Methods

Question

给定2个矩阵：

public float[] mRi = new float[16];  
public float[] mR = new float[16];

这些将是来自

SensorManager.getRotationMatrix(mR, x, y, z) and
SensorManager.getRotationMatrix(mRi, x, y, z)

所以会有两个4x4矩阵，

我想得到以下方程式的结果：

ResultMtrix=inverse(mRi)*mR

事实上，我知道它是否适用于investm（）和multiplyMM（），但我不知道如何使用矩阵。

你能帮忙吗？

Answer 1

嘿，伙计，我假设它们是4x4矩阵（16个元素）。。。您可以使用高斯-乔丹消去法http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination用于反转。矩阵乘法被描述为无处不在，甚至比矩阵求逆还要多。

Answer 2

您所描述的矩阵实际上是一维向量，因此我假设您所称的inverse实际是transpose。这种情况下的计算非常简单：

Methods

// 1 row * 1 column
public static float scalarMultiplication (float[] m1, float[] m2) {
    if (m1.length != m2.length)
        throw new IllegalArgumentException("Vectors need to have the same length");
    float m = 0;
    for (int i=0; i<m1.length; i++)
        m += (m1[i]*m2[i]);
    return m;
}

// N rows * N columns
public static float[][] vectorMultiplication (float[] m1, float[] m2) {
    if (m1.length != m2.length)
        throw new IllegalArgumentException("Vectors need to have the same length");
    float[][] m = new float[m1.length][m1.length];
    for (int i=0; i<m1.length; i++)
        for (int j=0; j<m1.length; j++)
            m[i][j] = (m1[i]*m2[j]);
    return m;
}

Test

            float[] m1 = new float[16];
            float[] m2 = new float[16];

            for (int i=0; i<m1.length; i++) {
                m1[i]=i;
                m2[i]=i*i;
            }

            System.out.println ("Multiple is " + scalarMultiplication(m1, m2));
            float[][] m = vectorMultiplication(m1, m2);
            for (int i=0; i<m[0].length; i++) {
                for (int j=0; j<m[0].length; j++) {
                    System.out.print (m[i][j] +" ");
                }
                System.out.println();
            }

Output

Multiple is 14400.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
0.0 1.0 4.0 9.0 16.0 25.0 36.0 49.0 64.0 81.0 100.0 121.0 144.0 169.0 196.0 225.0 
0.0 2.0 8.0 18.0 32.0 50.0 72.0 98.0 128.0 162.0 200.0 242.0 288.0 338.0 392.0 450.0 
0.0 3.0 12.0 27.0 48.0 75.0 108.0 147.0 192.0 243.0 300.0 363.0 432.0 507.0 588.0 675.0 
0.0 4.0 16.0 36.0 64.0 100.0 144.0 196.0 256.0 324.0 400.0 484.0 576.0 676.0 784.0 900.0 
0.0 5.0 20.0 45.0 80.0 125.0 180.0 245.0 320.0 405.0 500.0 605.0 720.0 845.0 980.0 1125.0 
0.0 6.0 24.0 54.0 96.0 150.0 216.0 294.0 384.0 486.0 600.0 726.0 864.0 1014.0 1176.0 1350.0 
0.0 7.0 28.0 63.0 112.0 175.0 252.0 343.0 448.0 567.0 700.0 847.0 1008.0 1183.0 1372.0 1575.0 
0.0 8.0 32.0 72.0 128.0 200.0 288.0 392.0 512.0 648.0 800.0 968.0 1152.0 1352.0 1568.0 1800.0 
0.0 9.0 36.0 81.0 144.0 225.0 324.0 441.0 576.0 729.0 900.0 1089.0 1296.0 1521.0 1764.0 2025.0 
0.0 10.0 40.0 90.0 160.0 250.0 360.0 490.0 640.0 810.0 1000.0 1210.0 1440.0 1690.0 1960.0 2250.0 
0.0 11.0 44.0 99.0 176.0 275.0 396.0 539.0 704.0 891.0 1100.0 1331.0 1584.0 1859.0 2156.0 2475.0 
0.0 12.0 48.0 108.0 192.0 300.0 432.0 588.0 768.0 972.0 1200.0 1452.0 1728.0 2028.0 2352.0 2700.0 
0.0 13.0 52.0 117.0 208.0 325.0 468.0 637.0 832.0 1053.0 1300.0 1573.0 1872.0 2197.0 2548.0 2925.0 
0.0 14.0 56.0 126.0 224.0 350.0 504.0 686.0 896.0 1134.0 1400.0 1694.0 2016.0 2366.0 2744.0 3150.0 
0.0 15.0 60.0 135.0 240.0 375.0 540.0 735.0 960.0 1215.0 1500.0 1815.0 2160.0 2535.0 2940.0 3375.0

Methods

Test

Output

友情链接