请给出一个和hadoop自带计算圆周率代码算法思路一样的完整代码

好的，这里是一个和Hadoop自带的计算圆周率代码算法思路一样的Java代码，使用了MapReduce框架实现：

Mapper：

public class PiMapper extends Mapper<LongWritable, Text, NullWritable, NullWritable> {
    private long numSamples;
    private Random random = new Random();

    protected void setup(Context context) throws IOException,InterruptedException {
        Configuration conf = context.getConfiguration();
        numSamples = conf.getLong("numSamples", 1);
    }

    protected void map(LongWritable key, Text value, Context context) throws IOException,InterruptedException {
        long countInside = 0;
        for (long i = 0; i < numSamples; i++) {
            double x = random.nextDouble();
            double y = random.nextDouble();
            if ((x * x + y * y) < 1.0) {
                countInside++;
            }
        }
        context.getCounter("Pi", "countInside").increment(countInside);
        context.getCounter("Pi", "numSamples").increment(numSamples);
    }
}

Reducer：

public class PiReducer extends Reducer<NullWritable, NullWritable, NullWritable, NullWritable> {
    protected void reduce(NullWritable key, Iterable<NullWritable> values, Context context) throws IOException,InterruptedException {
        long numSamples = context.getCounter("Pi", "numSamples").getValue();
        long countInside = context.getCounter("Pi", "countInside").getValue();
        double pi = 4.0 * countInside / numSamples;
        context.write(NullWritable.get(), new Text("Pi estimation: " + pi));
    }
}

Driver：

public class PiEstimator {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        long numMaps = Long.parseLong(args[0]);
        long numSamples = Long.parseLong(args[1]);
        Job job = Job.getInstance(conf);
        job.setJobName("Pi Estimator");
        job.setJarByClass(PiEstimator.class);
        job.setNumReduceTasks(1);
        job.setOutputKeyClass(NullWritable.class);
        job.setOutputValueClass(NullWritable.class);
        job.setMapperClass(PiMapper.class);
        job.setReducerClass(PiReducer.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        TextInputFormat.setInputPaths(job, new Path("/dev/null"));
        TextOutputFormat.setOutputPath(job, new Path("/tmp/pi"));
        job.getConfiguration().setLong("numSamples", numSamples / numMaps);
        job.waitForCompletion(true);
    }
}

这个代码使用了MapReduce框架，将计算分为了多个Mapper任务，并将结果汇总到了一个Reducer任务中。每个Mapper任务会生成一定数量的随机点，然后计算这些随机点中在圆内的点的数量。Reducer任务会将所有的Mapper任务中在圆内的点的数量加起来，并计算出π的近似值。