Example usage for org.apache.commons.math3.linear RealMatrix getColumnDimension

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealMatrix getColumnDimension.

Prototype

int getColumnDimension();

Source Link

Document

Returns the number of columns in the matrix.

Usage

From source file:org.knime.al.util.noveltydetection.knfst.MatrixFunctions.java

public static RealMatrix concatVertically(final RealMatrix top, final RealMatrix bottom) {
    if (top.getColumnDimension() != bottom.getColumnDimension()) {
        throw new IllegalArgumentException("The matrices must have the same column dimension!");
    }//from  w w w.  j a  va2  s. c  om

    final double[][] result = new double[top.getRowDimension() + bottom.getRowDimension()][top
            .getColumnDimension()];

    final int tr = top.getRowDimension();

    for (int c = 0; c < top.getColumnDimension(); c++) {
        for (int r = 0; r < top.getRowDimension(); r++) {
            result[r][c] = top.getEntry(r, c);
        }
        for (int r = 0; r < bottom.getRowDimension(); r++) {
            result[tr + r][c] = bottom.getEntry(r, c);
        }
    }

    return MatrixUtils.createRealMatrix(result);
}

---

From source file:org.knime.al.util.noveltydetection.knfst.MultiClassKNFST.java

public MultiClassKNFST(final KernelCalculator kernel, final String[] labels, final ExecutionMonitor progMon)
        throws Exception {
    super(kernel);
    m_labels = labels;//ww  w .j a  v a  2 s. com

    final ExecutionMonitor kernelProgMon = progMon.createSubProgress(0.3);
    final ExecutionMonitor nullspaceProgMon = progMon.createSubProgress(0.7);
    final RealMatrix kernelMatrix = kernel.kernelize(kernelProgMon);

    // obtain unique class labels
    final ClassWrapper[] classes = ClassWrapper.classes(labels);

    // calculate projection of KNFST
    nullspaceProgMon.setMessage("Calculating nullspace projection");
    m_projection = projection(kernelMatrix, labels);

    nullspaceProgMon.setProgress(1.0, "Finished calculating nullspace projection");

    // calculate target points ( = projections of training data into the
    // null space)
    m_targetPoints = MatrixUtils.createRealMatrix(classes.length, m_projection.getColumnDimension());
    int n = 0;
    int nOld = 0;
    for (int c = 0; c < classes.length; c++) {
        n += classes[c].getCount();
        m_targetPoints.setRowVector(c, MatrixFunctions.columnMeans(kernelMatrix
                .getSubMatrix(nOld, n - 1, 0, kernelMatrix.getColumnDimension() - 1).multiply(m_projection)));
        nOld = n;
    }

    // set betweenClassDistances
    m_betweenClassDistances = MatrixFunctions.calculateRowVectorDistances(m_targetPoints);

}

---

From source file:org.knime.al.util.noveltydetection.knfst.MultiClassKNFST.java

public MultiClassKNFST(final RealMatrix kernelMatrix, final String[] labels) throws KNFSTException {
    m_labels = labels;/* w  ww  . jav a 2s  . c o  m*/
    // obtain unique class labels
    final ClassWrapper[] classes = ClassWrapper.classes(labels);

    // calculate projection of KNFST
    m_projection = projection(kernelMatrix, labels);

    // calculate target points ( = projections of training data into the
    // null space)
    m_targetPoints = MatrixUtils.createRealMatrix(classes.length, m_projection.getColumnDimension());
    int n = 0;
    int nOld = 0;
    for (int c = 0; c < classes.length; c++) {
        n += classes[c].getCount();
        m_targetPoints.setRowVector(c, MatrixFunctions.columnMeans(kernelMatrix
                .getSubMatrix(nOld, n - 1, 0, kernelMatrix.getColumnDimension() - 1).multiply(m_projection)));
        nOld = n;
    }

    // set betweenClassDistances
    m_betweenClassDistances = MatrixFunctions.calculateRowVectorDistances(m_targetPoints);
}

---

From source file:org.knime.al.util.noveltydetection.knfst.OneClassKNFST.java

public OneClassKNFST(final KernelCalculator kernel, final ExecutionMonitor progMon) throws Exception {
    super(kernel);

    final ExecutionMonitor kernelProgMon = progMon.createSubProgress(0.3);
    final ExecutionMonitor nullspaceProgMon = progMon.createSubProgress(0.7);

    // get number of training samples
    final RealMatrix kernelMatrix = m_kernel.kernelize(kernelProgMon);
    final int n = kernelMatrix.getRowDimension();

    // include dot products of training samples and the origin in feature
    // space (these dot products are always zero!)
    final RealMatrix k = MatrixFunctions.concatVertically(
            MatrixFunctions.concatHorizontally(kernelMatrix,
                    MatrixUtils.createRealMatrix(kernelMatrix.getRowDimension(), 1)),
            MatrixUtils.createRealMatrix(1, kernelMatrix.getColumnDimension() + 1));

    // create one-class labels + a different label for the origin
    final String[] labels = new String[n + 1];
    for (int l = 0; l <= n; l++) {
        labels[l] = (l == n) ? "0" : "1";
    }//from   w ww  . j ava 2  s  .c o  m

    // get model parameters
    nullspaceProgMon.setMessage("Calculating nullspace projection");
    final RealMatrix projection = projection(k, labels);
    nullspaceProgMon.setProgress(1.0, "Finished calculating nullspace projection");
    final int[] indices = new int[n];
    for (int i = 0; i < n; i++) {
        indices[i] = i;
    }
    m_targetPoints = MatrixUtils.createRowRealMatrix(MatrixFunctions
            .columnMeans(k.getSubMatrix(0, n - 1, 0, k.getColumnDimension() - 1).multiply(projection))
            .toArray());
    m_projection = projection.getSubMatrix(0, n - 1, 0, projection.getColumnDimension() - 1);
    m_betweenClassDistances = new double[] { Math.abs(m_targetPoints.getEntry(0, 0)) };
}

---

From source file:org.knime.al.util.noveltydetection.knfst.OneClassKNFST.java

public OneClassKNFST(final RealMatrix kernelMatrix) throws KNFSTException {
    final int n = kernelMatrix.getRowDimension();

    // include dot products of training samples and the origin in feature
    // space (these dot products are always zero!)
    final RealMatrix k = MatrixFunctions.concatVertically(
            MatrixFunctions.concatHorizontally(kernelMatrix,
                    MatrixUtils.createRealMatrix(kernelMatrix.getRowDimension(), 1)),
            MatrixUtils.createRealMatrix(1, kernelMatrix.getColumnDimension() + 1));

    // create one-class labels + a different label for the origin
    final String[] labels = new String[n + 1];
    for (int l = 0; l <= n; l++) {
        labels[l] = (l == n) ? "0" : "1";
    }/*from w ww  .  j  a v  a 2s. c o  m*/

    // get model parameters
    final RealMatrix projection = projection(k, labels);
    final int[] indices = new int[n];
    for (int i = 0; i < n; i++) {
        indices[i] = i;
    }
    m_targetPoints = MatrixUtils.createRowRealMatrix(MatrixFunctions
            .columnMeans(k.getSubMatrix(0, n - 1, 0, k.getColumnDimension() - 1).multiply(projection))
            .toArray());
    m_projection = projection.getSubMatrix(0, n - 1, 0, projection.getColumnDimension() - 1);
    m_betweenClassDistances = new double[] { Math.abs(m_targetPoints.getEntry(0, 0)) };
}

---

From source file:org.knime.al.util.noveltydetection.knfst.OneClassKNFST.java

private NoveltyScores score(final RealMatrix kernelMatrix) {
    // projected test samples:
    final RealMatrix projectionVectors = kernelMatrix.transpose().multiply(m_projection);

    // differences to the target value:
    final RealMatrix diff = projectionVectors.subtract(MatrixFunctions
            .ones(kernelMatrix.getColumnDimension(), 1).scalarMultiply(m_targetPoints.getEntry(0, 0)));

