Example usage for org.apache.commons.math3.linear MatrixUtils createRowRealMatrix

Introduction

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

Prototype

public static RealMatrix createRowRealMatrix(double[] rowData) throws NoDataException, NullArgumentException 

Source Link

Document

Create a row RealMatrix using the data from the input array.

Usage

From source file:com.yahoo.egads.utilities.SpectralMethods.java

public static RealMatrix createHankelMatrix(RealMatrix data, int windowSize) {

    int n = data.getRowDimension();
    int m = data.getColumnDimension();
    int k = n - windowSize + 1;

    RealMatrix res = MatrixUtils.createRealMatrix(k, m * windowSize);
    double[] buffer = {};

    for (int i = 0; i < n; ++i) {
        double[] row = data.getRow(i);
        buffer = ArrayUtils.addAll(buffer, row);

        if (i >= windowSize - 1) {
            RealMatrix mat = MatrixUtils.createRowRealMatrix(buffer);
            res.setRowMatrix(i - windowSize + 1, mat);
            buffer = ArrayUtils.subarray(buffer, m, buffer.length);
        }/*from w  w  w . j a  v a 2  s.  c o  m*/
    }

    return res;
}

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From source file:com.github.tteofili.looseen.yay.SGM.java

private RealMatrix[] initBiases() {

    RealMatrix[] initialBiases = new RealMatrix[weights.length];

    for (int i = 0; i < initialBiases.length; i++) {
        double[] data = new double[weights[i].getRowDimension()];
        Arrays.fill(data, 0.01d);
        RealMatrix matrix = MatrixUtils.createRowRealMatrix(data);

        initialBiases[i] = matrix;//from www .  j a v a  2  s  .c  o m
    }
    return initialBiases;
}

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From source file:com.github.tteofili.looseen.yay.SGM.java

/**
 * predict network output given an input
 *
 * @param input the input/*from w ww  .j a v  a 2s. c om*/
 * @return the output
 * @throws Exception
 */
private double[] predictOutput(double[] input) throws Exception {

    RealMatrix hidden = rectifierFunction.applyMatrix(
            MatrixUtils.createRowRealMatrix(input).multiply(weights[0].transpose()).add(biases[0]));
    RealMatrix scores = hidden.multiply(weights[1].transpose()).add(biases[1]);

    RealMatrix probs = scores.copy();
    int len = scores.getColumnDimension() - 1;
    for (int d = 0; d < configuration.window - 1; d++) {
        int startColumn = d * len / (configuration.window - 1);
        RealMatrix subMatrix = scores.getSubMatrix(0, scores.getRowDimension() - 1, startColumn,
                startColumn + input.length);
        for (int sm = 0; sm < subMatrix.getRowDimension(); sm++) {
            probs.setSubMatrix(softmaxActivationFunction.applyMatrix(subMatrix.getRowMatrix(sm)).getData(), sm,
                    startColumn);
        }
    }

    RealVector d = probs.getRowVector(0);
    return d.toArray();
}

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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  .  java 2s  .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)) };
}

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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 w w  .j  ava  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)) };
}

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