Java矩阵运算包ujmp的基本使用

本人最近在用ujmp包写一些程序，ujmp包是针对于超大数据量计算的矩阵的运算包，并且有图形显示的功能且支持多种文件格式的读取和输出，还支持连接数据库，matlab数据类型和weka数据类型，总体来说非常好用，但是有一个很大的缺陷就是基本没有相关的示例和文档，官网上的示例有基本全都过时不能用了，本人总结了一下相关用法，仅供大家参考，代码并不能运行，知识给大家列出了相应的矩阵运算方式和构造方式，希望能对大家有所帮助。

基本用法如下：

@Test
    public void testUJMP() {

        //初始化一个4X4的矩阵
        Matrix dense = DenseMatrix.Factory.zeros(4, 4);
        Matrix dense2 = DenseMatrix.Factory.zeros(4, 4);
        //输出矩阵的行和列的长度
        System.out.println("dense rowcount colcount " + dense.getRowCount() + "  " + dense.getColumnCount());;
        //利用行和列进行矩阵的赋值
        for (int i = 0; i < dense.getRowCount(); ++i){
            for (int j = 0 ; j < dense.getColumnCount(); ++j){
                //可以使用setXXX来进行矩阵的赋值，其中第一个参数是值，第二个参数是行，第三个参数是列
                dense.setAsInt((i*j + (int)(Math.pow(i + 1, j))), i, j);
                dense2.setAsInt(i + j, i , j);
            }
        }
        Math.pow(1,2);
        //输出矩阵
        System.out.println(dense);
        System.out.println("dense2 \n" + dense2);

        //初始化一个稀疏矩阵
        Matrix spares = SparseMatrix.Factory.zeros(400,500);
        //这里使用另一种方法获取其行和列
        // long[] getSize()  是一个维度为2的矩阵，第一个是行，第二个数是列
        for (int i = 0; i < spares.getSize()[0]; ++i){
            for (int j = 0; j< spares.getSize()[1]; ++j){
                spares.setAsBigDecimal(BigDecimal.valueOf(i *j), i, j);
            }
        }
        System.out.println(spares.getSize()[0] + "   " + spares.getSize()[1]);
        //System.out.println("spares Matrix : \n" + spares);

        /*****************************************
         *      矩阵的运算
         *****************************************/

        //转置
        Matrix transpose = dense.transpose();
        System.out.println(transpose);
        //两个矩阵求和

        Matrix sum = dense.plus(dense2);
        System.out.println("sum \n" + sum);

        //两个矩阵相减
        Matrix difference = dense.minus(dense2);
        System.out.println("difference \n" + difference);

        //矩阵相乘
        Matrix matrixProduct = dense.mtimes(dense2);
        System.out.println("matrixProduct\n" + matrixProduct);

        //矩阵 k*M (K 为常数, M为矩阵)
        Matrix scaled = dense.times(2.0);
        System.out.println("scaled \n" + scaled);

        //矩阵的逆
        Matrix inverse = dense.inv();
        System.out.println(inverse);

        //伪逆矩阵 广义逆矩阵
        Matrix pesudoInv = dense.pinv();
        System.out.println(pesudoInv);

        //求矩阵的行列式
        double determiant = dense.det();
        System.out.println("determiant = " + determiant);

        //矩阵的奇异值分解
        Matrix[] sigularValueDecompostion = dense.svd();
        for (int i = 0; i < sigularValueDecompostion.length; ++i){
            System.out.println("sigularValueDecompostion " + i + "= \n" + sigularValueDecompostion[i]);
        }

        //求矩阵的特征值
        Matrix[] eigenValueDecompostion = dense.eig();
        for (int i = 0; i < eigenValueDecompostion.length; ++i){
            System.out.println("eigenValueDecompostion " + i + "= \n" + eigenValueDecompostion[i]);
        }

        //矩阵的LU分解，将矩阵分解成一个上三角矩阵和下三角矩阵的乘积
        Matrix[] luValueDecompostion = dense.lu();
        for (int i = 0; i < luValueDecompostion.length; ++i){
            System.out.println("luValueDecompostion " + i + "= \n" + luValueDecompostion[i]);
        }

        //qr分解  半正交矩阵与一个上三角矩阵的积，常用来求解线性最小二乘问题
        Matrix[] qrDecomposition = dense.qr();
        for (int i = 0; i < qrDecomposition.length; ++i){
            System.out.println("qrDecomposition " + i + "= \n" + qrDecomposition[i]);
        }


