Question

我想知道在Java 8中使用流来执行以下操作是否有更简洁的方法：

public static double[] multiply(double[][] matrix, double[] vector) {
    int rows = matrix.length;
    int columns = matrix[0].length;

    double[] result = new double[rows];

    for (int row = 0; row < rows; row++) {
        double sum = 0;
        for (int column = 0; column < columns; column++) {
            sum += matrix[row][column]
                    * vector[column];
        }
        result[row] = sum;
    }
    return result;
}

进行编辑。我收到了一个非常好的答案，但是性能比旧的实现慢了大约10倍，所以我在这里添加测试代码，以防有人想要调查它：

@Test
public void profile() {
    long start;
    long stop;
    int tenmillion = 10000000;
    double[] vector = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };

    double[][] matrix = new double[tenmillion][10];

    for (int i = 0; i < tenmillion; i++) {
        matrix[i] = vector.clone();
    }
    start = System.currentTimeMillis();
    multiply(matrix, vector);
    stop = System.currentTimeMillis();
 }

Answer 1

使用Stream的直接方式如下：

public static double[] multiply(double[][] matrix, double[] vector) {
    return Arrays.stream(matrix)
                 .mapToDouble(row -> 
                    IntStream.range(0, row.length)
                             .mapToDouble(col -> row[col] * vector[col])
                             .sum()
                 ).toArray();
}

这将为矩阵的每一行（Stream<double[]>）创建一个Stream，然后将每一行映射到使用vector数组计算产品的double值。

我们必须在索引上使用Stream来计算产品，因为很遗憾没有可以将两个Streams压缩在一起的内置工具。

Answer 2

衡量绩效的方式对于衡量绩效并不是非常可靠，手动编写微基准通常不是一个好主意。例如，在编译代码时，JVM可能会选择更改执行顺序，并且可能无法将启动和停止变量分配到您希望分配的位置，从而在测量中产生意外结果。预热JVM以及让JIT编译器进行所有优化也非常重要。 GC还可以在引入应用程序吞吐量和响应时间的变化方面发挥重要作用。我强烈建议使用JMH和Caliper等专用工具进行微基准测试。

我还用JVM预热，随机数据集和更多迭代次数编写了一些基准测试代码。事实证明，Java 8流提供了更好的结果。

/**
 *
 */
public class MatrixMultiplicationBenchmark {
    private static AtomicLong start = new AtomicLong();
    private static AtomicLong stop = new AtomicLong();
    private static Random random = new Random();

    /**
     * Main method that warms-up each implementation and then runs the benchmark.
     *
     * @param args main class args
     */
    public static void main(String[] args) {
        // Warming up with more iterations and smaller data set
        System.out.println("Warming up...");
        IntStream.range(0, 10_000_000).forEach(i -> run(10, MatrixMultiplicationBenchmark::multiplyWithStreams));
        IntStream.range(0, 10_000_000).forEach(i -> run(10, MatrixMultiplicationBenchmark::multiplyWithForLoops));

        // Running with less iterations and larger data set
        startWatch("Running MatrixMultiplicationBenchmark::multiplyWithForLoops...");
        IntStream.range(0, 10).forEach(i -> run(10_000_000, MatrixMultiplicationBenchmark::multiplyWithForLoops));
        endWatch("MatrixMultiplicationBenchmark::multiplyWithForLoops");

        startWatch("Running MatrixMultiplicationBenchmark::multiplyWithStreams...");
        IntStream.range(0, 10).forEach(i -> run(10_000_000, MatrixMultiplicationBenchmark::multiplyWithStreams));
        endWatch("MatrixMultiplicationBenchmark::multiplyWithStreams");
    }

    /**
     * Creates the random matrix and vector and applies them in the given implementation as BiFunction object.
     *
     * @param multiplyImpl implementation to use.
     */
    public static void run(int size, BiFunction<double[][], double[], double[]> multiplyImpl) {
        // creating random matrix and vector
        double[][] matrix = new double[size][10];
        double[] vector = random.doubles(10, 0.0, 10.0).toArray();
        IntStream.range(0, size).forEach(i -> matrix[i] = random.doubles(10, 0.0, 10.0).toArray());

        // applying matrix and vector to the given implementation. Returned value should not be ignored in test cases.
        double[] result = multiplyImpl.apply(matrix, vector);
    }

    /**
     * Multiplies the given vector and matrix using Java 8 streams.
     *
     * @param matrix the matrix
     * @param vector the vector to multiply
     *
     * @return result after multiplication.
     */
    public static double[] multiplyWithStreams(final double[][] matrix, final double[] vector) {
        final int rows = matrix.length;
        final int columns = matrix[0].length;

        return IntStream.range(0, rows)
                .mapToDouble(row -> IntStream.range(0, columns)
                        .mapToDouble(col -> matrix[row][col] * vector[col])
                        .sum()).toArray();
    }

    /**
     * Multiplies the given vector and matrix using vanilla for loops.
     *
     * @param matrix the matrix
     * @param vector the vector to multiply
     *
     * @return result after multiplication.
     */
    public static double[] multiplyWithForLoops(double[][] matrix, double[] vector) {
        int rows = matrix.length;
        int columns = matrix[0].length;

        double[] result = new double[rows];

        for (int row = 0; row < rows; row++) {
            double sum = 0;
            for (int column = 0; column < columns; column++) {
                sum += matrix[row][column] * vector[column];
            }
            result[row] = sum;
        }
        return result;
    }

    private static void startWatch(String label) {
        System.out.println(label);
        start.set(System.currentTimeMillis());
    }

    private static void endWatch(String label) {
        stop.set(System.currentTimeMillis());
        System.out.println(label + " took " + ((stop.longValue() - start.longValue()) / 1000) + "s");
    }
}

这是输出

Warming up...
Running MatrixMultiplicationBenchmark::multiplyWithForLoops...
MatrixMultiplicationBenchmark::multiplyWithForLoops took 100s
Running MatrixMultiplicationBenchmark::multiplyWithStreams...
MatrixMultiplicationBenchmark::multiplyWithStreams took 89s

Java 8矩阵*向量乘法

2 个答案: