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

Introduction

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

Prototype

int getRowDimension();

Source Link

Document

Returns the number of rows in the matrix.

Usage

From source file:com.clust4j.algo.preprocess.MeanCenterer.java

@Override
public MeanCenterer fit(RealMatrix data) {
    synchronized (fitLock) {
        final int m = data.getRowDimension();
        final int n = data.getColumnDimension();

        // need to mean center...
        this.means = new double[n];
        final double[][] y = data.getData();

        // First pass, compute mean...
        for (int j = 0; j < n; j++) {
            for (int i = 0; i < m; i++) {
                means[j] += y[i][j];/*  w w  w  .  j  a  va  2  s  .c o  m*/

                // if last:
                if (i == m - 1) {
                    means[j] /= (double) m;
                }
            }
        }

        return this;
    }
}

---

From source file:com.clust4j.algo.preprocess.MinMaxScaler.java

@Override
public MinMaxScaler fit(RealMatrix X) {
    synchronized (fitLock) {
        final int m = X.getRowDimension();
        final int n = X.getColumnDimension();

        this.mins = new double[n];
        this.maxes = new double[n];
        double[][] data = X.getData();

        for (int col = 0; col < n; col++) {
            double mn = Double.POSITIVE_INFINITY, mx = Double.NEGATIVE_INFINITY;

            for (int row = 0; row < m; row++) {
                mn = FastMath.min(mn, data[row][col]);
                mx = FastMath.max(mx, data[row][col]);
            }//from  w w  w. j  av a 2  s. c o  m

            this.mins[col] = mn;
            this.maxes[col] = mx;
        }

        return this;
    }
}

---

From source file:edu.cudenver.bios.matrix.test.TestMatrixOrthonormalization.java

/**
 * Verify that the A = QxR for the Q, R matrices produced by the
 * orthonormalization//from  w w w.  ja v a  2s  . c o m
 */
public void testQRshouldBeA() {
    RealMatrix Q = norm.getQ();
    RealMatrix R = norm.getR();

    // verify that QR = A
    RealMatrix shouldBeOriginalMatrix = Q.multiply(R);
    // Make sure that QR has same dimension as original A matrix
    if (shouldBeOriginalMatrix.getRowDimension() != data.length
            || shouldBeOriginalMatrix.getColumnDimension() != data[0].length) {
        fail();
    }
    // make sure elements of QR match elements of A (within tolerance)
    for (int r = 0; r < shouldBeOriginalMatrix.getRowDimension(); r++) {
        for (int c = 0; c < shouldBeOriginalMatrix.getColumnDimension(); c++) {
            if (Precision.compareTo(shouldBeOriginalMatrix.getEntry(r, c), data[r][c], TOLERANCE) != 0)
                fail();
        }
    }

    assertTrue(true);
}

---

From source file:com.clust4j.algo.AbstractPartitionalClusterer.java

public AbstractPartitionalClusterer(RealMatrix data, BaseClustererParameters planner, final int k) {
    super(data, planner);

    if (k < 1)
        error(new IllegalArgumentException("k must exceed 0"));
    if (k > data.getRowDimension())
        error(new IllegalArgumentException("k exceeds number of records"));

    this.k = this.singular_value ? 1 : k;
    if (this.singular_value && k != 1) {
        warn("coerced k to 1 due to equality of all elements in input matrix");
    }/*from w ww  . jav a  2  s.c o m*/
}

---

From source file:eagle.security.userprofile.model.kde.UserProfileKDEModeler.java

private void computeProbabilityDensityEstimation(RealMatrix inputMat) {

    probabilityEstimation = new double[inputMat.getRowDimension()];
    for (int i = 0; i < probabilityEstimation.length; i++)
        probabilityEstimation[i] = 1.0;/*from www. j  av  a  2 s  .  co  m*/

    for (int i = 0; i < inputMat.getRowDimension(); i++) {
        for (int j = 0; j < inputMat.getColumnDimension(); j++) {
            if (statistics[j].getStddev() > 0) {
                double stddev = statistics[j].getStddev();
                double mean = statistics[j].getMean();
                double sqrt2PI = Math.sqrt(2.0 * Math.PI);
                double denominatorFirstPart = sqrt2PI * stddev;
                double squareMeanNormal = Math.pow((inputMat.getEntry(i, j) - mean), 2);
                double twoPowStandardDev = Math.pow(stddev, 2);
                double twoTimesTwoPowStandardDev = 2.0 * twoPowStandardDev;
                probabilityEstimation[i] *= ((1.00 / denominatorFirstPart)
                        * (Math.exp(-(squareMeanNormal / twoTimesTwoPowStandardDev))));
            }
        }
    }

    java.util.List<Double> listProb = new ArrayList<Double>();
    for (int i = 0; i < probabilityEstimation.length; i++) {
        probabilityEstimation[i] = Math.log10(probabilityEstimation[i]);
        listProb.add(probabilityEstimation[i]);
    }

