public+class+Math+{ public+static+final+int+data+=+999+; public

public class Math {
public static final int data = 999;

public static int add(int a, int b) {
return a + b;
}

public static int subtract(int a, int b) {
return a - b;
}

public static int multiply(int a, int b) {
return a * b;
}

public static double divide(int a, int b) {
return (double) a / b;
}
}

相关问题

java+数据结构课程设计

Java + 数据结构课程设计可以包含以下内容：

1. 数据结构的基本操作：包括栈、队列、链表、树、图等数据结构的基本操作，如插入、删除、查找等。

2. 数据压缩与解压缩：可以使用哈夫曼编码等算法实现数据的压缩和解压缩。

3. 24点扑克牌游戏：可以使用栈和递归等数据结构和算法实现24点扑克牌游戏的计算和判断。

4. 16枚硬币的反面问题：可以使用递归和分治等算法实现16枚硬币的反面问题的求解。

5. 线性表、树、图的操作和演示：可以实现线性表、树、图等数据结构的基本操作，并通过图形化界面演示它们的操作过程。

6. 农夫过河：可以使用广度优先搜索等算法实现农夫过河问题的求解。

7. 迷宫问题：可以使用深度优先搜索等算法实现迷宫问题的求解。

以下是一个Java + 数据结构课程设计的例子：

设计一个简单的图形化界面程序，实现以下功能：

1. 实现一个栈和队列的基本操作，包括入栈、出栈、入队、出队等。

2. 实现一个哈夫曼编码的压缩和解压缩功能。

3. 实现24点扑克牌游戏的计算和判断功能。

4. 实现16枚硬币的反面问题的求解功能。

5. 实现线性表、树、图等数据结构的基本操作，并通过图形化界面演示它们的操作过程。

6. 实现农夫过河问题和迷宫问题的求解功能。

// 栈的实现
class Stack {
    private int[] data;
    private int top;

    public Stack(int size) {
        data = new int[size];
        top = -1;
    }

    public void push(int value) {
        if (top == data.length - 1) {
            System.out.println("Stack is full!");
            return;
        }
        data[++top] = value;
    }

    public int pop() {
        if (top == -1) {
            System.out.println("Stack is empty!");
            return -1;
        }
        return data[top--];
    }

    public boolean isEmpty() {
        return top == -1;
    }
}

// 队列的实现
class Queue {
    private int[] data;
    private int front;
    private int rear;

    public Queue(int size) {
        data = new int[size];
        front = rear = -1;
    }

    public void enqueue(int value) {
        if (rear == data.length - 1) {
            System.out.println("Queue is full!");
            return;
        }
        data[++rear] = value;
    }

    public int dequeue() {
        if (front == rear) {
            System.out.println("Queue is empty!");
            return -1;
        }
        return data[++front];
    }

    public boolean isEmpty() {
        return front == rear;
    }
}

// 哈夫曼编码的实现
class Huffman {
    private static class Node implements Comparable<Node> {
        int value;
        Node left;
        Node right;

        public Node(int value) {
            this.value = value;
        }

        public Node(int value, Node left, Node right) {
            this.value = value;
            this.left = left;
            this.right = right;
        }

        public boolean isLeaf() {
            return left == null && right == null;
        }

        @Override
        public int compareTo(Node o) {
            return value - o.value;
        }
    }

    public static void compress(String input, String output) throws IOException {
        // 统计字符出现的次数
        int[] freq = new int[256];
        for (int i = 0; i < input.length(); i++) {
            freq[input.charAt(i)]++;
        }

        // 构建哈夫曼树
        PriorityQueue<Node> pq = new PriorityQueue<>();
        for (int i = 0; i < freq.length; i++) {
            if (freq[i] > 0) {
                pq.offer(new Node(freq[i], null, null));
            }
        }
        while (pq.size() > 1) {
            Node left = pq.poll();
            Node right = pq.poll();
            pq.offer(new Node(left.value + right.value, left, right));
        }
        Node root = pq.poll();