        //Cholesky分解 对于每一个正定矩阵 Cholesky分解都存在
        Matrix choleskyDecomposition = dense.chol();
        System.out.println("choleskyDecomposition \n" + choleskyDecomposition);

    }

当然还有一些高级操作如下：

        long m = 5;
        long n = 5;
        /**
         * 制造一个空矩阵
         */
        Matrix emptyMatrix = MatrixFactory.emptyMatrix();
        /**
         * 制造一个m*n随机矩阵
         */
        Matrix randMatrix = Matrix.factory.rand(m, n);
        /**
         * 制造一个m*n零矩阵
         */
        Matrix zeroMatrix = Matrix.factory.zeros(m, n);
        /**
         * 制造一个m*n对角线为1其余元素为0的矩阵
         */
        Matrix eyeMatrix = Matrix.factory.eye(m, n);
        /**
         * 制造一个m*n全部元素为1的矩阵
         */
        Matrix oneMatrix = Matrix.factory.ones(m, n);
        /**
         * 矩阵的相关操作
         */
        // 矩阵与数值的相关运算，意思大家根据英语的含义就能看出，这里就不解释了
        Matrix res_1 = oneMatrix.times(10);
        Matrix res_2 = oneMatrix.divide(10);
        Matrix res_3 = oneMatrix.plus(10);
        Matrix res_4 = oneMatrix.minus(10);
        /**
         * 矩阵与矩阵的相关运算 加和减函数都不用变,乘的话要加上m表示matrix间计算
         */
        Matrix res_5 = oneMatrix.mtimes(randMatrix);
        Matrix res_7 = oneMatrix.plus(randMatrix);
        Matrix res_8 = oneMatrix.minus(randMatrix);
        /**
         * 求转置求逆,这里有三种返回型，分别是link orig new 计算时间new > orig > link 无返回型和orig的时间类似
         */
        Matrix res_9 = oneMatrix.transpose(Ret.LINK);
        Matrix res_10 = oneMatrix.transpose(Ret.ORIG);
        Matrix res_11 = oneMatrix.transpose(Ret.NEW);
        Matrix res_12 = oneMatrix.inv();
        // 选取子矩阵
        Matrix res_13 = oneMatrix.subMatrix(Ret.NEW, startRow, startColumn,
                endRow, endColumn);
        // 选取行
        Matrix res_14 = oneMatrix.selectRows(returnType, rows);
        // 选取列
        Matrix res_15 = oneMatrix.selectColumns(returnType, columns);
        // 按第i列进行排序，reverse表示返回的排序矩阵是按正序还是逆序
        Matrix res_16 = oneMatrix.sortrows(returnType, column, reverse);
        // 将矩阵的所有数值相加得到的返回值
        Matrix res_17 = oneMatrix.getValueSum();
        // 选去矩阵的行和列
        Matrix res_18 = oneMatrix.getColumnCount();
        Matrix res_19 = oneMatrix.getRowCount();
        //判断矩阵否和一个矩阵或一个值相等，相等的话在相应的位置设置为为true否则为false,
        //如果要看相等的个数的总和则可再继续用一个getvaluecount函数即可
        Matrix res_20 = oneMatrix.eq(returnType, matrix);
        matrix res_21 = oneMatrix.eq(returnType, value)
        当矩阵返回类型为RET.ORIG的时候不能使用任何有可能改变矩阵大小的操作(除非自己知道确实不会改变)，例如转置、选取行列、子矩阵等~~~~~

最后做一点补充

package MatrixPFTest.yi.maytwenty;

import org.ujmp.core.Matrix;
import org.ujmp.core.MatrixFactory;
import org.ujmp.core.calculation.Calculation.Ret;

public class PerfomaceTest {
    public static void main(String[] args) {
        long begin, end;
        /**
         * test变test2才变 *********test2不能被改变
         */

        long m = 725, n = 20;
        // Matrix test_1 = Matrix.factory.rand(5, 5);
        // test_1.showGUI();
        // Matrix test_2 = test_1.transpose(Ret.ORIG);
        // test_2.showGUI();
        // Matrix test_3 = test_2.mtimes(Matrix.factory.ones(5, 5).times(2));
        // test_3.showGUI();
        begin = System.currentTimeMillis();
        Matrix res = Matrix.factory.rand(m, n);
        Matrix res0 = Matrix.factory.rand(m, n);
        end = System.currentTimeMillis();
        Constans.sop("构建矩阵耗时" + (end - begin) + "ms");
        // res.setLabel("res");
        // res.showGUI();

