Examples of org.apache.mahout.math.RandomTrinaryMatrix

org.apache.mahout.math.RandomTrinaryMatrix
Random matrix. Each value is taken from {-1,0,1} with roughly equal probability. Note that by default, the value is determined by a relatively simple hash of the coordinates. Such a hash is not usable where real randomness is required, but suffices nicely for random projection methods. If the simple hash method is not satisfactory, an optional high quality mode is available which uses a murmur hash of the coordinates.

  private final Matrix b;




  public SequentialBigSvd(Matrix A, int p) {
    // Y = A * \Omega
    y = A.times(new RandomTrinaryMatrix(A.columnSize(), p));


    // R'R = Y' Y
    cd1 = new CholeskyDecomposition(y.transpose().times(y));


    // B = Q" A = (Y R^{-1} )' A

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    Matrix v2 = s.getV().viewPart(0, 20, 0, 3).assign(Functions.ABS);
    assertEquals(v1, v2);
  }


  private static Matrix lowRankMatrix() {
    Matrix u = new RandomTrinaryMatrix(1, 20, 4, false);
    Matrix d = new DiagonalMatrix(new double[]{5, 3, 1, 0.5});
    Matrix v = new RandomTrinaryMatrix(2, 23, 4, false);


    return u.times(d).times(v.transpose());
  }

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      } finally {
        in.close();
      }


      Matrix aI = m.get();
      Matrix omega = new RandomTrinaryMatrix(seed, aI.columnSize(), internalDimension, false);
      Matrix y = aI.times(omega);


      if (y2 == null) {
        y2 = y.transpose().times(y);
      } else {
        y2.assign(y.transpose().times(y), Functions.PLUS);
      }
    }
    r2 = new CholeskyDecomposition(y2);


    // step 2, compute B
    int ncols = 0;
    for (File file : partsOfA) {
      MatrixWritable m = new MatrixWritable();
      DataInputStream in = new DataInputStream(new FileInputStream(file));
      try {
        m.readFields(in);
      } finally {
        in.close();
      }
      Matrix aI = m.get();
      ncols = Math.max(ncols, aI.columnSize());


      Matrix omega = new RandomTrinaryMatrix(seed, aI.numCols(), internalDimension, false);
      for (int j = 0; j < aI.numCols(); j += columnsPerSlice) {
        Matrix yI = aI.times(omega);
        Matrix aIJ = aI.viewPart(0, aI.rowSize(), j, Math.min(columnsPerSlice, aI.columnSize() - j));
        Matrix bIJ = r2.solveRight(yI).transpose().times(aIJ);
        addToSavedCopy(bFile(tmpDir, j), bIJ);

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    for (File file : partsOfA) {
      MatrixWritable m = new MatrixWritable();
      m.readFields(new DataInputStream(new FileInputStream(file)));
      Matrix aI = m.get();


      Matrix y = aI.times(new RandomTrinaryMatrix(seed, aI.numCols(), dim, false));
      Matrix uI = r2.solveRight(y).times(svd.getU());
      m.set(uI);
      DataOutputStream out = new DataOutputStream(new FileOutputStream(
          new File(tmpDir, String.format("U-%s", file.getName().replaceAll(".*-", "")))));
      try {

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    assertEquals(v1, v2);
  }


  private static Matrix lowRankMatrix(File tmpDir, String aBase, int rowsPerSlice, int rows, int columns) throws IOException {
    int rank = 10;
    Matrix u = new RandomTrinaryMatrix(1, rows, rank, false);
    Matrix d = new DenseMatrix(rank, rank);
    d.set(0, 0, 5);
    d.set(1, 1, 3);
    d.set(2, 2, 1);
    d.set(3, 3, 0.5);
    Matrix v = new RandomTrinaryMatrix(2, columns, rank, false);
    Matrix a = u.times(d).times(v.transpose());


    if (tmpDir != null) {
      for (int i = 0; i < a.rowSize(); i += rowsPerSlice) {
        MatrixWritable m = new MatrixWritable(a.viewPart(i, Math.min(a.rowSize() - i, rowsPerSlice), 0, a.columnSize()));
        DataOutputStream out = new DataOutputStream(new FileOutputStream(new File(tmpDir, String.format("%s-%09d", aBase, i))));

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      } finally {
        in.close();
      }


      Matrix aI = m.get();
      Matrix omega = new RandomTrinaryMatrix(seed, aI.columnSize(), internalDimension, false);
      Matrix y = aI.times(omega);


      if (y2 == null) {
        y2 = y.transpose().times(y);
      } else {
        y2.assign(y.transpose().times(y), Functions.PLUS);
      }
    }
    r2 = new CholeskyDecomposition(y2);