        // 生成哈夫曼编码表
        String[] codes = new String[256];        generateCodes(root, "", codes);

        // 写入压缩文件
        try (BitOutputStream out = new BitOutputStream(new FileOutputStream(output))) {
            // 写入字符出现的次数
            for (int i = 0; i < freq.length; i++) {
                out.writeInt(freq[i]);
            }

            // 写入压缩后的数据
            for (int i = 0; i < input.length(); i++) {
                String code = codes[input.charAt(i)];
                for (int j = 0; j < code.length(); j++) {
                    out.writeBit(code.charAt(j) - '0');
                }
            }
        }
    }

    public static void decompress(String input, String output) throws IOException {
        // 读取字符出现的次数
        int[] freq = new int[256];
        try (BitInputStream in = new BitInputStream(new FileInputStream(input))) {
            for (int i = 0; i < freq.length; i++) {
                freq[i] = in.readInt();
            }

            // 构建哈夫曼树
            PriorityQueue<Node> pq = new PriorityQueue<>();
            for (int i = 0; i < freq.length; i++) {
                if (freq[i] > 0) {
                    pq.offer(new Node(freq[i], null, null));
                }
            }
            while (pq.size() > 1) {
                Node left = pq.poll();
                Node right = pq.poll();
                pq.offer(new Node(left.value + right.value, left, right));
            }
            Node root = pq.poll();

            // 解压缩数据
            try (BitOutputStream out = new BitOutputStream(new FileOutputStream(output))) {
                Node node = root;
                while (true) {
                    int bit = in.readBit();
                    if (bit == -1) {
                        break;
                    }
                    if (bit == 0) {
                        node = node.left;
                    } else {
                        node = node.right;
                    }
                    if (node.isLeaf()) {
                        out.write(node.value);
                        node = root;
                    }
                }
            }
        }
    }

    private static void generateCodes(Node node, String code, String[] codes) {
        if (node.isLeaf()) {
            codes[node.value] = code;
            return;
        }
        generateCodes(node.left, code + "0", codes);
        generateCodes(node.right, code + "1", codes);
    }
}

// 24点扑克牌游戏的实现
class Poker {
    private static final int TARGET = 24;
    private static final double EPSILON = 1e-6;

    public static boolean is24(double[] nums) {
        if (nums.length == 1) {
            return Math.abs(nums[0] - TARGET) < EPSILON;
        }
        for (int i = 0; i < nums.length; i++) {
            for (int j = i + 1; j < nums.length; j++) {
                double[] next = new double[nums.length - 1];
                for (int k = 0, l = 0; k < nums.length; k++) {
                    if (k != i && k != j) {
                        next[l++] = nums[k];
                    }
                }
                next[next.length - 1] = nums[i] + nums[j];
                if (is24(next)) {
                    return true;
                }
                next[next.length - 1] = nums[i] - nums[j];
                if (is24(next)) {
                    return true;
                }
                next[next.length - 1] = nums[j] - nums[i];
                if (is24(next)) {
                    return true;
                }
                next[next.length - 1] = nums[i] * nums[j];
                if (is24(next)) {
                    return true;
                }
                if (nums[j] != 0) {
                    next[next.length - 1] = nums[i] / nums[j];
                    if (is24(next)) {
                        return true;
                    }
                }
                if (nums[i] != 0) {
                    next[next.length - 1] = nums[j] / nums[i];
                    if (is24(next)) {
                        return true;
                    }
                }
            }
        }
        return false;
    }
}

// 16枚硬币的反面问题的实现
class Coins {
    private static final int N = 16;
    private static final int[] COINS = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};

    public static void findSolution() {
        int[] state = new int[N];
        for (int i = 0; i < N; i++) {
            state[i] = 1;
        }
        int count = 0;
        while (true) {
            count++;
            int sum = 0;
            for (int i = 0; i < N; i++) {
                if (state[i] == -1) {
                    sum += COINS[i];
                }
            }
            if (sum == 24) {
                System.out.print("Solution " + count + ": ");
                for (int i = 0; i < N; i++) {
                    System.out.print(state[i] == -1 ? "H" : "T");
                }
                System.out.println();
            }
            int i = N - 1;
            while (i >= 0 && state[i] == 1) {
                state[i] = -1;
                i--;
            }
            if (i < 0) {
                break;
            }
            state[i] = 1;
        }
    }
}

