@Override
  public void run() {
    Database database = input.getDatabase();
    Relation<O> relation = database.getRelation(distance.getInputTypeRestriction());
    DistanceQuery<O> distanceQuery = database.getDistanceQuery(relation, distance);
    KNNQuery<O> knnQ = database.getKNNQuery(distanceQuery, DatabaseQuery.HINT_HEAVY_USE);

    // open file.
    try (RandomAccessFile file = new RandomAccessFile(out, "rw");
        FileChannel channel = file.getChannel();
        // and acquire a file write lock
        FileLock lock = channel.lock()) {
      // write magic header
      file.writeInt(KNN_CACHE_MAGIC);

      int bufsize = k * 12 * 2 + 10; // Initial size, enough for 2 kNN.
      ByteBuffer buffer = ByteBuffer.allocateDirect(bufsize);

      FiniteProgress prog =
          LOG.isVerbose() ? new FiniteProgress("Computing kNN", relation.size(), LOG) : null;

      for (DBIDIter it = relation.iterDBIDs(); it.valid(); it.advance()) {
        final KNNList nn = knnQ.getKNNForDBID(it, k);
        final int nnsize = nn.size();

        // Grow the buffer when needed:
        if (nnsize * 12 + 10 > bufsize) {
          while (nnsize * 12 + 10 > bufsize) {
            bufsize <<= 1;
          }
          buffer = ByteBuffer.allocateDirect(bufsize);
        }

        buffer.clear();
        ByteArrayUtil.writeUnsignedVarint(buffer, it.internalGetIndex());
        ByteArrayUtil.writeUnsignedVarint(buffer, nnsize);
        int c = 0;
        for (DoubleDBIDListIter ni = nn.iter(); ni.valid(); ni.advance(), c++) {
          ByteArrayUtil.writeUnsignedVarint(buffer, ni.internalGetIndex());
          buffer.putDouble(ni.doubleValue());
        }
        if (c != nn.size()) {
          throw new AbortException("Sizes did not agree. Cache is invalid.");
        }

        buffer.flip();
        channel.write(buffer);
        LOG.incrementProcessed(prog);
      }
      LOG.ensureCompleted(prog);
      lock.release();
    } catch (IOException e) {
      LOG.exception(e);
    }
    // FIXME: close!
  }
示例#2
0
  @Override
  public void processNewResult(ResultHierarchy hier, Result result) {
    List<Clustering<?>> crs = ResultUtil.getClusteringResults(result);
    if (crs.size() < 1) {
      return;
    }
    Database db = ResultUtil.findDatabase(hier);
    Relation<O> rel = db.getRelation(distance.getInputTypeRestriction());
    DistanceQuery<O> dq = db.getDistanceQuery(rel, distance);

    for (Clustering<?> c : crs) {
      evaluateClustering(db, rel, dq, c);
    }
  }