解释每一行代码public class KMeansMapper extends Mapper<LongWritable, Text, Text, Text> { private List<List<Double>> centers = new ArrayList<>(); @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String[] dimensions; List<Double> point = new ArrayList<>(); double centerIndex = 1; double minDistance = Double.MAX_VALUE; int iteration = context.getConfiguration().getInt(KMeans.ITERATION, 0); if (centers.size() == 0) { String centersPath = context.getCacheFiles()[0].toString(); centers = CentersOperation.getCenters(centersPath, true); } dimensions = value.toString().split("[,\t]"); for (int i = 0; i < dimensions.length - 1; i++) { point.add(Double.parseDouble(dimensions[i])); } for (int i = 0; i < centers.size(); i++) { double distance = 0; List<Double> center = centers.get(i); for (int j = 0; j < center.size(); j++) { distance += Math.pow((point.get(j) - center.get(j)), 2); } distance = Math.sqrt(distance); if (distance < minDistance) { minDistance = distance; centerIndex = i + 1; } } String pointData = value.toString().split("\t")[0]; if (iteration == (KMeans.MAX_ITERATION - 1)) { context.write(new Text(pointData), new Text(String.valueOf(centerIndex))); } else { context.write(new Text(String.valueOf(centerIndex)), new Text(pointData)); } } }

这段代码是一个KMeans算法的Map阶段的实现。它继承了Hadoop的Mapper类，输入类型是<LongWritable, Text>，输出类型是<Text, Text>。以下是每一行代码的解释：

1. `public class KMeansMapper extends Mapper<LongWritable, Text, Text, Text> {`
定义KMeansMapper类，继承了Mapper类，并指定输入和输出类型。

2. `private List<List<Double>> centers = new ArrayList<>();`
定义一个List类型的变量centers，存储聚类中心点的坐标。

3. `@Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {`
重写Mapper类的map方法，接收三个参数：key、value和context对象。

4. `String[] dimensions; List<Double> point = new ArrayList<>(); double centerIndex = 1; double minDistance = Double.MAX_VALUE; int iteration = context.getConfiguration().getInt(KMeans.ITERATION, 0);`
定义一些变量，dimensions是用来存储每个数据点的坐标，point是用来存储每个数据点的坐标值，centerIndex是用来存储该数据点所属的聚类中心点的编号，minDistance是用来存储该数据点到聚类中心点的最小距离，iteration是用来存储迭代次数的变量。

5. `if (centers.size() == 0) { String centersPath = context.getCacheFiles()[0].toString(); centers = CentersOperation.getCenters(centersPath, true); }`
如果centers为空，则从context对象中获取聚类中心点的路径，并调用CentersOperation.getCenters方法读取聚类中心点的坐标值。

6. `dimensions = value.toString().split("[,\t]"); for (int i = 0; i < dimensions.length - 1; i++) { point.add(Double.parseDouble(dimensions[i])); }`
将输入数据点的坐标值存储在point列表中。

7. `for (int i = 0; i < centers.size(); i++) { double distance = 0; List<Double> center = centers.get(i); for (int j = 0; j < center.size(); j++) { distance += Math.pow((point.get(j) - center.get(j)), 2); } distance = Math.sqrt(distance); if (distance < minDistance) { minDistance = distance; centerIndex = i + 1; } }`
计算该数据点到每个聚类中心点的距离，并找到最近的聚类中心点，更新minDistance和centerIndex。

8. `String pointData = value.toString().split("\t")[0];`
获取数据点的编号。

9. `if (iteration == (KMeans.MAX_ITERATION - 1)) { context.write(new Text(pointData), new Text(String.valueOf(centerIndex))); } else { context.write(new Text(String.valueOf(centerIndex)), new Text(pointData)); }`
如果迭代次数已达到最大值，则将数据点编号和聚类中心点编号写入context对象；否则，将聚类中心点编号和数据点编号写入context对象。最后，上述步骤会重复执行，直到所有数据点都被处理完毕。