        begin = System.currentTimeMillis();
        Matrix res_1_trannull = res.transpose();
        end = System.currentTimeMillis();
        Constans.sop("res_1_trannull-耗时" + (end - begin) + "ms");

        begin = System.currentTimeMillis();
        Matrix res_2_tranlink = res.transpose(Ret.LINK);
        end = System.currentTimeMillis();
        Constans.sop("res_2_tranlink-耗时" + (end - begin) + "ms");
        // res_2_tranlink.setLabel("res_2_tranlink");
        // res_2_tranlink.setAsDouble(10, 0, 0);
        // res_2_tranlink.showGUI();

        /**
         * 进行矩阵赋值，两个矩阵式同一个矩阵，除非用copy()
         */
        Matrix xxxMatrix = res_2_tranlink;
        xxxMatrix.setAsDouble(10, 0, 0);
        xxxMatrix.showGUI();
        /**
         * 对LINK的矩阵进行赋值
         */
        res_2_tranlink = MatrixFactory.ones(1, 1);
        res_2_tranlink.setAsDouble(110, 0, 0);
        res_2_tranlink.showGUI();

        /**
         * 选取特定行与列
         */
        begin = System.currentTimeMillis();
        Matrix res_3 = res_2_tranlink.selectColumns(Ret.NEW, 10);
        end = System.currentTimeMillis();
        res_3.showGUI();
        Constans.sop("选取列-NEW-耗时" + (end - begin) + "ms");

        begin = System.currentTimeMillis();
        Matrix res_4 = res_2_tranlink.selectColumns(Ret.LINK, 0);
        end = System.currentTimeMillis();
        res_4.setAsDouble(10, 0, 0);
        res_4.showGUI();
        Constans.sop("选取列-link-耗时" + (end - begin) + "ms");

        /**
         * 求逆耗时较长，但是inv和invSymm相差无几
         */
        for (int i = 0; i < 1; ++i) {
            begin = System.currentTimeMillis();
            Matrix res_5 = res_2_tranlink.inv();
            end = System.currentTimeMillis();
            Constans.sop("inv-耗时" + (end - begin) + "ms");
        }

        /**
         * 获取行数，列数
         */
        begin = System.currentTimeMillis();
        long res_rowcount = res_2_tranlink.getRowCount();
        end = System.currentTimeMillis();
        Constans.sop("getRowCount-耗时" + (end - begin) + "ms");

        /**
         * 矩阵相乘的检测
         */

        begin = System.currentTimeMillis();
        Matrix res_muti_link = res_2_tranlink.mtimes(Ret.LINK, false, res0);
        end = System.currentTimeMillis();
        res_muti_link.setAsDouble(100, 0, 0);
        // res_muti_link.showGUI();
        Constans.sop("res_muti_link-耗时" + (end - begin) + "ms");

        // 这里是LINK后和LINK后的矩阵相乘，但是返回的是NEW，所以可以改变值
        Matrix afterlinklink = res_muti_link.mtimes(res_2_tranlink);
        afterlinklink.setAsDouble(100, 0, 0);
        afterlinklink.showGUI();
        begin = System.currentTimeMillis();
        Matrix res_muti_new = res_2_tranlink.mtimes(Ret.NEW, false, res0);
        end = System.currentTimeMillis();
        res_muti_new.showGUI();
        Constans.sop("res_muti_new-耗时" + (end - begin) + "ms");

        /**
         * 对不是LINK的矩阵选取行或列再改变变量值，使用LINK的话都会受到影响
         */
        Matrix beforeMatrix = Matrix.factory.rand(5, 5);
        beforeMatrix.setLabel("beforeMatrix");
        beforeMatrix.showGUI();

        Matrix nowMatrix = beforeMatrix.selectRows(Ret.NEW, 0);
        nowMatrix.setAsDouble(10, 0, 0);
        nowMatrix.setLabel("nowMatrix");
        nowMatrix.showGUI();

        Matrix laterMatrix = beforeMatrix.transpose(Ret.LINK);
        laterMatrix.setLabel("laterMatrix");
        // laterMatrix.showGUI();
        Matrix xx = laterMatrix.minus(Ret.LINK, false, 10);
        double xxd = xx.getAsDouble(0, 0);
        Constans.sop(xxd);
        // xx.showGUI();

    }
}