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。
1 packagetest.ffm83.commons.math; 2 3 importorg.apache.commons.math3.linear.Array2DRowRealMatrix; 4 5 import org.apache.commons.math3.linear.LUDecomposition; 6 7 importorg.apache.commons.math3.linear.RealMatrix; 8 9 importorg.apache.commons.math3.stat.descriptive.moment.GeometricMean; 10 11 importorg.apache.commons.math3.stat.descriptive.moment.Kurtosis; 12 13 importorg.apache.commons.math3.stat.descriptive.moment.Mean; 14 15 importorg.apache.commons.math3.stat.descriptive.moment.Skewness; 16 17 importorg.apache.commons.math3.stat.descriptive.moment.StandardDeviation; 18 19 importorg.apache.commons.math3.stat.descriptive.moment.Variance; 20 21 import org.apache.commons.math3.stat.descriptive.rank.Max; 22 23 importorg.apache.commons.math3.stat.descriptive.rank.Min; 24 25 importorg.apache.commons.math3.stat.descriptive.rank.Percentile; 26 27 importorg.apache.commons.math3.stat.descriptive.summary.Product; 28 29 importorg.apache.commons.math3.stat.descriptive.summary.Sum; 30 31 importorg.apache.commons.math3.stat.descriptive.summary.SumOfSquares; 32 33 34 35 /** 36 37 * 简单使用commons Math方法 38 39 * @author 范芳铭 40 41 */ 42 43 public class MathUsage { 44 45 public static void main(String[] args) { 46 47 double[] values = new double[] { 0.33, 1.33,0.27333, 0.3, 0.501, 48 49 0.444, 0.44, 0.34496, 0.33,0.3, 0.292, 0.667 }; 50 51 Min min = new Min(); 52 53 Max max = new Max(); 54 55 56 57 Mean mean = new Mean(); // 算术平均值 58 59 Product product = new Product();//乘积 60 61 Sum sum = new Sum(); 62 63 Variance variance = new Variance();//方差 64 65 System.out.println("min: " +min.evaluate(values)); 66 67 System.out.println("max: " +max.evaluate(values)); 68 69 System.out.println("mean: " +mean.evaluate(values)); 70 71 System.out.println("product:" + product.evaluate(values)); 72 73 System.out.println("sum: " +sum.evaluate(values)); 74 75 System.out.println("variance:" + variance.evaluate(values)); 76 77 78 79 Percentile percentile = newPercentile(); // 百分位数 80 81 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 82 83 Skewness skewness = new Skewness(); //Skewness(); 84 85 Kurtosis kurtosis = new Kurtosis(); //Kurtosis,峰度 86 87 SumOfSquares sumOfSquares = newSumOfSquares(); // 平方和 88 89 StandardDeviation StandardDeviation =new StandardDeviation();//标准差 90 91 System.out.println("80 percentilevalue: " 92 93 + percentile.evaluate(values,80.0)); 94 95 System.out.println("geometricmean: " + geoMean.evaluate(values)); 96 97 System.out.println("skewness:" + skewness.evaluate(values)); 98 99 System.out.println("kurtosis:" + kurtosis.evaluate(values)); 100 101 System.out.println("sumOfSquares:" + sumOfSquares.evaluate(values)); 102 103 System.out.println("StandardDeviation: " +StandardDeviation.evaluate(values)); 104 105 106 107 System.out.println("-------------------------------------"); 108 109 // Create a real matrix with two rowsand three columns 110 111 double[][] matrixData = { {1d,2d,3d},{2d,5d,3d}}; 112 113 RealMatrix m = newArray2DRowRealMatrix(matrixData); 114 115 System.out.println(m); 116 117 // One more with three rows, twocolumns 118 119 double[][] matrixData2 = { {1d,2d},{2d,5d}, {1d, 7d}}; 120 121 RealMatrix n = newArray2DRowRealMatrix(matrixData2); 122 123 // Note: The constructor copies the input double[][] array. 124 125 // Now multiply m by n 126 127 RealMatrix p = m.multiply(n); 128 129 System.out.println("p:"+p); 130 131 System.out.println(p.getRowDimension()); // 2 132 133 System.out.println(p.getColumnDimension()); // 2 134 135 // Invert p, using LUdecomposition 136 137 RealMatrix pInverse = newLUDecomposition(p).getSolver().getInverse(); 138 139 System.out.println(pInverse); 140 141 } 142 143 } 144 145 运行结果如下： 146 147 min: 0.27333 148 149 max: 1.33 150 151 mean: 0.46269083333333333 152 153 product: 2.3429343978460972E-5 154 155 sum: 5.552289999999999 156 157 variance: 0.08757300031742428 158 159 80 percentile value: 0.5674000000000001 160 161 geometric mean: 0.4112886050879374 162 163 skewness: 2.670095445623868 164 165 kurtosis: 7.718241303328169 166 167 sumOfSquares: 3.5322966905000004 168 169 StandardDeviation: 0.2959273564870681 170 171 ------------------------------------- 172 173 Array2DRowRealMatrix{{1.0,2.0,3.0},{2.0,5.0,3.0}} 174 175 p:Array2DRowRealMatrix{{8.0,33.0},{15.0,50.0}} 176 177 2 178 179 2 180 181 Array2DRowRealMatrix{{-0.5263157895,0.3473684211},{0.1578947368,-0.0842105263}}
