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K-均值聚类__示例程序_图模型_大数据计算服务-阿里云

k-均值聚类（Kmeans） 算法是非常基础并大量使用的聚类算法。算法基本思想是：以空间中 k 个点为中心进行聚类，对最靠近他们的点进行归类。通过迭代的方法，逐次更新各聚类中心的值，直至得到最好的聚类结果。

假设要把样本集分为 k 个类别，算法描述如下：

• 适当选择 k 个类的初始中心
• 在第 i 次迭代中，对任意一个样本，求其到 k 个中心的距离，将该样本归到距离最短的中心所在的类
• 利用均值等方法更新该类的中心值
• 对于所有的 k 个聚类中心，如果利用上两步的迭代法更新后，值保持不变或者小于某个阈值，则迭代结束，否则继续迭代

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.log4j.Logger;
import com.aliyun.odps.io.WritableRecord;
import com.aliyun.odps.graph.Aggregator;
import com.aliyun.odps.graph.ComputeContext;
import com.aliyun.odps.graph.GraphJob;
import com.aliyun.odps.graph.GraphLoader;
import com.aliyun.odps.graph.MutationContext;
import com.aliyun.odps.graph.Vertex;
import com.aliyun.odps.graph.WorkerContext;
import com.aliyun.odps.io.DoubleWritable;
import com.aliyun.odps.io.LongWritable;
import com.aliyun.odps.io.NullWritable;
import com.aliyun.odps.data.TableInfo;
import com.aliyun.odps.io.Text;
import com.aliyun.odps.io.Tuple;
import com.aliyun.odps.io.Writable;
public class Kmeans {
  private final static Logger LOG = Logger.getLogger(Kmeans.class);
  public static class KmeansVertex extends
      Vertex<Text, Tuple, NullWritable, NullWritable> {
    @Override
    public void compute(
        ComputeContext<Text, Tuple, NullWritable, NullWritable> context,
        Iterable<NullWritable> messages) throws IOException {
      context.aggregate(getValue());
    }
  }
  public static class KmeansVertexReader extends
      GraphLoader<Text, Tuple, NullWritable, NullWritable> {
    @Override
    public void load(LongWritable recordNum, WritableRecord record,
        MutationContext<Text, Tuple, NullWritable, NullWritable> context)
        throws IOException {
      KmeansVertex vertex = new KmeansVertex();
      vertex.setId(new Text(String.valueOf(recordNum.get())));
      vertex.setValue(new Tuple(record.getAll()));
      context.addVertexRequest(vertex);
    }
  }
  public static class KmeansAggrValue implements Writable {
    Tuple centers = new Tuple();
    Tuple sums = new Tuple();
    Tuple counts = new Tuple();
    @Override
    public void write(DataOutput out) throws IOException {
      centers.write(out);
      sums.write(out);
      counts.write(out);
    }
    @Override
    public void readFields(DataInput in) throws IOException {
      centers = new Tuple();
      centers.readFields(in);
      sums = new Tuple();
      sums.readFields(in);
      counts = new Tuple();
      counts.readFields(in);
    }
    @Override
    public String toString() {
      return "centers " + centers.toString() + ", sums " + sums.toString()
          + ", counts " + counts.toString();
    }
  }
  public static class KmeansAggregator extends Aggregator<KmeansAggrValue> {
    @SuppressWarnings("rawtypes")
    @Override
    public KmeansAggrValue createInitialValue(WorkerContext context)
        throws IOException {
      KmeansAggrValue aggrVal = null;
      if (context.getSuperstep() == 0) {
        aggrVal = new KmeansAggrValue();
        aggrVal.centers = new Tuple();
        aggrVal.sums = new Tuple();
        aggrVal.counts = new Tuple();
        byte[] centers = context.readCacheFile("centers");
        String lines[] = new String(centers).split("n");
        for (int i = 0; i < lines.length; i++) {
          String[] ss = lines[i].split(",");
          Tuple center = new Tuple();
          Tuple sum = new Tuple();
          for (int j = 0; j < ss.length; ++j) {
            center.append(new DoubleWritable(Double.valueOf(ss[j].trim())));
            sum.append(new DoubleWritable(0.0));
          }
          LongWritable count = new LongWritable(0);
          aggrVal.sums.append(sum);
          aggrVal.counts.append(count);
          aggrVal.centers.append(center);
        }
      } else {
        aggrVal = (KmeansAggrValue) context.getLastAggregatedValue(0);
      }
      return aggrVal;
    }
    @Override
    public void aggregate(KmeansAggrValue value, Object item) {
      int min = 0;
      double mindist = Double.MAX_VALUE;
      Tuple point = (Tuple) item;
      for (int i = 0; i < value.centers.size(); i++) {
        Tuple center = (Tuple) value.centers.get(i);
        // use Euclidean Distance, no need to calculate sqrt
        double dist = 0.0d;
        for (int j = 0; j < center.size(); j++) {
