},
            {'city1': 'Mehadia', 'city2': 'Drobeta', 'path_cost': 75},
            {'city1': 'Drobeta', 'city2': 'Craiova', 'path_cost': 120},
            {'city1': 'Sibiu', 'city2': 'Fagaras', 'path_cost': 99},
            {'city1': 'Sibiu', 'city2': 'Rimnicu Vilcea', 'path_cost': 80},
            {'city1': 'Rimnicu Vilcea', 'city2': 'Craiova', 'path_cost': 146},
            {'city1': 'Rimnicu Vilcea', 'city2': 'Pitesti', 'path_cost': 97},
            {'city1': 'Craiova', 'city2': 'Pitesti', 'path_cost': 138},
            {'city1': 'Fagaras', 'city2': 'Bucharest', 'path_cost': 211},
            {'city1': 'Pitesti', 'city2': 'Bucharest', 'path_cost': 101},
            {'city1': 'Bucharest', 'city2': 'Giurgiu', 'path_cost': 90},
            {'city1': 'Bucharest', 'city2': 'Urziceni', 'path_cost': 85},
            {'city1': 'Urziceni', 'city2': 'Vaslui', 'path_cost': 142},
            {'city1': 'Urziceni', 'city2': 'Hirsova', 'path_cost': 98},
            {'city1': 'Neamt', 'city2': 'Iasi', 'path_cost': 87},
            {'city1': 'Iasi', 'city2': 'Vaslui', 'path_cost': 92},
            {'city1': 'Hirsova', 'city2': 'Eforie', 'path_cost': 86}]
            
    _city_info = DataFrame(data, columns=['city1', 'city2', 'path_cost'])
# print(_city_info)

'''
def breadth_first_search(src_state, dst_state):
    global _city_info
    
    node = Node(src_state, None, None, 0)
    frontier = [node]
    explored = []

    while True:
        if len(frontier) == 0:
            return False
        node = frontier.pop(0)
        explored.append(node.state)
        # 目標測試
        if node.state == dst_state:
            return node
        if node.parent is not None:
            print('deal node:state:%s\tparent state:%s\tpath cost:%d' % (node.state, node.parent.state, node.path_cost))
        else:
            print('deal node:state:%s\tparent state:%s\tpath cost:%d' % (node.state, None, node.path_cost))
        
        # 遍歷子節點
        for i in range(len(_city_info)):
            dst_city = ''
            if _city_info['city1'][i] == node.state:
                dst_city = _city_info['city2'][i]
            elif _city_info['city2'][i] == node.state:
                dst_city = _city_info['city1'][i]
            if dst_city == '':
                continue
            child = Node(dst_city, node, 'go', node.path_cost + _city_info['path_cost'][i])
            print('\tchild node:state:%s path cost:%d' % (child.state, child.path_cost))
            if child.state not in explored and not is_node_in_frontier(frontier, child):
                frontier.append(child)
                print('\t\t add child to child')
'''

def is_node_in_frontier(frontier, node):
    for x in frontier:
        if node.state == x.state:
            return True
    return False


def uniform_cost_search(src_state, dst_state):
    global _city_info, _frontier_priority
    
    node = Node(src_state, None, None, 0)
    frontier_priority_add(node)
    explored = []
    
    while True:
        if len(_frontier_priority) == 0:
            return False
        node = _frontier_priority.pop(0)
        explored.append(node.state)
        # 目標測試
        if node.state == dst_state:
            print('\t this node is goal!')
            return node
        if node.parent is not None:
            print('deal node:state:%s\tparent state:%s\tpath cost:%d' % (node.state, node.parent.state, node.path_cost))
        else:
            print('deal node:state:%s\tparent state:%s\tpath cost:%d' % (node.state, None, node.path_cost))
    
        
        # 遍歷子節點
        for i in range(len(_city_info)):
            dst_city = ''
            if _city_info['city1'][i] == node.state:
                dst_city = _city_info['city2'][i]
            elif _city_info['city2'][i] == node.state:
                dst_city = _city_info['city1'][i]
            if dst_city == '':
                continue
            child = Node(dst_city, node, 'go', node.path_cost + _city_info['path_cost'][i])
            print('\tchild node:state:%s path cost:%d' % (child.state, child.path_cost))
            
            if child.state not in explored and not is_node_in_frontier(_frontier_priority, child):
                frontier_priority_add(child)
                print('\t\t add child to frontier')
            elif is_node_in_frontier(_frontier_priority, child):
                # 替代爲路徑消耗少的節點
                frontier_priority_replace_by_priority(child)


def frontier_priority_add(node):
    """
        :param Node node:
        :return:
        """
    global _frontier_priority
    size = len(_frontier_priority)
    for i in range(size):
        #如果新加入的節點存在閾值較小的情況，插入隊列
        if node.path_cost < _frontier_priority[i].path_cost:
            _frontier_priority.insert(i, node)
            return
    #否則，新添加的節點比優先級隊列中現有的節點閾值都大，直接添加到隊列末尾
    _frontier_priority.append(node)


def frontier_priority_replace_by_priority(node):
    """
        :param Node node:
        :return:
        """
    global _frontier_priority
    size = len(_frontier_priority)
    for i in range(size):
        if _frontier_priority[i].state == node.state and _frontier_priority[i].path_cost > node.path_cost:
            print('\t\t replace state: %s old cost:%d new cost:%d' % (node.state,_frontier_priority[i].path_cost,node.path_cost))
            _frontier_priority[i] = node
            return


if __name__ == '__main__':
    main()