// 农夫过河问题的实现
class Farmer {
    private static final int MAX_WEIGHT = 10;

    public static void findSolution() {
        Queue queue = new Queue(100);
        queue.enqueue(new State(0, 0, 0, 0));
        while (!queue.isEmpty()) {
            State state = queue.dequeue();
            if (state.isFinalState()) {
                System.out.println(state);
                break;
            }
            for (State next : state.getNextStates()) {
                if (next.isValidState()) {
                    queue.enqueue(next);
                }
            }
        }
    }

    private static class State {
        int farmer;
        int wolf;
        int goat;
        int cabbage;

        public State(int farmer, int wolf, int goat, int cabbage) {
            this.farmer = farmer;
            this.wolf = wolf;
            this.goat = goat;
            this.cabbage = cabbage;
        }

        public boolean isFinalState() {
            return farmer == 1 && wolf == 1 && goat == 1 && cabbage == 1;
        }

        public boolean isValidState() {
            if (wolf == goat && farmer != wolf) {
                return false;
            }
            if (goat == cabbage && farmer != goat) {
                return false;
            }
            return true;
        }

        public List<State> getNextStates() {
            List<State> nextStates = new ArrayList<>();
            if (farmer == 0) {
                nextStates.add(new State(1, wolf, goat, cabbage));
            } else {
                nextStates.add(new State(0, wolf, goat, cabbage));
            }
            if (farmer == wolf) {
                if (farmer == 0) {
                    nextStates.add(new State(1, 1, goat, cabbage));
                } else {
                    nextStates.add(new State(0, 0, goat, cabbage));
                }
            }
            if (farmer == goat) {
                if (farmer == 0) {
                    nextStates.add(new State(1, wolf, 1, cabbage));
                } else {
                    nextStates.add(new State(0, wolf, 0, cabbage));
                }
            }
            if (farmer == cabbage) {
                if (farmer == 0) {
                    nextStates.add(new State(1, wolf, goat, 1));
                } else {
                    nextStates.add(new State(0, wolf, goat, 0));
                }
            }
            return nextStates;
        }

        @Override
        public String toString() {
            return "State{" +
                    "farmer=" + farmer +
                    ", wolf=" + wolf +
                    ", goat=" + goat +
                    ", cabbage=" + cabbage +
                    '}';
        }
    }
}

// 迷宫问题的实现
class Maze {
    private static final int[][] DIRECTIONS = {{-1, 0}, {0, -1}, {1, 0}, {0, 1}};

    public static void findSolution(int[][] maze, int startX, int startY, int endX, int endY) {
        Queue queue = new Queue(100);
        queue.enqueue(new Point(startX, startY));
        while (!queue.isEmpty()) {
            Point point = queue.dequeue();
            if (point.x == endX && point.y == endY) {
                System.out.println("Solution found!");
                return;
            }
            for (int[] dir : DIRECTIONS) {
                int x = point.x + dir[0];
                int y = point.y + dir[1];
                if (x >= 0 && x < maze.length && y >= 0 && y < maze[0].length

em算法Java代码，MapReduce实现EM算法步骤+代码

EM算法是一种迭代算法，用于在含有隐变量的概率模型中进行参数估计。以下是EM算法的Java代码实现：

public class EMAlgorithm {

    // E-step：计算隐变量的后验概率
    public double[][] getPosterior(double[][] data, double[] weights, double[] means, double[] variances) {
        int numPoints = data.length;
        int numClusters = means.length;

        double[][] posterior = new double[numPoints][numClusters];
        for (int i = 0; i < numPoints; i++) {
            double sum = 0.0;
            for (int j = 0; j < numClusters; j++) {
                posterior[i][j] = weights[j] * Gaussian(data[i], means[j], variances[j]);
                sum += posterior[i][j];
            }
            for (int j = 0; j < numClusters; j++) {
                posterior[i][j] /= sum;
            }
        }
        return posterior;
    }

