用Hadoop扔飞镖算Pi——献给圆周率日

q3256

　　众所周知，在一个正方形上抛飞镖，假设有一个半径为正方形边长的1/4圆，则抛的飞镖在1/4圆内的概率为1/4圆面积除以正方形面积，即(pi*r^2 /4)/r^2，假设抛飞镖在圆内的概率为n，则pi=4n，通过蒙特卡洛模拟则可算出一个较接近的pi。由于频率是不断接近概率的，因此必须抛很多次飞 镖，这里采用了Hadoop去模拟。
1. [代码]MapReduce的mainRunner忽略，大家可根据需要改下输入输出KeyValue的意义    

01

// Mapper

02	public class PieMapper extends

03	Mapper<IntWritable, LongWritable, LongWritable, LongWritable> {

04	private Random random = new Random();

05

@Override

06	protected void map(IntWritable key, LongWritable value, Context context)

07	throws IOException, InterruptedException {

08	long inside = 0;

09	for (int i = 0; i < value.get(); i++) {

10	double a = random.nextDouble();

11	double b = random.nextDouble();

12	if ((a * a + b * b) <= 1) {

13

inside++;

14

}

15

}

16	context.write(value, new LongWritable(inside));

17

}

18

}

19	// 输入为第几行（忽略）+本行所算的抛飞镖次数

20

21	// Reducer

22	public class PieReducer extends

23	Reducer<LongWritable, LongWritable, LongWritable, DoubleWritable> {

24

@Override

25	protected void reduce(LongWritable key, Iterable<LongWritable> values,

26	Context context) throws IOException, InterruptedException {

27	long total = 0;

28	long inside = 0;

29	for (LongWritable value : values) {

30	total += key.get();

31	inside += value.get();

32

}

33	context.write(key, new DoubleWritable(

34	4 * (inside / total)));

35

}

36

}

37

38	// 代码应该比较清楚，输出为抛的总次数所对应的算出的圆周率，可以用来对比抛N次所得到的精度。当然Double是非常不精确的，要想更精确则需要重新实现大的浮点类型