    // step 2, compute B
    int ncols = 0;
    for (File file : partsOfA) {
      MatrixWritable m = new MatrixWritable();
      DataInputStream in = new DataInputStream(new FileInputStream(file));
      try {
        m.readFields(in);
      } finally {
        in.close();
      }
      Matrix aI = m.get();
      ncols = Math.max(ncols, aI.columnSize());


      Matrix omega = new RandomTrinaryMatrix(seed, aI.numCols(), internalDimension, false);
      for (int j = 0; j < aI.numCols(); j += columnsPerSlice) {
        Matrix yI = aI.times(omega);
        Matrix aIJ = aI.viewPart(0, aI.rowSize(), j, Math.min(columnsPerSlice, aI.columnSize() - j));
        Matrix bIJ = r2.solveRight(yI).transpose().times(aIJ);
        addToSavedCopy(bFile(tmpDir, j), bIJ);

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    for (File file : partsOfA) {
      MatrixWritable m = new MatrixWritable();
      m.readFields(new DataInputStream(new FileInputStream(file)));
      Matrix aI = m.get();


      Matrix y = aI.times(new RandomTrinaryMatrix(seed, aI.numCols(), dim, false));
      Matrix uI = r2.solveRight(y).times(svd.getU());
      m.set(uI);
      DataOutputStream out = new DataOutputStream(new FileOutputStream(
          new File(tmpDir, String.format("U-%s", file.getName().replaceAll(".*-", "")))));
      try {

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      } finally {
        in.close();
      }


      Matrix aI = m.get();
      Matrix omega = new RandomTrinaryMatrix(seed, aI.columnSize(), internalDimension, false);
      Matrix y = aI.times(omega);


      if (y2 == null) {
        y2 = y.transpose().times(y);
      } else {
        y2.assign(y.transpose().times(y), Functions.PLUS);
      }
    }
    r2 = new CholeskyDecomposition(y2);


    // step 2, compute B
    int ncols = 0;
    for (File file : partsOfA) {
      MatrixWritable m = new MatrixWritable();
      final DataInputStream in = new DataInputStream(new FileInputStream(file));
      try {
        m.readFields(in);
      } finally {
        in.close();
      }
      Matrix aI = m.get();
      ncols = Math.max(ncols, aI.columnSize());


      Matrix omega = new RandomTrinaryMatrix(seed, aI.numCols(), internalDimension, false);
      for (int j = 0; j < aI.numCols(); j += columnsPerSlice) {
        Matrix yI = aI.times(omega);
        Matrix aIJ = aI.viewPart(0, aI.rowSize(), j, Math.min(columnsPerSlice, aI.columnSize() - j));
        Matrix bIJ = r2.solveRight(yI).transpose().times(aIJ);
        addToSavedCopy(bFile(tmpDir, j), bIJ);

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    for (File file : partsOfA) {
      MatrixWritable m = new MatrixWritable();
      m.readFields(new DataInputStream(new FileInputStream(file)));
      Matrix aI = m.get();


      Matrix y = aI.times(new RandomTrinaryMatrix(seed, aI.numCols(), dim, false));
      Matrix uI = r2.solveRight(y).times(svd.getU());
      m.set(uI);
      final DataOutputStream out = new DataOutputStream(new FileOutputStream(new File(tmpDir, String.format("U-%s", file.getName().replaceAll(".*-", "")))));
      try {
        m.write(out);

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    assertEquals(v1, v2);
  }


  private static Matrix lowRankMatrix(File tmpDir, String aBase, int rowsPerSlice, int rows, int columns) throws IOException {
    int rank = 10;
    Matrix u = new RandomTrinaryMatrix(1, rows, rank, false);
    Matrix d = new DenseMatrix(rank, rank);
    d.set(0, 0, 5);
    d.set(1, 1, 3);
    d.set(2, 2, 1);
    d.set(3, 3, 0.5);
    Matrix v = new RandomTrinaryMatrix(2, columns, rank, false);
    Matrix a = u.times(d).times(v.transpose());


    if (tmpDir != null) {
      for (int i = 0; i < a.rowSize(); i += rowsPerSlice) {
        MatrixWritable m = new MatrixWritable(a.viewPart(i, Math.min(a.rowSize() - i, rowsPerSlice), 0, a.columnSize()));
        DataOutputStream out = new DataOutputStream(new FileOutputStream(new File(tmpDir, String.format("%s-%09d", aBase, i))));

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org.apache.mahout.math.ssvd.SequentialBigSvd

org.apache.mahout.math.ssvd.SequentialBigSvdTest

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org.apache.mahout.math.ssvd.SequentialOutOfCoreSvdTest

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