    Collections.sort(listProb);
    int i = 0;
    for (double d : listProb)
        probabilityEstimation[i++] = d;

    minProbabilityEstimate = probabilityEstimation[probabilityEstimation.length - 1];
    maxProbabilityEstimate = probabilityEstimation[0];

    int len = probabilityEstimation.length;
    int nintyFivePercentIndex = (int) Math.round(0.05 * len);
    int medianPercentIndex = (int) Math.round(0.5 * len);
    if (medianPercentIndex >= len)
        medianProbabilityEstimate = probabilityEstimation[medianPercentIndex - 1];
    else
        medianProbabilityEstimate = probabilityEstimation[medianPercentIndex];
    nintyFivePercentileEstimate = probabilityEstimation[nintyFivePercentIndex];
}

---

From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement.java

private RealMatrix setCovarianceMatrixInternal(RealMatrix newValue) {
    if (newValue.getRowDimension() != alternatives.size()
            || newValue.getColumnDimension() != alternatives.size()) {
        throw new IllegalArgumentException(
                "Incorrect matrix size " + newValue.getRowDimension() + "x" + newValue.getColumnDimension()
                        + ", expected " + alternatives.size() + "x" + alternatives.size());
    }/*from   w ww . j  av a2s.  c o m*/
    try {
        mvgGenerator = random == null ? new MultivariateGaussianGenerator(meanVector, newValue, null)
                : random.createMultivariateGaussian(meanVector, newValue);
    } catch (Exception e) {
        throw new IllegalArgumentException(e);
    }
    RealMatrix oldValue = covarianceMatrix;
    covarianceMatrix = newValue;
    return oldValue;
}

---

From source file:com.clust4j.algo.preprocess.StandardScaler.java

@Override
public StandardScaler fit(RealMatrix data) {
    synchronized (fitLock) {
        final int m = data.getRowDimension();
        final int n = data.getColumnDimension();

        if (m < 2)
            throw new IllegalArgumentException(
                    "cannot " + "meaningfully compute standard deviation " + "on fewer than two observations");

        // need to mean center...
        this.means = new double[n];
        this.stdevs = new double[n];

        final double[][] X = data.getData();

        for (int col = 0; col < n; col++) {
            double var, std, mn;
            double sumSq = 0.0;
            double sum = 0.0;

            for (int row = 0; row < m; row++) {
                sumSq += X[row][col] * X[row][col];
                sum += X[row][col];/*from   w  w  w . ja v a2 s.  co  m*/
            }

            /*
             * A naive algorithm to calculate the estimated variance (1M):
             * 
             * Let n = 0, Sum = 0, SumSq = 0 
             * For each datum x: 
             *   n = n + 1 
             *   Sum = Sum + x 
             *   SumSq = SumSq + x * x 
             * Var = (SumSq - (Sum * Sum) / n) / (n - 1)
             * 
             * @see https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
             */
            var = (sumSq - (sum * sum) / (double) m) / ((double) m - 1.0);
            std = m < 2 ? Double.NaN : FastMath.sqrt(var);
            mn = sum / (double) m;

            means[col] = mn;
            stdevs[col] = std;
        }

        return this;
    }
}

---

From source file:net.myrrix.online.factorizer.als.NegativeInputTest.java

@Test
public void testALS() throws Exception {

    FastByIDMap<FastByIDFloatMap> byRow = new FastByIDMap<FastByIDFloatMap>();
    FastByIDMap<FastByIDFloatMap> byCol = new FastByIDMap<FastByIDFloatMap>();

