Package org.apache.mahout.math.decomposer.lanczos

Examples of org.apache.mahout.math.decomposer.lanczos.LanczosState

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    state = new HdfsBackedLanczosState(corpus, rank,
        intitialVector, new Path(getTestTempDirPath(), "lanczosStateDir" + suf(symmetric) + counter));
    solver = new DistributedLanczosSolver();
    solver.solve(state, rank, symmetric);

    LanczosState allAtOnceState = doTestDistributedLanczosSolver(symmetric, rank, false);
    for(int i=0; i<state.getIterationNumber(); i++) {
      Vector v = state.getBasisVector(i).normalize();
      Vector w = allAtOnceState.getBasisVector(i).normalize();
      double diff = v.minus(w).norm(2);
      assertTrue("basis " + i + " is too long: " + diff, diff < 0.1);
    }
    counter++;
  }
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                             boolean isSymmetric,
                             int desiredRank,
                             String outputEigenVectorPathString) throws IOException {
    DistributedRowMatrix matrix = new DistributedRowMatrix(inputPath, outputTmpPath, numRows, numCols);
    matrix.setConf(new Configuration(originalConfig));
    LanczosState state = new LanczosState(matrix, desiredRank, getInitialVector(matrix));
    return runJob(originalConfig, state, desiredRank, isSymmetric, outputEigenVectorPathString);
  }
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                 boolean isSymmetric,
                 int desiredRank) throws Exception {
    DistributedRowMatrix matrix = new DistributedRowMatrix(inputPath, outputTmpPath, numRows, numCols);
    matrix.setConf(new Configuration(getConf() != null ? getConf() : new Configuration()));

    LanczosState state;
    if(workingDirPath == null) {
      state = new LanczosState(matrix, desiredRank, getInitialVector(matrix));
    } else {
      HdfsBackedLanczosState hState =
          new HdfsBackedLanczosState(matrix, desiredRank, getInitialVector(matrix), workingDirPath);
      hState.setConf(matrix.getConf());
      state = hState;
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    DistributedRowMatrix corpus = getCorpus(symmetric);
    Configuration conf = new Configuration();
    corpus.setConf(conf);
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    Vector intitialVector = solver.getInitialVector(corpus);
    LanczosState state;
    if(hdfsBackedState) {
      HdfsBackedLanczosState hState = new HdfsBackedLanczosState(corpus,
          desiredRank, intitialVector, new Path(getTestTempDirPath(),
              "lanczosStateDir" + suf(symmetric) + counter));
      hState.setConf(conf);
      state = hState;
    } else {
      state = new LanczosState(corpus, desiredRank, intitialVector);
    }
    solver.solve(state, desiredRank, symmetric);
    SolverTest.assertOrthonormal(state);
    for(int i = 0; i < desiredRank/2; i++) {
      SolverTest.assertEigen(i, state.getRightSingularVector(i), corpus, 0.1, symmetric);
    }
    counter++;
    return state;
  }
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              new Path(outputCalc, "laplacian-" + (System.nanoTime() & 0xFF)));
      L.setConf(new Configuration(conf));

      // eigendecomposition (step 3)
      int overshoot = (int) ((double) eigenrank * OVERSHOOT_MULTIPLIER);
      LanczosState state = new LanczosState(L, eigenrank,
          new DistributedLanczosSolver().getInitialVector(L));

      DistributedRowMatrix U = performEigenDecomposition(conf, L, state, eigenrank, overshoot, outputCalc);
      U.setConf(new Configuration(conf));
      List<Double> eigenValues = Lists.newArrayList();
      for(int i=0; i<eigenrank; i++) {
        eigenValues.set(i, state.getSingularValue(i));
      }

      // here's where things get interesting: steps 4, 5, and 6 are unique
      // to this algorithm, and depending on the final output, steps 1-3
      // may be repeated as well
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    // since some of the eigen-output is spurious and will be eliminated
    // upon verification, we have to aim to overshoot and then discard
    // unnecessary vectors later
    int overshoot = (int) ((double) clusters * OVERSHOOT_MULTIPLIER);
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    LanczosState state = new LanczosState(L, numDims, solver.getInitialVector(L));
    Path lanczosSeqFiles = new Path(outputCalc, "eigenvectors-" + (System.nanoTime() & 0xFF));
    solver.runJob(conf,
                  state,
                  overshoot,
                  true,
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              new Path(outputCalc, "laplacian-" + (System.nanoTime() & 0xFF)));
      L.setConf(new Configuration(conf));

      // eigendecomposition (step 3)
      int overshoot = (int) ((double) eigenrank * OVERSHOOT_MULTIPLIER);
      LanczosState state = new LanczosState(L, eigenrank,
          DistributedLanczosSolver.getInitialVector(L));

      DistributedRowMatrix U = performEigenDecomposition(conf, L, state, eigenrank, overshoot, outputCalc);
      U.setConf(new Configuration(conf));
      List<Double> eigenValues = Lists.newArrayList();
      for (int i = 0; i < eigenrank; i++) {
        eigenValues.set(i, state.getSingularValue(i));
      }

      // here's where things get interesting: steps 4, 5, and 6 are unique
      // to this algorithm, and depending on the final output, steps 1-3
      // may be repeated as well
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                             boolean isSymmetric,
                             int desiredRank,
                             String outputEigenVectorPathString) throws IOException {
    DistributedRowMatrix matrix = new DistributedRowMatrix(inputPath, outputTmpPath, numRows, numCols);
    matrix.setConf(new Configuration(originalConfig));
    LanczosState state = new LanczosState(matrix, desiredRank, getInitialVector(matrix));
    return runJob(originalConfig, state, desiredRank, isSymmetric, outputEigenVectorPathString);
  }
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                 boolean isSymmetric,
                 int desiredRank) throws Exception {
    DistributedRowMatrix matrix = new DistributedRowMatrix(inputPath, outputTmpPath, numRows, numCols);
    matrix.setConf(new Configuration(getConf() != null ? getConf() : new Configuration()));

    LanczosState state;
    if (workingDirPath == null) {
      state = new LanczosState(matrix, desiredRank, getInitialVector(matrix));
    } else {
      HdfsBackedLanczosState hState =
          new HdfsBackedLanczosState(matrix, desiredRank, getInitialVector(matrix), workingDirPath);
      hState.setConf(matrix.getConf());
      state = hState;
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      // since some of the eigen-output is spurious and will be eliminated
      // upon verification, we have to aim to overshoot and then discard
      // unnecessary vectors later
      int overshoot = Math.min((int) (clusters * OVERSHOOTMULTIPLIER), numDims);
      DistributedLanczosSolver solver = new DistributedLanczosSolver();
      LanczosState state = new LanczosState(L, overshoot, DistributedLanczosSolver.getInitialVector(L));
      Path lanczosSeqFiles = new Path(outputCalc, "eigenvectors");

      solver.runJob(conf, state, overshoot, true, lanczosSeqFiles.toString());

      // perform a verification
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Related Classes of org.apache.mahout.math.decomposer.lanczos.LanczosState

  • org.apache.mahout.clustering.spectral.eigencuts.EigencutsDriver
  • org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver
  • org.apache.mahout.math.DenseMatrix
  • org.apache.mahout.math.hadoop.decomposer.DistributedLanczosSolver
  • org.apache.mahout.math.hadoop.decomposer.TestDistributedLanczosSolver
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