CommonMath

The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang. 官方案例文档

• org.apache.commons.math4.stat - statistics, statistical tests
• org.apache.commons.math4.analysis - rootfinding, integration, interpolation, polynomials
• org.apache.commons.math4.random - random numbers, strings and data generation
• org.apache.commons.math4.special - special functions (Gamma, Beta)
• org.apache.commons.math4.linear - matrices, solving linear systems
• org.apache.commons.math4.util - common math/stat functions extending java.lang.Math
• org.apache.commons.math4.complex - complex numbers
• org.apache.commons.math4.distribution - probability distributions
• org.apache.commons.math4.fraction - rational numbers
• org.apache.commons.math4.transform - transform methods (Fast Fourier)
• org.apache.commons.math4.geometry - geometry (Euclidean spaces and Binary Space Partitioning)
• org.apache.commons.math4.optim - function maximization or minimization
• org.apache.commons.math4.ode - Ordinary Differential Equations integration
• org.apache.commons.math4.genetics - Genetic Algorithms
• org.apache.commons.math4.fitting - Curve Fitting
• org.apache.commons.math4.ml - Machine Learning

• Computing means, variances and other summary statistics for a list of numbers
• Fitting a line to a set of data points using linear regression
• Fitting a curve to a set of data points
• Finding a smooth curve that passes through a collection of points (interpolation)
• Fitting a parametric model to a set of measurements using least-squares methods
• Solving equations involving real-valued functions (i.e. root-finding)
• Solving systems of linear equations
• Solving Ordinary Differential Equations
• Minimizing multi-dimensional functions
• Generating random numbers with more restrictions (e.g distribution, range) than what is possible using the JDK
• Generating random samples and/or datasets that are “like” the data in an input file
• Performing statistical significance tests
• Miscellaneous mathematical functions such as factorials, binomial coefficients and “special functions” (e.g. gamma, beta functions)

1. 多项式拟合

double[] x = new double[]{1, 2, 3, 4, 5};
double[] y = new double[]{19,33,53,79,111};
		  
WeightedObservedPoints points = new WeightedObservedPoints(); 	 
for(int i = 0; i < x.length; i++) { //把数据点加入观察的序列
	points.add(x[i], y[i]);
}		
PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2);  //指定多项式阶数 
double[] result = fitter.fit(points.toList());  // 曲线拟合，结果保存于数组[c,b,a]  a*x*x+b*x+c=y	 
for(int i = 0; i < result.length; i++) {
	System.out.println(result[i]);
}

/** 
* 计算指定对象的运行时间开销。 
* 
* @param curveFitting 指定被测对象。 
* @return 返回sub.run的时间开销，单位为s。 
* @throws Exception 
*/  
public double calcTimeCost(CurveFitting curveFitting) throws Exception {  
    List<Object> params = curveFitting.getParams();  
    long startTime = System.nanoTime();  
    Object result = curveFitting.run(params);  
    long stopTime = System.nanoTime();  
    curveFitting.printResult(result);  
    System.out.println("start: " + startTime + " / stop: " + stopTime);  
    return 1.0e-9 * (stopTime - startTime);  
}