String State() { return "****************\n" + "add 执行添加\n" + "Select 执行查询\n" + "update 执行修改\n" + "delete 执行删除\n" + "exit 初始化集合\n" + "esc 执行返回\n" + "end 退出程序\n" + "****************\n" + "请输入你要执行的操作代码："; }

这是一个Java方法的定义，方法名为State，返回一个字符串类型的值（String）。该方法的作用是返回一个包含一些操作指令的字符串，用于提示用户进行操作选择。具体内容为：

"****************\n" +
"add 执行添加\n" +
"Select 执行查询\n" +
"update 执行修改\n" +
"delete 执行删除\n" +
"exit 初始化集合\n" +
"esc 执行返回\n" +
"end 退出程序\n" +
"****************\n" +
"请输入你要执行的操作代码："

该字符串包含了一些操作指令，例如add表示执行添加操作，Select表示执行查询操作，update表示执行修改操作等等，并以换行符和分隔线的形式进行排版。该方法的具体实现如上所示。

相关问题

A*算法野人传教士问题java

下面是使用A*算法解决野人传教士问题的Java代码示例：

import java.util.*;

public class AStarSearch {
    private static final int MAX_STATE_SPACE = 100000;
    private static final int MAX_PATH_COST = 100000;
    private static final int NUM_MISSIONARIES = 3;
    private static final int NUM_CANNIBALS = 3;

    // State representation of a node
    static class Node {
        int[] state = new int[2 * NUM_MISSIONARIES + 2];
        int pathCost;
        int heuristicCost;
        Node parent;

        public Node(int[] state, int pathCost, int heuristicCost, Node parent) {
            this.state = state;
            this.pathCost = pathCost;
            this.heuristicCost = heuristicCost;
            this.parent = parent;
        }

        public boolean isGoalState() {
            return state[0] == 0 && state[1] == 0;
        }

        @Override
        public boolean equals(Object obj) {
            if (obj instanceof Node) {
                Node node = (Node) obj;
                return Arrays.equals(state, node.state);
            }
            return false;
        }

        @Override
        public int hashCode() {
            return Arrays.hashCode(state);
        }

        @Override
        public String toString() {
            return Arrays.toString(state);
        }
    }

    // Heuristic function
    private static int calculateHeuristic(Node node) {
        int numMissionariesLeft = node.state[0];
        int numCannibalsLeft = node.state[1];
        int numMissionariesRight = NUM_MISSIONARIES - numMissionariesLeft;
        int numCannibalsRight = NUM_CANNIBALS - numCannibalsLeft;

        int heuristicCost = (numMissionariesLeft + numCannibalsLeft - 2) / 2;
        if (numMissionariesLeft < numCannibalsLeft || numMissionariesRight < numCannibalsRight) {
            heuristicCost += MAX_PATH_COST;
        }
        return heuristicCost;
    }

    // Successor function
    private static List<Node> generateSuccessors(Node node) {
        List<Node> successors = new ArrayList<>();
        int[] state = node.state.clone();
        int pathCost = node.pathCost + 1;
        int numMissionariesLeft = state[0];
        int numCannibalsLeft = state[1];
        int boatPosition = state[2];
        int numMissionariesRight = NUM_MISSIONARIES - numMissionariesLeft;
        int numCannibalsRight = NUM_CANNIBALS - numCannibalsLeft;

        if (boatPosition == 0) {
            // Move one missionary to right
            if (numMissionariesLeft > 0) {
                state[0] = numMissionariesLeft - 1;
                state[2] = 1;
                state[3] = numMissionariesRight + 1;
                state[4] = numCannibalsRight;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
                state = node.state.clone();
            }
            // Move two missionaries to right
            if (numMissionariesLeft > 1) {
                state[0] = numMissionariesLeft - 2;
                state[2] = 1;
                state[3] = numMissionariesRight + 2;
                state[4] = numCannibalsRight;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
                state = node.state.clone();
            }
            // Move one cannibal to right
            if (numCannibalsLeft > 0) {
                state[1] = numCannibalsLeft - 1;
                state[2] = 1;
                state[3] = numMissionariesRight;
                state[4] = numCannibalsRight + 1;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
                state = node.state.clone();
            }
            // Move one missionary and one cannibal to right
            if (numMissionariesLeft > 0 && numCannibalsLeft > 0) {
                state[0] = numMissionariesLeft - 1;
                state[1] = numCannibalsLeft - 1;
                state[2] = 1;
                state[3] = numMissionariesRight + 1;
                state[4] = numCannibalsRight + 1;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
                state = node.state.clone();
            }
            // Move one cannibal to right
            if (numCannibalsLeft > 1) {
                state[1] = numCannibalsLeft - 2;
                state[2] = 1;
                state[3] = numMissionariesRight;
                state[4] = numCannibalsRight + 2;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
            }
        } else {
            // Move one missionary to left
            if (numMissionariesRight > 0) {
                state[0] = numMissionariesLeft + 1;
                state[2] = 0;
                state[3] = numMissionariesRight - 1;
                state[4] = numCannibalsRight;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
                state = node.state.clone();
            }
            // Move two missionaries to left
            if (numMissionariesRight > 1) {
                state[0] = numMissionariesLeft + 2;
                state[2] = 0;
                state[3] = numMissionariesRight - 2;
                state[4] = numCannibalsRight;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
                state = node.state.clone();
            }
            // Move one cannibal to left
            if (numCannibalsRight > 0) {
                state[1] = numCannibalsLeft + 1;
                state[2] = 0;
                state[3] = numMissionariesRight;
                state[4] = numCannibalsRight - 1;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
                state = node.state.clone();
            }
            // Move one missionary and one cannibal to left
            if (numMissionariesRight > 0 && numCannibalsRight > 0) {
                state[0] = numMissionariesLeft + 1;
                state[1] = numCannibalsLeft + 1;
                state[2] = 0;
                state[3] = numMissionariesRight - 1;
                state[4] = numCannibalsRight - 1;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
                state = node.state.clone();
            }
            // Move one cannibal to left
            if (numCannibalsRight > 1) {
                state[1] = numCannibalsLeft + 2;
                state[2] = 0;
                state[3] = numMissionariesRight;
                state[4] = numCannibalsRight - 2;
                if (isValidState(state)) {
                    successors.add(new Node(state, pathCost, calculateHeuristic(new Node(state, 0, 0, null)), node));
                }
            }
        }
        return successors;
    }

