Java验证辛钦大数定理

本实验通过程序模拟采集大量的样本数据来验证辛钦大数定理。

实验环境

本实验采用Java语言编程,开发环境为Eclipse,图像生成使用JFreeChart类。

一,验证辛钦大数定理

由辛钦大数定理描述为:

辛钦大数定理(弱大数定理)  设随机变量序列 X1, X2, … 相互独立,服从同一分布,具有数学期望E(Xi) = μ, i = 1, 2, …, 则对于任意正数ε ,有

       

           

实验思路:

实验产生的随机变量Xi服从均匀分布与(0-1)分布,即X~U(0,1)或X~b(1,0.5)首先随机产生5000(0,1)内,已知X服从均匀分布或(0-1)分布,所以均值E(X)=(a+b)/2=0.5。且随机变量的方差相等,统计样本容量为n的样本算术平均值,n以10为步长线性增加,画出()的图像,将其与y=0.5的图像对比,可得,当n越来越大时,趋向于均值E(X)=0.5,即


 

实验画得如下图一:

         

                                 图一

由图可看出,当数据点足够多时

               

实验程序如下,程序已经加上注释:

import java.awt.Color;
import java.util.Random;
import java.util.SortedSet;
import java.util.TreeSet;

import org.jfree.chart.ChartFactory;
import org.jfree.chart.ChartFrame;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.axis.NumberAxis;
import org.jfree.chart.plot.PlotOrientation;
import org.jfree.chart.plot.XYPlot;
import org.jfree.chart.renderer.xy.XYLineAndShapeRenderer;
import org.jfree.data.category.DefaultCategoryDataset;
import org.jfree.data.function.Function2D;
import org.jfree.data.function.NormalDistributionFunction2D;
import org.jfree.data.general.DatasetGroup;
import org.jfree.data.general.DatasetUtilities;
import org.jfree.data.xy.XYDataset;
import org.jfree.data.xy.XYSeries;
import org.jfree.data.xy.XYSeriesCollection;

public class KhinchinBigDataTheorem {

	/*********************************
	 *样本点集
	 ********************************/
	private static XYSeriesCollection dataset=new XYSeriesCollection();
	
	/**********************************
	 * getXYSeriesCollection()
	 * 获得样本点XY坐标点集XYSeriesCollection
	 * @return
	 *********************************/
	public static XYSeriesCollection getXYSeriesCollection(){
		XYSeries series= new XYSeries("Khinchin");
		
		int sampleSize=5000;                               //验证样本容量
		int bin=10;                                         //以步长为bin进行样本概率统计
		int poltSize=sampleSize/bin;                        //样本分成的区间数
		double[] sampleProbability=new double[poltSize];    //每个区间内出现的点得数量的矩阵
		double[] XAxis=new double[poltSize];                //每个区间所采取的Xi(X轴坐标点)的矩阵
		
		for (int i = 0; i < XAxis.length; i++) {
			sampleProbability[i]=0;
			XAxis[i]=0;
		}
		/***************************************************
		 * 产生500000个(0,1)内均匀分布与(0-1)分布的样本点
		 * 画出样本数量从少到多的算术平均值趋向于均值的差距
		 ***************************************************/
		double u=0.5;                                       //样本服从的均值
		double[] samplePoints=new double[sampleSize];       //分布的样本点
		int su=0;
		for (int i = 0; i < samplePoints.length; i++) {
			//交替产生均匀分布与(0-1)分布样本点
			if (i%2==0) {
				samplePoints[i]=new Random().nextDouble();
			}else {
				samplePoints[i]=generator(0.5);
			}
		}

		double sum=0;
		for (int i = 0; i < samplePoints.length; i++) {
			sum+=samplePoints[i];
			if (i%bin==0) {
				XAxis[i/bin]=i;
				sampleProbability[i/bin]=sum/(i+1);
				//System.out.println(sampleProbability[i/bin]);
			}
		}
		for (int i = 0; i < poltSize ; i++) {
				series.add(XAxis[i], sampleProbability[i]);
		}
		
		dataset.addSeries(series);
		return dataset;
	}
	
	/**********************************************
	 * 产生概率为0.5的(0-1)分布点
	 * @param p
	 * @return
	 **********************************************/
	public static int generator(double p){
		Random random=new Random();
		double g=random.nextDouble();
		int i=0;
		if(g<p){
			i=1;
		}else {
			i=0;
		}
		return i;
	}
	
	public XYSeriesCollection dataset1;
	public JFreeChart chart;
	public XYPlot plot;
	
	public KhinchinBigDataTheorem() {

		//KhinchinBigDataTheorem centerLimit=new KhinchinBigDataTheorem();
		dataset1=getXYSeriesCollection();
		//获取样本数据集
		XYSeriesCollection dataset=new XYSeriesCollection();
		XYSeries series= new XYSeries("0.5 Line");
		for (int i = 0; i < 500; i++) {
			series.add(i*10.0, 0.5);
		}
		dataset.addSeries(series);
 		chart = ChartFactory.createXYLineChart("MultiAxis", "X axis",
				"First Y Axis", dataset1, PlotOrientation.VERTICAL, true, true,
				false);

		plot = chart.getXYPlot();
		plot.setDataset(1, dataset);
		XYLineAndShapeRenderer render2 =  new XYLineAndShapeRenderer();	
		render2.setSeriesPaint(0, Color.BLUE);
		plot.setRenderer(1, render2);
	}

	public static void main(String[] agrs) {
		KhinchinBigDataTheorem obj = new KhinchinBigDataTheorem();
		ChartFrame frame = new ChartFrame("多坐标轴", obj.chart);
		frame.pack();
		frame.setVisible(true);
	}
}

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转载自www.linuxidc.com/Linux/2016-10/136272.htm
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