Examples with Matrix Jama.Matrix used on opensource projects

Example 6 with Matrix

use of Jama.Matrix in project knime-core by knime.

the class PCAReverseNodeModel method execute.

/**
 * Performs the PCA.
 *
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    final PCAModelPortObject model = (PCAModelPortObject) inData[MODEL_INPORT];
    final Matrix eigenvectors = EigenValue.getSortedEigenVectors(model.getEigenVectors(), model.getEigenvalues(), m_inputColumnIndices.length);
    if (m_failOnMissingValues.getBooleanValue()) {
        for (final DataRow row : (DataTable) inData[DATA_INPORT]) {
            for (int i = 0; i < m_inputColumnIndices.length; i++) {
                if (row.getCell(m_inputColumnIndices[i]).isMissing()) {
                    throw new IllegalArgumentException("data table contains missing values");
                }
            }
        }
    }
    final String[] origColumnNames = ((PCAModelPortObjectSpec) ((PCAModelPortObject) inData[MODEL_INPORT]).getSpec()).getColumnNames();
    final DataColumnSpec[] specs = createAddTableSpec((DataTableSpec) inData[DATA_INPORT].getSpec(), origColumnNames);
    final CellFactory fac = new CellFactory() {

        @Override
        public DataCell[] getCells(final DataRow row) {
            return convertInputRow(eigenvectors, row, model.getCenter(), m_inputColumnIndices, origColumnNames.length);
        }

        @Override
        public DataColumnSpec[] getColumnSpecs() {
            return specs;
        }

        @Override
        public void setProgress(final int curRowNr, final int rowCount, final RowKey lastKey, final ExecutionMonitor texec) {
            texec.setProgress((double) curRowNr / rowCount);
        }
    };
    final ColumnRearranger cr = new ColumnRearranger((DataTableSpec) inData[DATA_INPORT].getSpec());
    cr.append(fac);
    if (m_removePCACols.getBooleanValue()) {
        cr.remove(m_inputColumnIndices);
    }
    final BufferedDataTable result = exec.createColumnRearrangeTable((BufferedDataTable) inData[DATA_INPORT], cr, exec);
    final PortObject[] out = { result };
    return out;
}

Example 7 with Matrix

use of Jama.Matrix in project knime-core by knime.

the class PCAApplyNodeModel method execute.

/**
 * Performs the PCA.
 *
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    final PCAModelPortObject model = (PCAModelPortObject) inData[MODEL_INPORT];
    final int dimensions = m_dimSelection.getNeededDimensions();
    if (dimensions == -1) {
        throw new IllegalArgumentException("Number of dimensions not correct configured");
    }
    if (m_failOnMissingValues.getBooleanValue()) {
        for (final DataRow row : (DataTable) inData[DATA_INPORT]) {
            for (int i = 0; i < m_inputColumnIndices.length; i++) {
                if (row.getCell(m_inputColumnIndices[i]).isMissing()) {
                    throw new IllegalArgumentException("data table contains missing values");
                }
            }
        }
    }
    final Matrix eigenvectors = EigenValue.getSortedEigenVectors(model.getEigenVectors(), model.getEigenvalues(), dimensions);
    final DataColumnSpec[] specs = PCANodeModel.createAddTableSpec((DataTableSpec) inData[DATA_INPORT].getSpec(), dimensions);
    final int dim = dimensions;
    final CellFactory fac = new CellFactory() {

        @Override
        public DataCell[] getCells(final DataRow row) {
            return PCANodeModel.convertInputRow(eigenvectors, row, model.getCenter(), m_inputColumnIndices, dim, m_failOnMissingValues.getBooleanValue());
        }

        @Override
        public DataColumnSpec[] getColumnSpecs() {
            return specs;
        }

        @Override
        public void setProgress(final int curRowNr, final int rowCount, final RowKey lastKey, final ExecutionMonitor texec) {
            texec.setProgress((double) curRowNr / rowCount, "converting input row " + curRowNr + " of " + rowCount);
        }
    };
    final ColumnRearranger cr = new ColumnRearranger((DataTableSpec) inData[DATA_INPORT].getSpec());
    cr.append(fac);
    if (m_removeOriginalCols.getBooleanValue()) {
        cr.remove(m_inputColumnNames);
    }
    final BufferedDataTable result = exec.createColumnRearrangeTable((BufferedDataTable) inData[DATA_INPORT], cr, exec);
    final PortObject[] out = { result };
    return out;
}

Example 8 with Matrix

use of Jama.Matrix in project h2o-2 by h2oai.

the class Gram method cholesky.

