常用的数学计算工具(java)

1.引用包

org.apache.commons:commons-math3:3.6.1

2.测试类:

@Test

public void testMath(){

double[] values =new double[] {0.33, 1.33,0.27333, 0.3, 0.501,

            0.444, 0.44, 0.34496, 0.33,0.3, 0.292, 0.667 };

    SummaryStatistics summaryStatistics =new SummaryStatistics();

    for(double data:values){

summaryStatistics.addValue(data);

    }

System.out.println(summaryStatistics.toString());

    Min min =new Min();

    Max max =new Max();

    Mean mean =new Mean(); // 算术平均值

    Product product =new Product();//乘积

    Sum sum =new Sum();

    Variance variance =new Variance();//方差

    System.out.println("min: " +min.evaluate(values));

    System.out.println("max: " +max.evaluate(values));

    System.out.println("mean: " +mean.evaluate(values));

    System.out.println("product:" + product.evaluate(values));

    System.out.println("sum: " +sum.evaluate(values));

    System.out.println("variance:" + variance.evaluate(values));

    Percentile percentile =new Percentile(); // 百分位数

    GeometricMean geoMean =new GeometricMean(); // 几何平均数,n个正数的连乘积的n次算术根叫做这n个数的几何平均数

    Skewness skewness =new Skewness(); //Skewness();

    Kurtosis kurtosis =new Kurtosis(); //Kurtosis,峰度

    SumOfSquares sumOfSquares =new SumOfSquares(); // 平方和

    StandardDeviation StandardDeviation =new StandardDeviation();//标准差

    System.out.println("80 percentilevalue: "

            + percentile.evaluate(values,80.0));

    System.out.println("geometricmean: " + geoMean.evaluate(values));

    System.out.println("skewness:" + skewness.evaluate(values));

    System.out.println("kurtosis:" + kurtosis.evaluate(values));

    System.out.println("sumOfSquares:" + sumOfSquares.evaluate(values));

    System.out.println("StandardDeviation: " +StandardDeviation.evaluate(values));

    System.out.println("-------------------------------------");

    // Create a real matrix with two rowsand three columns

    double[][] matrixData = { {1d,2d,3d},{2d,5d,3d}};

    RealMatrix m =new Array2DRowRealMatrix(matrixData);

    System.out.println(m);

    // One more with three rows, twocolumns

    double[][] matrixData2 = { {1d,2d},{2d,5d}, {1d, 7d}};

    RealMatrix n =new Array2DRowRealMatrix(matrixData2);

    // Note: The constructor copies  the input double[][] array.

// Now multiply m by n

    RealMatrix p = m.multiply(n);

    System.out.println("p:"+p);

    System.out.println(p.getRowDimension());    // 2

    System.out.println(p.getColumnDimension()); // 2

// Invert p, using LUdecomposition

    RealMatrix pInverse =new LUDecomposition(p).getSolver().getInverse();

    System.out.println(pInverse);

}