Introduction and use of Apache Commons math

Source: Internet
Author: User

Apache Commons Math is a set of functions biased toward scientific calculation, mainly for linear algebra, mathematical analysis, probability and statistics.

Although I graduated from mathematics, I was also holding the "mathematical analysis" gnawing, but for a long time, these concepts are beginning unfamiliar, write a little example, only for reference.

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;

/**

* Simple use of the Commons math method

* @author Fan Fangming

*/

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 (); Arithmetic average

Product Product = new product ();//Product

Sum sum = new sum ();

Variance Variance = new Variance ();//Variance

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 (); Percentile number of percentiles

Geometricmean Geomean = Newgeometricmean (); Geometric averages, n-times arithmetic roots of a continuous product of n positive numbers are called the geometric averages of the n numbers.

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

Kurtosis Kurtosis = new Kurtosis (); Kurtosis, peak degree

Sumofsquares sumofsquares = Newsumofsquares (); Sum of squares

Standarddeviation standarddeviation =new standarddeviation ();//Standard deviation

System.out.println ("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 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);

}

}

The results of the operation are as follows:

min:0.27333

max:1.33

mean:0.46269083333333333

Product:2.3429343978460972e-5

sum:5.552289999999999

variance:0.08757300031742428

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}}

Introduction and use of Apache Commons math

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