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:eagle.security.userprofile.model.kde.UserProfileKDEModeler.java

private void computeStats(RealMatrix m) {
    if (m.getColumnDimension() != this.cmdTypes.length) {
        LOG.error("Please fix the commands list in config file");
    }// ww w .ja  v a2 s  .  c om

    statistics = new UserCommandStatistics[m.getColumnDimension()];

    for (int i = 0; i < m.getColumnDimension(); i++) {
        UserCommandStatistics stats = new UserCommandStatistics();
        stats.setCommandName(this.cmdTypes[i]);
        RealVector colData = m.getColumnVector(i);
        StandardDeviation deviation = new StandardDeviation();
        double stddev = deviation.evaluate(colData.toArray());

        if (LOG.isDebugEnabled())
            LOG.debug("Stddev is NAN ? " + (Double.isNaN(stddev) ? "yes" : "no"));
        if (stddev <= lowVarianceVal)
            stats.setLowVariant(true);
        else
            stats.setLowVariant(false);

        stats.setStddev(stddev);
        Mean mean = new Mean();
        double mu = mean.evaluate(colData.toArray());
        if (LOG.isDebugEnabled())
            LOG.debug("mu is NAN ? " + (Double.isNaN(mu) ? "yes" : "no"));

        stats.setMean(mu);
        statistics[i] = stats;
    }
}

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From source file:com.itemanalysis.psychometrics.factoranalysis.PrincipalComponentsMethod.java

public PrincipalComponentsMethod(RealMatrix R, int nFactors, RotationMethod rotationMethod) {
    this.R = R;/*from   w  w w.j  a  v a2  s .c  om*/
    this.nVariables = R.getColumnDimension();
    this.nFactors = nFactors;
    this.rotationMethod = rotationMethod;
}

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From source file:com.itemanalysis.psychometrics.cfa.ConfirmatoryFactorAnalysis.java

/**
 * Computes the first centroid factor loadings to be used
 * as initial values for the confirmatory factor anlaysis.
 *
 * This only works for a one-factor model. Need to
 * generalize to multiple factors.//from w  w  w .  j a v  a2 s. c o  m
 * 
 * @param varcov observed covariance matrix
 * @return
 */
public double[] computeInitialFactorLoadings(RealMatrix varcov) {
    int r = varcov.getRowDimension();
    int c = varcov.getColumnDimension();
    double totalVariance = 0.0;
    double[] rowSum = new double[c];
    double[] inits = new double[c];

    for (int i = 0; i < r; i++) {
        for (int j = 0; j < c; j++) {
            rowSum[i] += varcov.getEntry(i, j);
            totalVariance += varcov.getEntry(i, j);
        }
    }

    double denom = Math.sqrt(totalVariance);

    for (int i = 0; i < r; i++) {
        inits[i] = rowSum[i] / denom;
    }

    return inits;
}

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

/**
 * Determines if dimensions and all cells are equal between the
 * two matrices//from   w ww . j  av a2s  .  c  om
 * 
 * @param A matrix A
 * @param B matrix B
 * @return true if equal, false if not equal
 */
private boolean compareMatrices(RealMatrix A, RealMatrix B) {
    if (A.getRowDimension() == B.getRowDimension() && A.getColumnDimension() == B.getColumnDimension()) {
        for (int r = 0; r < A.getRowDimension(); r++) {
            for (int c = 0; c < A.getColumnDimension(); c++) {
                if (Double.compare(A.getEntry(r, c), B.getEntry(r, c)) != 0) {
                    System.err.println("Value mismatch at row=" + r + ", column=" + c);
                    return false;
                }
            }
        }
    } else {
        System.err.println("Dimensions do not match: A (" + A.getRowDimension() + " x " + A.getColumnDimension()
                + "), B (" + B.getRowDimension() + " x " + B.getColumnDimension() + ")");
        return false;
    }
    return true;
}

---

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

/**
 * Inverse transform the incoming data. If the corresponding weight is 0.0,
 * will coerce the column to positive infinity rather than NaN.
 *//* w  ww  .  ja v a  2s  .com*/
@Override
public RealMatrix inverseTransform(RealMatrix data) {
    checkFit();

    final int m = data.getRowDimension();
    if (data.getColumnDimension() != n)
        throw new DimensionMismatchException(n, data.getColumnDimension());

    double[][] X = data.getData();
    double weight, val;
    for (int j = 0; j < n; j++) {
        weight = weights[j];

        for (int i = 0; i < m; i++) {
            // sometimes, weight can be 0.0 if the user is masochistic...
            val = X[i][j] / weight;
            X[i][j] = Double.isNaN(val) ? Inf : val;
        }
    }