          double v = ((DoubleWritable) point.get(j)).get()
              - ((DoubleWritable) center.get(j)).get();
          dist += v * v;
        }
        if (dist < mindist) {
          mindist = dist;
          min = i;
        }
      }
      // update sum and count
      Tuple sum = (Tuple) value.sums.get(min);
      for (int i = 0; i < point.size(); i++) {
        DoubleWritable s = (DoubleWritable) sum.get(i);
        s.set(s.get() + ((DoubleWritable) point.get(i)).get());
      }
      LongWritable count = (LongWritable) value.counts.get(min);
      count.set(count.get() + 1);
    }
    @Override
    public void merge(KmeansAggrValue value, KmeansAggrValue partial) {
      for (int i = 0; i < value.sums.size(); i++) {
        Tuple sum = (Tuple) value.sums.get(i);
        Tuple that = (Tuple) partial.sums.get(i);
        for (int j = 0; j < sum.size(); j++) {
          DoubleWritable s = (DoubleWritable) sum.get(j);
          s.set(s.get() + ((DoubleWritable) that.get(j)).get());
        }
      }
      for (int i = 0; i < value.counts.size(); i++) {
        LongWritable count = (LongWritable) value.counts.get(i);
        count.set(count.get() + ((LongWritable) partial.counts.get(i)).get());
      }
    }
    @SuppressWarnings("rawtypes")
    @Override
    public boolean terminate(WorkerContext context, KmeansAggrValue value)
        throws IOException {
      // compute new centers
      Tuple newCenters = new Tuple(value.sums.size());
      for (int i = 0; i < value.sums.size(); i++) {
        Tuple sum = (Tuple) value.sums.get(i);
        Tuple newCenter = new Tuple(sum.size());
        LongWritable c = (LongWritable) value.counts.get(i);
        for (int j = 0; j < sum.size(); j++) {
          DoubleWritable s = (DoubleWritable) sum.get(j);
          double val = s.get() / c.get();
          newCenter.set(j, new DoubleWritable(val));
          // reset sum for next iteration
          s.set(0.0d);
        }
        // reset count for next iteration
        c.set(0);
        newCenters.set(i, newCenter);
      }
      // update centers
      Tuple oldCenters = value.centers;
      value.centers = newCenters;
      LOG.info("old centers: " + oldCenters + ", new centers: " + newCenters);
      // compare new/old centers
      boolean converged = true;
      for (int i = 0; i < value.centers.size() && converged; i++) {
        Tuple oldCenter = (Tuple) oldCenters.get(i);
        Tuple newCenter = (Tuple) newCenters.get(i);
        double sum = 0.0d;
        for (int j = 0; j < newCenter.size(); j++) {
          double v = ((DoubleWritable) newCenter.get(j)).get()
              - ((DoubleWritable) oldCenter.get(j)).get();
          sum += v * v;
        }
        double dist = Math.sqrt(sum);
        LOG.info("old center: " + oldCenter + ", new center: " + newCenter
            + ", dist: " + dist);
        // converge threshold for each center: 0.05
        converged = dist < 0.05d;
      }
      if (converged || context.getSuperstep() == context.getMaxIteration() - 1) {
        // converged or reach max iteration, output centers
        for (int i = 0; i < value.centers.size(); i++) {
          context.write(((Tuple) value.centers.get(i)).toArray());
        }
        // true means to terminate iteration
        return true;
      }
      // false means to continue iteration
      return false;
    }
  }
  private static void printUsage() {
    System.out.println("Usage: <in> <out> [Max iterations (default 30)]");
    System.exit(-1);
  }
  public static void main(String[] args) throws IOException {
    if (args.length < 2)
      printUsage();
    GraphJob job = new GraphJob();
    job.setGraphLoaderClass(KmeansVertexReader.class);
    job.setRuntimePartitioning(false);
    job.setVertexClass(KmeansVertex.class);
    job.setAggregatorClass(KmeansAggregator.class);
    job.addInput(TableInfo.builder().tableName(args[0]).build());
    job.addOutput(TableInfo.builder().tableName(args[1]).build());
    // default max iteration is 30
    job.setMaxIteration(30);
    if (args.length >= 3)
      job.setMaxIteration(Integer.parseInt(args[2]));
    long start = System.currentTimeMillis();
    job.run();
    System.out.println("Job Finished in "
        + (System.currentTimeMillis() - start) / 1000.0 + " seconds");
  }
}