    // Check if a state is valid
    private static boolean isValidState(int[] state) {
        int numMissionariesLeft = state[0];
        int numCannibalsLeft = state[1];
        int boatPosition = state[2];
        int numMissionariesRight = NUM_MISSIONARIES - numMissionariesLeft;
        int numCannibalsRight = NUM_CANNIBALS - numCannibalsLeft;

        if (numMissionariesLeft > NUM_MISSIONARIES || numMissionariesRight > NUM_MISSIONARIES ||
                numCannibalsLeft > NUM_CANNIBALS || numCannibalsRight > NUM_CANNIBALS ||
                numMissionariesLeft < 0 || numMissionariesRight < 0 || numCannibalsLeft < 0 ||
                numCannibalsRight < 0 || (numMissionariesLeft > 0 && numMissionariesLeft < numCannibalsLeft) ||
                (numMissionariesRight > 0 && numMissionariesRight < numCannibalsRight)) {
            return false;
        }
        return true;
    }

    // A* search
    public static List<Node> aStarSearch() {
        Node root = new Node(new int[]{NUM_MISSIONARIES, NUM_CANNIBALS, 0, 0, 0}, 0, 0, null);
        PriorityQueue<Node> frontier = new PriorityQueue<>(MAX_STATE_SPACE, Comparator.comparingInt(o -> o.pathCost + o.heuristicCost));
        Set<Node> explored = new HashSet<>();
        frontier.add(root);

        while (!frontier.isEmpty()) {
            Node node = frontier.poll();
            if (node.isGoalState()) {
                List<Node> path = new ArrayList<>();
                while (node != null) {
                    path.add(node);
                    node = node.parent;
                }
                Collections.reverse(path);
                return path;
            }
            explored.add(node);
            List<Node> successors = generateSuccessors(node);
            for (Node successor : successors) {
                if (!frontier.contains(successor) && !explored.contains(successor)) {
                    frontier.add(successor);
                } else if (frontier.contains(successor)) {
                    Iterator<Node> iterator = frontier.iterator();
                    while (iterator.hasNext()) {
                        Node existingNode = iterator.next();
                        if (existingNode.equals(successor) && existingNode.pathCost > successor.pathCost) {
                            iterator.remove();
                            frontier.add(successor);
                            break;
                        }
                    }
                } else if (explored.contains(successor)) {
                    Iterator<Node> iterator = explored.iterator();
                    while (iterator.hasNext()) {
                        Node existingNode = iterator.next();
                        if (existingNode.equals(successor) && existingNode.pathCost > successor.pathCost) {
                            iterator.remove();
                            frontier.add(successor);
                            break;
                        }
                    }
                }
            }
        }
        return null;
    }

    public static void main(String[] args) {
        List<Node> path = aStarSearch();
        if (path == null) {
            System.out.println("No solution found!");
        } else {
            System.out.println("Optimal solution found with cost " + path.get(path.size() - 1).pathCost);
            for (Node node : path) {
                System.out.println(Arrays.toString(node.state));
            }
        }
    }
}

在这个示例中，我们使用了一个自定义的`Node`类来表示状态空间中的一个节点。每个节点都有一个状态表示、路径成本、启发式成本和一个父节点。我们还定义了一个`calculateHeuristic`方法来计算启发式成本，一个`generateSuccessors`方法来生成一个节点的后继节点，一个`isValidState`方法来检查一个状态是否有效，以及一个`aStarSearch`方法来执行A*搜索。