    // assign -- already copied in getData()
    return new Array2DRowRealMatrix(X, false);
}

---

From source file:de.andreasschoknecht.LS3.LSSMCalculator.java

/**
 * Calculate the LSSM matrix containing the final similarity values between the documents, i.e., the models.
 * For calculating the similarity values the Vtk matrix is scaled with the singular value matrix. Afterwards, the cosine similarity
 * transformed on the interval [0,1] is calculated, which represents the similarity value of two documents.
 *///w  ww.  jav  a 2 s. c o  m
void calculateLSSMMatrix() {
    // scale Vtk with singular value matrix Sk
    RealMatrix scaledVtk = Sk.multiply(Vtk);

    int docsNumber = scaledVtk.getColumnDimension();
    double[][] tmpArray = new double[docsNumber][docsNumber];

    for (int i = 0; i < docsNumber; i++) {
        RealVector documentVector1 = scaledVtk.getColumnVector(i);
        for (int j = i; j < docsNumber; j++) {
            double lssmValue = (documentVector1.cosine(scaledVtk.getColumnVector(j)) + 1) / 2;
            tmpArray[i][j] = round(lssmValue, 2);
            tmpArray[j][i] = round(lssmValue, 2);
        }
    }

    lssmMatrix = new Array2DRowRealMatrix(tmpArray);
}

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From source file:com.itemanalysis.psychometrics.cfa.ConfirmatoryFactorAnalysis.java

public ConfirmatoryFactorAnalysis(RealMatrix varcov, double numberOfExaminees, int modelType,
        int estimationMethod) {
    int nItems = varcov.getColumnDimension();
    setModel(nItems, modelType, computeInitialFactorLoadings(varcov));
    setEstimator(model, varcov, numberOfExaminees, estimationMethod);
}

---

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

/**
 * Inverse transform your matrix./*  w w w.j  ava 2s  .  c  o  m*/
 */
@Override
public RealMatrix inverseTransform(RealMatrix X) {
    checkFit();

    final int m = X.getRowDimension();
    final int n = X.getColumnDimension();

    if (n != shift.length)
        throw new DimensionMismatchException(n, shift.length);

    double[][] x = X.getData();
    for (int j = 0; j < n; j++) {
        for (int i = 0; i < m; i++) {
            x[i][j] = yjInvTransSingle(x[i][j], this.lambdas[j]);
        }
    }

    // Implicit copy in the getData()
    return new Array2DRowRealMatrix(x, false);
}

---

From source file:com.itemanalysis.psychometrics.factoranalysis.VarimaxCriteria.java

/**
 * Computes the function value for varimax rotation.
 *
 * @param L matrix of factor loadings./*  w w w  . j  a  v a  2 s  .  c o m*/
 */
public void computeValues(RealMatrix L) {
    //initialize dimensions and column mean array
    int nrow = L.getRowDimension();
    int ncol = L.getColumnDimension();
    Mean[] colMean = new Mean[ncol];
    for (int i = 0; i < ncol; i++) {
        colMean[i] = new Mean();
    }

    //square each element in matrix
    RealMatrix L2 = L.copy();
    double value = 0.0;
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            value = L.getEntry(i, j);
            value *= value;
            L2.setEntry(i, j, value);
            colMean[j].increment(value);
        }
    }

    double dif = 0.0;
    RealMatrix QL = new Array2DRowRealMatrix(nrow, ncol);
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            dif = L2.getEntry(i, j) - colMean[j].getResult();
            QL.setEntry(i, j, dif);
        }
    }

    //compute gradientAt
    gradient = new Array2DRowRealMatrix(nrow, ncol);
    for (int i = 0; i < nrow; i++) {
        for (int j = 0; j < ncol; j++) {
            value = -L.getEntry(i, j) * QL.getEntry(i, j);
            gradient.setEntry(i, j, value);
        }
    }

    //compute function value
    RealMatrix B = QL.transpose().multiply(QL);
    double sum = B.getTrace();
    functionValue = -sum / 4.0;

}

---

From source file:com.itemanalysis.psychometrics.cfa.ConfirmatoryFactorAnalysis.java

public ConfirmatoryFactorAnalysis(RealMatrix varcov, double[] inits, double numberOfExaminees, int modelType,
        int estimationMethod) {
    int nItems = varcov.getColumnDimension();
    setModel(nItems, modelType, inits);// w w  w  .j  a  v a2  s . c  o  m
    setEstimator(model, varcov, numberOfExaminees, estimationMethod);
}

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