答案 0 :(得分:1)
所以我只是为了好玩,这是我的代码。它适用于任何大小的矩阵。
public class Matrixer
{
final double[][] matrix, computedMatrix;
final int rows, cols;
public Matrixer(int N, int M, final double[][] imatrix)
{
rows = N;
cols = M;
matrix = imatrix;
computedMatrix = new double[N][M];
}
public void computeAverages()
{
for (int i = 1; i < rows - 1; i++)
{
for (int j = 1; j < cols - 1; j++)
{
computedMatrix[i][j] = cellNeighborsAverage(i, j);
}
}
}
private double cellNeighborsAverage(int row, int col)
{
// Ignore center cell
double sum = matrix[row - 1][col - 1] + matrix[row - 1][col]
+ matrix[row - 1][col + 1] + matrix[row][col - 1]
+ matrix[row][col + 1] + matrix[row + 1][col - 1]
+ matrix[row + 1][col] + matrix[row + 1][col + 1];
return sum / 8;
}
public void printComputedMatrix()
{
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
System.out.printf("%.2f", computedMatrix[i][j]);
System.out.print(", ");
}
System.out.println();
}
}
public static void main(String[] args)
{
final double[][] matrix =
{
{1, 2, 3, 4, 5},
{5, 4, 3, 5, 1},
{3, 2, 2, 3, 4},
{2, 3, 4, 5, 3},
{3, 2, 4, 5, 6},
};
Matrixer mx = new Matrixer(5, 5, matrix);
mx.computeAverages();
mx.printComputedMatrix();
}
}
测试输出:
0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 2.63, 3.13, 3.13, 0.00,
0.00, 3.25, 3.63, 3.38, 0.00,
0.00, 2.75, 3.25, 3.88, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00
答案 1 :(得分:0)
伪代码,因为这不是特定于Java的问题:
var myMatrix = [
[ x, x, x, x, x],
[ x, x, x, x, x],
[ x, x, x, x, x],
[ x, x, x, x, x],
[ x, x, x, x, x] ];
function neighborAverage(row, col) {
return (( myMatrix[row-1][col-1] +
myMatrix[row-1][col] +
myMatrix[row-1][col+1] +
myMatrix[row][col-1] +
// skip the center element
myMatrix[row][col+1] +
myMatrix[row+1][col-1] +
myMatrix[row+1][col] +
myMatrix[row+1][col+1]) / 8));
}
function matrixAverage() {
return ([
[ neighborAverage(1, 1), neighborAverage(1, 2), neighborAverage(1, 3) ],
[ neighborAverage(2, 1), neighborAverage(2, 2), neighborAverage(2, 3) ],
[ neighborAverage(3, 1), neighborAverage(3, 2), neighborAverage(3, 3) ] ]);
}
两个注释:
matrixAverage()
中的硬编码索引表示此伪代码仅适用于大小为5x5的矩阵。但是,将这个函数推广到任何大小的矩阵都不应该太难。
此伪代码计算所有平均值,但不会覆盖原始矩阵中的任何值。这可能是你想要的,也可能不是你想要的,因为你没有指定是否应该使用每个新值来计算后续值(如果是,那么应该以什么顺序计算值)。