    // M-step：计算新的参数
    public double[] getWeights(double[][] posterior) {
        int numPoints = posterior.length;
        int numClusters = posterior[0].length;

        double[] weights = new double[numClusters];
        for (int j = 0; j < numClusters; j++) {
            double sum = 0.0;
            for (int i = 0; i < numPoints; i++) {
                sum += posterior[i][j];
            }
            weights[j] = sum / numPoints;
        }
        return weights;
    }

    public double[] getMeans(double[][] data, double[][] posterior) {
        int numClusters = posterior[0].length;
        int numDimensions = data[0].length;
        double[] means = new double[numClusters];

        for (int j = 0; j < numClusters; j++) {
            double sum = 0.0;
            double totalWeight = 0.0;
            for (int i = 0; i < data.length; i++) {
                sum += posterior[i][j] * data[i][j];
                totalWeight += posterior[i][j];
            }
            means[j] = sum / totalWeight;
        }
        return means;
    }

    public double[] getVariances(double[][] data, double[][] posterior, double[] means) {
        int numClusters = posterior[0].length;
        int numDimensions = data[0].length;
        double[] variances = new double[numClusters];

        for (int j = 0; j < numClusters; j++) {
            double sum = 0.0;
            double totalWeight = 0.0;
            for (int i = 0; i < data.length; i++) {
                sum += posterior[i][j] * Math.pow(data[i][j] - means[j], 2);
                totalWeight += posterior[i][j];
            }
            variances[j] = sum / totalWeight;
        }
        return variances;
    }

    private double Gaussian(double[] dataPoint, double mean, double variance) {
        double stdDev = Math.sqrt(variance);
        return (1.0 / (stdDev * Math.sqrt(2 * Math.PI))) * Math.exp(-Math.pow(dataPoint[0] - mean, 2) / (2 * variance));
    }
}

下面是MapReduce实现EM算法的步骤：

1. Map阶段：对每个数据点，计算它对每个聚类中心的后验概率，输出键值对\<聚类中心, 后验概率\>；
2. Reduce阶段：对每个聚类中心，计算它的新的权重、均值和方差，并输出键值对\<聚类中心, 参数\>；
3. 迭代以上步骤，直到收敛为止。

以下是MapReduce实现EM算法的Java代码：

public class KMeansMR {

    public static class Map extends Mapper<LongWritable, Text, IntWritable, DoubleWritable> {

        private final static IntWritable cluster = new IntWritable();
        private final static DoubleWritable posterior = new DoubleWritable();

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 读取数据点
            double[] dataPoint = parseDataPoint(value.toString());

            // 计算数据点对每个聚类中心的后验概率
            for (int i = 0; i < numClusters; i++) {
                double posterior = weights[i] * Gaussian(dataPoint, means[i], variances[i]);
                cluster.set(i);
                posterior.set(posterior);
                context.write(cluster, posterior);
            }
        }
    }

    public static class Reduce extends Reducer<IntWritable, DoubleWritable, IntWritable, Text> {

        private final static DecimalFormat df = new DecimalFormat("#.####");

        public void reduce(IntWritable key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException {
            double sum = 0.0;
            int count = 0;
            for (DoubleWritable value : values) {
                sum += value.get();
                count++;
            }

            // 计算新的权重、均值和方差
            double newWeight = sum / numPoints;
            double[] newMeans = getNewMeans(key.get());
            double[] newVariances = getNewVariances(key.get(), newMeans);

            // 输出键值对<聚类中心, 参数>
            Text outputValue = new Text(df.format(newWeight) + "," + Arrays.toString(newMeans) + "," + Arrays.toString(newVariances));
            context.write(key, outputValue);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "KMeansMR");
        job.setJarByClass(KMeansMR.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(DoubleWritable.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}