用于转换double [] []矩阵的紧凑流表达式

Question

我想以double[][]最紧凑和最有效的表达方式转置矩阵。现在我有这个：

public static Function<double[][], double[][]> transpose() {
    return (m) -> {
        final int rows = m.length;
        final int columns = m[0].length;
        double[][] transpose = new double[columns][rows];
        range(0, rows).forEach(r -> {
            range(0, columns).forEach(c -> {
                transpose[c][r] = m[r][c];
            });
        });
        return transpose;
    };
}

有什么想法吗？

Answer 1

你可以有：

public static UnaryOperator<double[][]> transpose() {
    return m -> {
        return range(0, m[0].length).mapToObj(r ->
            range(0, m.length).mapToDouble(c -> m[c][r]).toArray()
        ).toArray(double[][]::new);
    };
}

此代码不使用，forEach而是使用mapToObj并将mapToDouble每行映射到它们的转置。由于返回类型相同，因此我也更改Function<double[][], double[][]>为UnaryOperator<double[][]>。

但是，像assylias的答案中那样具有简单的for循环可能不会更有效。

样例代码：

public static void main(String[] args) {
    double[][] m = { { 2, 3 }, { 1, 2 }, { -1, 1 } };
    double[][] tm = transpose().apply(m);
    System.out.println(Arrays.deepToString(tm)); // prints [[2.0, 1.0, -1.0], [3.0, 2.0, 1.0]]
}

我已经实现了一个JMH基准测试，可以比较上面的代码，for循环版本和上面的代码并行运行。使用大小分别为100、1000和3000的随机平方矩阵调用这三种方法。结果是，对于较小的矩阵，for循环版本更快，但是对于较大的矩阵，并行Stream解决方案的性能确实更好（
Windows 10，JDK 1.8.0_66，i5-3230M @ 2.60 GHz ）：

Benchmark                           (matrixSize)  Mode  Cnt    Score    Error  Units
StreamTest.forLoopTranspose                  100  avgt   30    0,026 ±  0,001  ms/op
StreamTest.forLoopTranspose                 1000  avgt   30   14,653 ±  0,205  ms/op
StreamTest.forLoopTranspose                 3000  avgt   30  222,212 ± 11,449  ms/op
StreamTest.parallelStreamTranspose           100  avgt   30    0,113 ±  0,007  ms/op
StreamTest.parallelStreamTranspose          1000  avgt   30    7,960 ±  0,207  ms/op
StreamTest.parallelStreamTranspose          3000  avgt   30  122,587 ±  7,100  ms/op
StreamTest.streamTranspose                   100  avgt   30    0,040 ±  0,003  ms/op
StreamTest.streamTranspose                  1000  avgt   30   14,059 ±  0,444  ms/op
StreamTest.streamTranspose                  3000  avgt   30  216,741 ±  5,738  ms/op

基准代码：

@Warmup(iterations = 10, time = 500, timeUnit = TimeUnit.MILLISECONDS)
@Measurement(iterations = 10, time = 500, timeUnit = TimeUnit.MILLISECONDS)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MILLISECONDS)
@Fork(3)
public class StreamTest {

    private static final UnaryOperator<double[][]> streamTranspose() {
        return m -> {
            return range(0, m[0].length).mapToObj(r ->
                range(0, m.length).mapToDouble(c -> m[c][r]).toArray()
            ).toArray(double[][]::new);
        };
    }

    private static final UnaryOperator<double[][]> parallelStreamTranspose() {
        return m -> {
            return range(0, m[0].length).parallel().mapToObj(r ->
                range(0, m.length).parallel().mapToDouble(c -> m[c][r]).toArray()
            ).toArray(double[][]::new);
        };
    }

    private static final Function<double[][], double[][]> forLoopTranspose() {
        return m -> {
            final int rows = m.length;
            final int columns = m[0].length;
            double[][] transpose = new double[columns][rows];
            for (int r = 0; r < rows; r++)
                  for (int c = 0; c < columns; c++++)
                    transpose[c][r] = m[r][c];
            return transpose;
        };
    }

    @State(Scope.Benchmark)
    public static class MatrixContainer {

        @Param({ "100", "1000", "3000" })
        private int matrixSize;

        private double[][] matrix;

        @Setup(Level.Iteration)
        public void setUp() {
            ThreadLocalRandom random = ThreadLocalRandom.current();
            matrix = random.doubles(matrixSize).mapToObj(i -> random.doubles(matrixSize).toArray()).toArray(double[][]::new);
        }

    }

    @Benchmark
    public double[][] streamTranspose(MatrixContainer c) {
        return streamTranspose().apply(c.matrix);
    }

    @Benchmark
    public double[][] parallelStreamTranspose(MatrixContainer c) {
        return parallelStreamTranspose().apply(c.matrix);
    }

    @Benchmark
    public double[][] forLoopTranspose(MatrixContainer c) {
        return forLoopTranspose().apply(c.matrix);
    }

}