在主方法中，我们调用`aStarSearch`方法来获取最优解路径，并打印出每个节点的状态表示。如果无法找到解决方案，则打印一条消息表示无法找到解决方案。

传教士与野人过河问题 java a*

传教士与野人过河问题是一个经典的人工智能搜索问题。A*算法是一种启发式搜索算法，可以用来解决这个问题。

首先，我们需要定义问题的状态和操作。在本问题中，状态由河岸上的传教士和野人的数量以及船的位置确定。操作包括：1. 一个人或两个人乘船过河；2. 将船回到原来的河岸。

接下来，我们可以使用A*算法来搜索最优解。A*算法在搜索过程中使用一个估价函数来估计从当前状态到目标状态的距离。在本问题中，我们可以使用下面这个估价函数：

h(n) = (m_left + c_left - 2) / 2 + (m_right + c_right - 2) / 2 + boat

其中m_left和c_left表示左岸上的传教士和野人数量，m_right和c_right表示右岸上的传教士和野人数量，boat表示船的位置（0表示在左岸，1表示在右岸）。这个估价函数的含义是，我们希望尽快将所有的传教士和野人都从左岸移到右岸，因此我们估计的距离就是左岸和右岸上的传教士和野人数量之和减去2，再除以2（因为每次乘船最多只能带2个人）。

然后，我们可以使用A*算法来搜索最优解。具体来说，我们需要定义一个状态类，其中包含当前状态的信息，以及f、g和h三个变量。其中，f表示当前状态的估价函数值，g表示从起始状态到当前状态的距离（即已经乘船的次数），h表示当前状态到目标状态的估计距离。然后，我们需要定义一个优先队列，按照f值从小到大排序，每次取出f值最小的状态进行扩展，并将扩展出的新状态加入队列中。直到找到目标状态或者队列为空为止。

下面是一个简单的Java实现，仅供参考：

import java.util.*;

public class MissionaryAndCannibal {

    static class State {
        int ml, cl, mr, cr, boat, g, h, f;
        State(int ml, int cl, int mr, int cr, int boat, int g, int h) {
            this.ml = ml; this.cl = cl; this.mr = mr; this.cr = cr; this.boat = boat;
            this.g = g; this.h = h; this.f = g + h;
        }
    }

    static int h(int ml, int cl, int mr, int cr, int boat) {
        return (ml + cl - 2) / 2 + (mr + cr - 2) / 2 + boat;
    }

    static boolean isValid(int m, int c) {
        return m >= 0 && c >= 0 && (m == 0 || m >= c);
    }

    static List<State> getSuccessors(State s) {
        List<State> successors = new ArrayList<>();
        int[] deltaM = {1, 2, 0, 0, 1};
        int[] deltaC = {0, 0, 1, 2, 1};
        for (int i = 0; i < 5; i++) {
            int newMl = s.ml - deltaM[i] * s.boat;
            int newCl = s.cl - deltaC[i] * s.boat;
            int newMr = s.mr + deltaM[i] * s.boat;
            int newCr = s.cr + deltaC[i] * s.boat;
            int newBoat = 1 - s.boat;
            int newG = s.g + 1;
            int newH = h(newMl, newCl, newMr, newCr, newBoat);
            if (isValid(newMl, newCl) && isValid(newMr, newCr)) {
                State newState = new State(newMl, newCl, newMr, newCr, newBoat, newG, newH);
                successors.add(newState);
            }
        }
        return successors;
    }

    static boolean isGoal(State s) {
        return s.ml == 0 && s.cl == 0;
    }

    static List<State> aStarSearch(State start) {
        PriorityQueue<State> open = new PriorityQueue<>(Comparator.comparingInt(s -> s.f));
        Set<State> closed = new HashSet<>();
        open.add(start);
        while (!open.isEmpty()) {
            State current = open.poll();
            if (isGoal(current)) {
                List<State> path = new ArrayList<>();
                while (current != null) {
                    path.add(current);
                    current = current.parent;
                }
                Collections.reverse(path);
                return path;
            }
            closed.add(current);
            for (State successor : getSuccessors(current)) {
                if (closed.contains(successor)) continue;
                if (!open.contains(successor)) {
                    successor.parent = current;
                    open.add(successor);
                } else {
                    State existing = open.stream().filter(s -> s.equals(successor)).findFirst().get();
                    if (successor.g < existing.g) {
                        existing.g = successor.g;
                        existing.f = existing.g + existing.h;
                        existing.parent = current;
                    }
                }
            }
        }
        return null;
    }

    public static void main(String[] args) {
        State start = new State(3, 3, 0, 0, 0, 0, h(3, 3, 0, 0, 0));
        List<State> path = aStarSearch(start);
        if (path == null) {
            System.out.println("No solution found.");
        } else {
            for (State state : path) {
                System.out.printf("(%d, %d, %d, %d, %d)\n", state.ml, state.cl, state.mr, state.cr, state.boat);
            }
        }
    }
}