    // Octave: R = [ 1 1 1 0 ; 0 -1 1 1 ; -1 0 0 1 ]
    MatrixUtils.addTo(0, 0, 1.0f, byRow, byCol);
    MatrixUtils.addTo(0, 1, 1.0f, byRow, byCol);
    MatrixUtils.addTo(0, 2, 1.0f, byRow, byCol);
    MatrixUtils.addTo(1, 1, -1.0f, byRow, byCol);
    MatrixUtils.addTo(1, 2, 1.0f, byRow, byCol);
    MatrixUtils.addTo(1, 3, 1.0f, byRow, byCol);
    MatrixUtils.addTo(2, 0, -1.0f, byRow, byCol);
    MatrixUtils.addTo(2, 3, 1.0f, byRow, byCol);

    // Octave: Y = [ 0.1 0.2 ; 0.2 0.5 ; 0.3 0.1 ; 0.2 0.2 ];
    FastByIDMap<float[]> previousY = new FastByIDMap<float[]>();
    previousY.put(0L, new float[] { 0.1f, 0.2f });
    previousY.put(1L, new float[] { 0.2f, 0.5f });
    previousY.put(2L, new float[] { 0.3f, 0.1f });
    previousY.put(3L, new float[] { 0.2f, 0.2f });

    MatrixFactorizer als = new AlternatingLeastSquares(byRow, byCol, 2, 0.0001, 40);
    als.setPreviousY(previousY);/*from w w  w .  j a v a  2  s  .  co m*/
    als.call();

    RealMatrix product = MatrixUtils.multiplyXYT(als.getX(), als.getY());

    StringBuilder productString = new StringBuilder(100);
    for (int row = 0; row < product.getRowDimension(); row++) {
        productString.append('\n').append(Arrays.toString(doubleToFloatArray(product.getRow(row))));
    }
    log.info("{}", productString);

    assertArrayEquals(new float[] { 0.899032f, 0.900162f, 0.990150f, -0.026642f }, product.getRow(0));
    assertArrayEquals(new float[] { 0.181214f, 0.089988f, 0.787198f, 1.012226f }, product.getRow(1));
    assertArrayEquals(new float[] { -0.104165f, -0.178240f, 0.360391f, 0.825856f }, product.getRow(2));
}

---

From source file:com.joptimizer.functions.SDPLogarithmicBarrier.java

/**
 * Build the genaralized logarithmic barrier function for the constraint
 * <br>G + Sum[x_i * F_i(x),i] < 0,  F_i, G symmetric matrices
 * @param Fi symmetric matrices//  www.j a  va 2  s . c o  m
 * @param G symmetric matrix
 */
public SDPLogarithmicBarrier(List<double[][]> FiMatrixList, double[][] GMatrix) {
    int dim = FiMatrixList.size();
    this.Fi = new RealMatrix[dim];
    RealMatrix Gg = new Array2DRowRealMatrix(GMatrix);
    if (Gg.getRowDimension() != Gg.getColumnDimension()) {
        throw new IllegalArgumentException("All matrices must be symmetric");
    }
    this.G = Gg;
    int pp = G.getRowDimension();
    for (int i = 0; i < dim; i++) {
        double[][] FiMatrix = FiMatrixList.get(i);
        RealMatrix Fii = new Array2DRowRealMatrix(FiMatrix);
        if (Fii.getRowDimension() != Fii.getColumnDimension() || pp != Fii.getRowDimension()) {
            throw new IllegalArgumentException("All matrices must be symmetric and with the same dimensions");
        }
        this.Fi[i] = Fii;
    }
    this.dim = dim;
    this.p = pp;
}

---

From source file:eagle.security.userprofile.model.kde.UserProfileKDEModeler.java

@Override
public List<UserProfileKDEModel> generate(String site, String user, RealMatrix matrix) {
    LOG.info(String.format("Receive aggregated user activity matrix: %s size: %s x %s", user,
            matrix.getRowDimension(), matrix.getColumnDimension()));
    computeStats(matrix);//  www.j  a  v  a 2  s .co  m
    computeProbabilityDensityEstimation(matrix);
    UserProfileKDEModel userprofileKDEModel = new UserProfileKDEModel(System.currentTimeMillis(), site, user,
            statistics, minProbabilityEstimate, maxProbabilityEstimate, nintyFivePercentileEstimate,
            medianProbabilityEstimate);
    return Arrays.asList(userprofileKDEModel);
}

---

• «
• 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
• »