java - 在java中获取子矩阵的引用

Question

我需要递归地并行处理子矩阵(原始矩阵分为 4 个，传递给一个方法)。矩阵存储为二维数组。我无法每次都将元素复制到新矩阵，因为它非常昂贵。有没有办法在java中引用子矩阵？

也许问题没说清楚，我没有得到答案here .

Answer 1

我会围绕 int[][] 数据编写一个包装器，并将其称为 Matrix 类。然后编写一个方法getSubMatrix(x, y, rows, cols)。这是一个简单的 Matrix 类:

static class Matrix {
    int[][] data;
    int x, y, columns, rows;

    public Matrix(int[][] data) {
        this(data, 0, 0, data.length, data[0].length);
    }

    private Matrix(int[][] data, int x, int y, int columns, int rows) {
        this.data = data;
        this.x = x;
        this.y = y;
        this.columns = columns;
        this.rows = rows;
    }

    public Matrix getSubMatrix(int x, int y, int columns, int rows) {
        return new Matrix(data, this.x + x , this.y + y, columns, rows);
    }

    public String toString() {

        StringBuffer sb = new StringBuffer();

        for (int i = y; i < x + rows; i++) {
            for (int j = x; j < x + columns; j++)
                sb.append(data[i][j]).append(" ");

            sb.append("\n");
        }
        sb.setLength(sb.length() - 1);

        return sb.toString();
    }
}

这个测试程序...:

public static void main(String[] args) throws IOException {

    int[][] testData = new int[10][10];

    for (int i = 0; i < testData.length; i++) 
        for (int j = 0; j < testData[i].length; j++)
            testData[i][j] = 100 + i + j;

    Matrix full = new Matrix(testData);

    System.out.println("Full test matrix:");
    System.out.println(full);

    System.out.println();

    System.out.println("Part of the matrix:");
    System.out.println(full.getSubMatrix(3, 3, 3, 3));

}

...打印:

Full test matrix:
100 101 102 103 104 105 106 107 108 109 
101 102 103 104 105 106 107 108 109 110 
102 103 104 105 106 107 108 109 110 111 
103 104 105 106 107 108 109 110 111 112 
104 105 106 107 108 109 110 111 112 113 
105 106 107 108 109 110 111 112 113 114 
106 107 108 109 110 111 112 113 114 115 
107 108 109 110 111 112 113 114 115 116 
108 109 110 111 112 113 114 115 116 117 
109 110 111 112 113 114 115 116 117 118 

Part of the matrix:
106 107 108 
107 108 109 
108 109 110