/**
   * Compute the cholesky decomposition.
   *
   * In case our gram starts with diagonal submatrix of dimension N, we exploit this fact to reduce the complexity of the problem.
   * We use the standard decomposition of the cholesky factorization into submatrices.
   *
   * We split the Gram into 3 regions (4 but we only consider lower diagonal, sparse means diagonal region in this context):
   *     diagonal
   *     diagonal*dense
   *     dense*dense
   * Then we can solve the cholesky in 3 steps:
   *  1. We solve the diagnonal part right away (just do the sqrt of the elements).
   *  2. The diagonal*dense part is simply divided by the sqrt of diagonal.
   *  3. Compute Cholesky of dense*dense - outer product of cholesky of diagonal*dense computed in previous step
   *
   * @param chol
   * @return
   */
public Cholesky cholesky(Cholesky chol, boolean parallelize, String id) {
    long start = System.currentTimeMillis();
    if (chol == null) {
        double[][] xx = _xx.clone();
        for (int i = 0; i < xx.length; ++i) xx[i] = xx[i].clone();
        chol = new Cholesky(xx, _diag.clone());
    }
    final Cholesky fchol = chol;
    final int sparseN = _diag.length;
    final int denseN = _fullN - sparseN;
    // compute the cholesky of the diagonal and diagonal*dense parts
    if (_diag != null)
        for (int i = 0; i < sparseN; ++i) {
            double d = 1.0 / (chol._diag[i] = Math.sqrt(_diag[i]));
            for (int j = 0; j < denseN; ++j) chol._xx[j][i] = d * _xx[j][i];
        }
    ForkJoinTask[] fjts = new ForkJoinTask[denseN];
    // compute the outer product of diagonal*dense
    //Log.info("SPARSEN = " + sparseN + "    DENSEN = " + denseN);
    final int[][] nz = new int[denseN][];
    for (int i = 0; i < denseN; ++i) {
        final int fi = i;
        fjts[i] = new RecursiveAction() {

            @Override
            protected void compute() {
                int[] tmp = new int[sparseN];
                double[] rowi = fchol._xx[fi];
                int n = 0;
                for (int k = 0; k < sparseN; ++k) if (rowi[k] != .0)
                    tmp[n++] = k;
                nz[fi] = Arrays.copyOf(tmp, n);
            }
        };
    }
    ForkJoinTask.invokeAll(fjts);
    for (int i = 0; i < denseN; ++i) {
        final int fi = i;
        fjts[i] = new RecursiveAction() {

            @Override
            protected void compute() {
                double[] rowi = fchol._xx[fi];
                int[] nzi = nz[fi];
                for (int j = 0; j <= fi; ++j) {
                    double[] rowj = fchol._xx[j];
                    int[] nzj = nz[j];
                    double s = 0;
                    for (int t = 0, z = 0; t < nzi.length && z < nzj.length; ) {
                        int k1 = nzi[t];
                        int k2 = nzj[z];
                        if (k1 < k2) {
                            t++;
                            continue;
                        } else if (k1 > k2) {
                            z++;
                            continue;
                        } else {
                            s += rowi[k1] * rowj[k1];
                            t++;
                            z++;
                        }
                    }
                    rowi[j + sparseN] = _xx[fi][j + sparseN] - s;
                }
            }
        };
    }
    ForkJoinTask.invokeAll(fjts);
    // compute the cholesky of dense*dense-outer_product(diagonal*dense)
    // TODO we still use Jama, which requires (among other things) copy and expansion of the matrix. Do it here without copy and faster.
    double[][] arr = new double[denseN][];
    for (int i = 0; i < arr.length; ++i) arr[i] = Arrays.copyOfRange(fchol._xx[i], sparseN, sparseN + denseN);
    //    Log.info(id + ": CHOLESKY PRECOMPUTE TIME " + (System.currentTimeMillis() - start));
    start = System.currentTimeMillis();
    // parallelize cholesky
    if (parallelize) {
        int p = Runtime.getRuntime().availableProcessors();
        InPlaceCholesky d = InPlaceCholesky.decompose_2(arr, 10, p);
        fchol.setSPD(d.isSPD());
        arr = d.getL();
    //      Log.info (id + ": H2O CHOLESKY DECOMP TAKES: " + (System.currentTimeMillis()-start));
    } else {
        // make it symmetric
        for (int i = 0; i < arr.length; ++i) for (int j = 0; j < i; ++j) arr[j][i] = arr[i][j];
        CholeskyDecomposition c = new Matrix(arr).chol();
        fchol.setSPD(c.isSPD());
        arr = c.getL().getArray();
    //Log.info ("JAMA CHOLESKY DECOMPOSE TAKES: " + (System.currentTimeMillis()-start));
    }
    for (int i = 0; i < arr.length; ++i) System.arraycopy(arr[i], 0, fchol._xx[i], sparseN, i + 1);
    return chol;
}

