private static void print(double[] v) {
// TODO Auto-generated method stub
for(int i=0;i<v.length;i++){
System.out.print(v[i]+ " ");
}
System.out.println();
}
}
7.math组件用法实例
package apache.commons.math.test;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.LUDecomposition;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.stat.descriptive.moment.GeometricMean;
import org.apache.commons.math3.stat.descriptive.moment.Kurtosis;
import org.apache.commons.math3.stat.descriptive.moment.Mean;
import org.apache.commons.math3.stat.descriptive.moment.Skewness;
import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import org.apache.commons.math3.stat.descriptive.rank.Max;
import org.apache.commons.math3.stat.descriptive.rank.Min;
import org.apache.commons.math3.stat.descriptive.rank.Percentile;
import org.apache.commons.math3.stat.descriptive.summary.Product;
import org.apache.commons.math3.stat.descriptive.summary.Sum;
import org.apache.commons.math3.stat.descriptive.summary.SumOfSquares;
/**
*
* @ClassName: TestMathUserage
* @Description: math组件用法实例
* @author zengfh
* @date 2014年11月21日 下午1:25:24
*
*/
public class TestMathUserage {
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 };
/*
* System.out.println( "min: " + StatUtils.min( values ) );
* System.out.println( "max: " + StatUtils.max( values ) );
* System.out.println( "mean: " + StatUtils.mean( values ) ); // Returns
* the arithmetic mean of the entries in the input array, or Double.NaN
* if the array is empty System.out.println( "product: " +
* StatUtils.product( values ) ); //Returns the product of the entries
* in the input array, or Double.NaN if the array is empty.
* System.out.println( "sum: " + StatUtils.sum( values ) ); //Returns
* the sum of the values in the input array, or Double.NaN if the array
* is empty. System.out.println( "variance: " + StatUtils.variance(
* values ) ); // Returns the variance of the entries in the input
* array, or Double.NaN if the array is empty.
*/
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 percentile value: "
+ percentile.evaluate(values, 80.0));
System.out.println("geometric mean: " + 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 rows and three columns
double[][] matrixData = { {1d,2d,3d}, {2d,5d,3d}};
RealMatrix m = new Array2DRowRealMatrix(matrixData);
System.out.println(m);
// One more with three rows, two columns
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 LU decomposition
RealMatrix pInverse = new LUDecomposition(p).getSolver().getInverse();
System.out.println(pInverse);
}
}