    // distances to the target value:
    final RealVector scoresVector = MatrixFunctions
            .sqrt(MatrixFunctions.rowSums(MatrixFunctions.multiplyElementWise(diff, diff)));

    return new NoveltyScores(scoresVector.toArray(), projectionVectors);
}

---

From source file:org.lenskit.mf.MFModel.java

/**
 * Construct a matrix factorization model.  The matrices are not copied, so the caller should
 * make sure they won't be modified by anyone else.
 *
 * @param umat The user feature matrix (users x features).
 * @param imat The item feature matrix (items x features).
 * @param uidx The user index mapping./*from w  w w  .  j  ava2s .  c  o m*/
 * @param iidx The item index mapping.
 */
public MFModel(RealMatrix umat, RealMatrix imat, KeyIndex uidx, KeyIndex iidx) {
    Preconditions.checkArgument(umat.getColumnDimension() == imat.getColumnDimension(),
            "mismatched matrix sizes");
    featureCount = umat.getColumnDimension();
    userCount = uidx.size();
    itemCount = iidx.size();
    Preconditions.checkArgument(umat.getRowDimension() == userCount, "user matrix has %s rows, expected %s",
            umat.getRowDimension(), userCount);
    Preconditions.checkArgument(imat.getRowDimension() == itemCount, "item matrix has %s rows, expected %s",
            imat.getRowDimension(), itemCount);
    userMatrix = umat;
    itemMatrix = imat;
    userIndex = uidx;
    itemIndex = iidx;
}

---

From source file:org.lenskit.util.math.RowView.java

RowView(RealMatrix mat, int r) {
    this(mat, r, 0, mat.getColumnDimension());
}

---

From source file:org.meteoinfo.math.linalg.LinalgUtil.java

/**
 * Calculates the Cholesky decomposition of a matrix.
 * The Cholesky decomposition of a real symmetric positive-definite matrix A consists of a 
 * lower triangular matrix L with same size such that: A = LLT. In a sense, this is 
 * the square root of A./*from w w w .  ja v a2  s  .c o  m*/
 * @param a The given matrix.
 * @return Result array.
 */
public static Array cholesky(Array a) {
    Array r = Array.factory(DataType.DOUBLE, a.getShape());
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    CholeskyDecomposition decomposition = new CholeskyDecomposition(matrix);
    RealMatrix L = decomposition.getL();
    int n = L.getColumnDimension();
    int m = L.getRowDimension();
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            r.setDouble(i * n + j, L.getEntry(i, j));
        }
    }

    return r;
}

---

From source file:org.meteoinfo.math.linalg.LinalgUtil.java

/**
 * Calculates the LUP-decomposition of a square matrix.
 * The LUP-decomposition of a matrix A consists of three matrices L, U and P that satisfy: 
 * PA = LU. L is lower triangular (with unit diagonal terms), U is upper triangular and P is 
 * a permutation matrix. All matrices are mm.
 * @param a Given matrix./* ww w  .j a  v  a 2  s  .  c o  m*/
 * @return Result P/L/U arrays.
 */
public static Array[] lu(Array a) {
    Array Pa = Array.factory(DataType.DOUBLE, a.getShape());
    Array La = Array.factory(DataType.DOUBLE, a.getShape());
    Array Ua = Array.factory(DataType.DOUBLE, a.getShape());
    double[][] aa = (double[][]) ArrayUtil.copyToNDJavaArray(a);
    RealMatrix matrix = new Array2DRowRealMatrix(aa, false);
    LUDecomposition decomposition = new LUDecomposition(matrix);
    RealMatrix P = decomposition.getP();
    RealMatrix L = decomposition.getL();
    RealMatrix U = decomposition.getU();
    int n = L.getColumnDimension();
    int m = L.getRowDimension();
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            Pa.setDouble(i * n + j, P.getEntry(i, j));
            La.setDouble(i * n + j, L.getEntry(i, j));
            Ua.setDouble(i * n + j, U.getEntry(i, j));
        }
    }

    return new Array[] { Pa, La, Ua };
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• 22
• 23
• 24
• 25
• 26
• 27
• 28
• 29
• 30
• 31
• 32
• »