apache commons math 示例代码

apache commons Math是一组偏向科学计算为主的函数，主要是针对线性代数，数学分析，概率和统计等方面。

我虽然是数学专业毕业，当年也是抱着《数学分析》啃，但是好久不用，这些概念都开始生疏，写一点例子，仅作参考。

 packagetest.ffm83.commons.math;

 importorg.apache.commons.math3.linear.Array2DRowRealMatrix; 

 import org.apache.commons.math3.linear.LUDecomposition; 

 importorg.apache.commons.math3.linear.RealMatrix; 

 importorg.apache.commons.math3.stat.descriptive.moment.GeometricMean; 

 importorg.apache.commons.math3.stat.descriptive.moment.Kurtosis; 

 importorg.apache.commons.math3.stat.descriptive.moment.Mean; 

 importorg.apache.commons.math3.stat.descriptive.moment.Skewness; 

 importorg.apache.commons.math3.stat.descriptive.moment.StandardDeviation; 

 importorg.apache.commons.math3.stat.descriptive.moment.Variance; 

 import org.apache.commons.math3.stat.descriptive.rank.Max; 

 importorg.apache.commons.math3.stat.descriptive.rank.Min; 

 importorg.apache.commons.math3.stat.descriptive.rank.Percentile; 

 importorg.apache.commons.math3.stat.descriptive.summary.Product; 

 importorg.apache.commons.math3.stat.descriptive.summary.Sum; 

 importorg.apache.commons.math3.stat.descriptive.summary.SumOfSquares;

 /**

  * 简单使用commons Math方法

  * @author 范芳铭

  */

 public class MathUsage {

      public static void main(String[] args) { 

         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 }; 

         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 = newPercentile(); // 百分位数 

         GeometricMean geoMean = newGeometricMean(); // 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 = newSumOfSquares(); // 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 = newArray2DRowRealMatrix(matrixData); 

         System.out.println(m); 

         // One more with three rows, twocolumns 

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

         RealMatrix n = newArray2DRowRealMatrix(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 = newLUDecomposition(p).getSolver().getInverse(); 

         System.out.println(pInverse); 

     } 

 }

 运行结果如下：

 min: 0.27333

 max: 1.33

 mean: 0.46269083333333333

 product: 2.3429343978460972E-5

 sum: 5.552289999999999

 variance: 0.08757300031742428

 80 percentile value: 0.5674000000000001

 geometric mean: 0.4112886050879374

 skewness: 2.670095445623868

 kurtosis: 7.718241303328169

 sumOfSquares: 3.5322966905000004

 StandardDeviation: 0.2959273564870681

 -------------------------------------

 Array2DRowRealMatrix{{1.0,2.0,3.0},{2.0,5.0,3.0}}

 p:Array2DRowRealMatrix{{8.0,33.0},{15.0,50.0}}

 2

 2

 Array2DRowRealMatrix{{-0.5263157895,0.3473684211},{0.1578947368,-0.0842105263}}