Example 9 with Matrix

use of Jama.Matrix in project h2o-2 by h2oai.

the class PCA method buildModel.

public PCAModel buildModel(DataInfo dinfo, GramTask tsk) {
    logStart();
    // X'X/n where n = num rows
    Matrix myGram = new Matrix(tsk._gram.getXX());
    SingularValueDecomposition mySVD = myGram.svd();
    // Extract eigenvalues and eigenvectors
    // Note: Singular values ordered in weakly descending order by algorithm
    double[] Sval = mySVD.getSingularValues();
    // rows = features, cols = principal components
    double[][] eigVec = mySVD.getV().getArray();
    assert Sval.length == eigVec.length;
    // DKV.put(EigenvectorMatrix.makeKey(input("source"), destination_key), new EigenvectorMatrix(eigVec));
    // Compute standard deviation
    double[] sdev = new double[Sval.length];
    double totVar = 0;
    double dfcorr = dinfo._adaptedFrame.numRows() / (dinfo._adaptedFrame.numRows() - 1.0);
    for (int i = 0; i < Sval.length; i++) {
        // if(standardize)
        // Correct since degrees of freedom = n-1
        Sval[i] = dfcorr * Sval[i];
        sdev[i] = Math.sqrt(Sval[i]);
        totVar += Sval[i];
    }
    // Proportion of total variance
    double[] propVar = new double[Sval.length];
    // Cumulative proportion of total variance
    double[] cumVar = new double[Sval.length];
    for (int i = 0; i < Sval.length; i++) {
        propVar[i] = Sval[i] / totVar;
        cumVar[i] = i == 0 ? propVar[0] : cumVar[i - 1] + propVar[i];
    }
    Key dataKey = input("source") == null ? null : Key.make(input("source"));
    int ncomp = Math.min(getNumPC(sdev, tolerance), max_pc);
    return new PCAModel(this, destination_key, dataKey, dinfo, tsk, sdev, propVar, cumVar, eigVec, mySVD.rank(), ncomp);
}

Example 10 with Matrix

use of Jama.Matrix in project h2o-3 by h2oai.

the class LinearAlgebraUtils method multiple.

static double[] multiple(double[] diagYY, /*diagonal*/
int nTot, int nVars) {
    int ny = diagYY.length;
    for (int i = 0; i < ny; i++) {
        diagYY[i] *= nTot;
    }
    double[][] uu = new double[ny][ny];
    for (int i = 0; i < ny; i++) {
        for (int j = 0; j < ny; j++) {
            double yyij = i == j ? diagYY[i] : 0;
            uu[i][j] = (yyij - diagYY[i] * diagYY[j] / nTot) / (nVars * Math.sqrt(diagYY[i] * diagYY[j]));
            if (Double.isNaN(uu[i][j])) {
                uu[i][j] = 0;
            }
        }
    }
    EigenvalueDecomposition eigen = new EigenvalueDecomposition(new Matrix(uu));
    double[] eigenvalues = eigen.getRealEigenvalues();
    double[][] eigenvectors = eigen.getV().getArray();
    int maxIndex = ArrayUtils.maxIndex(eigenvalues);
    return eigenvectors[maxIndex];
}