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:io.warp10.script.functions.MATTO.java

@Override
public Object apply(WarpScriptStack stack) throws WarpScriptException {

    Object o = stack.pop();//from   ww  w  .  j  a v  a2 s  .  co m

    if (!(o instanceof RealMatrix)) {
        throw new WarpScriptException(getName() + " expects a matrix on top of the stack.");
    }

    RealMatrix matrix = (RealMatrix) o;

    List<Object> rows = new ArrayList<Object>(matrix.getRowDimension());

    double[][] data = matrix.getData();

    for (int i = 0; i < matrix.getRowDimension(); i++) {
        List<Object> cols = new ArrayList<Object>(matrix.getColumnDimension());
        for (int j = 0; j < matrix.getColumnDimension(); j++) {
            cols.add(data[i][j]);
        }
        rows.add(cols);
    }

    stack.push(rows);

    return stack;
}

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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  w w  . j a va2s  .c  o  m
 * 
 * @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;
}

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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  ww .  j a va 2 s.  com*/
 * 
 * @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;
}

---

From source file:com.itemanalysis.psychometrics.mixture.MvNormalComponentDistribution.java

/**
 *
 * @param x a matrix of dimension 1 x k, where k is the number of variables
 * @return/* w  w w. j a va 2 s .co  m*/
 */
public double density(RealMatrix x) throws SingularMatrixException {
    double prob = 0.0;
    RealMatrix xTran = x.transpose();
    int d = xTran.getRowDimension();
    double det = new LUDecomposition(sigma).getDeterminant();
    double nconst = 1.0 / Math.sqrt(det * Math.pow(2.0 * Math.PI, d));
    RealMatrix Sinv = new LUDecomposition(sigma).getSolver().getInverse();
    RealMatrix delta = xTran.subtract(mu);
    RealMatrix dist = (delta.transpose().multiply(Sinv).multiply(delta));
    prob = nconst * Math.exp(-0.5 * dist.getEntry(0, 0));
    return prob;
}

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From source file:edu.oregonstate.eecs.mcplan.ml.SequentialProjectionHashLearner.java

private void T(final RealMatrix S_tilde, final RealMatrix S) {
    for (int i = 0; i < S_tilde.getRowDimension(); ++i) {
        for (int j = 0; j < S_tilde.getColumnDimension(); ++j) {
            final double st = S_tilde.getEntry(i, j);
            S_tilde.setEntry(i, j, st * S.getEntry(i, j) < 0 ? st : 0);
        }/*from  w w  w  .j a  v  a  2s  .com*/
    }
}

---

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

public void computeValues(RealMatrix L) {
    final int k = L.getColumnDimension();
    final int p = L.getRowDimension();
    RealMatrix I = new IdentityMatrix(p);
    RealMatrix L2 = MatrixUtils.multiplyElements(L, L);

    RealMatrix N = new Array2DRowRealMatrix(k, k);
    N.walkInRowOrder(new DefaultRealMatrixChangingVisitor() {
        @Override//from  ww  w. j a v a2s  .  co  m
        public double visit(int row, int column, double value) {
            if (row == column)
                return 0.0;
            return 1.0;
        }
    });

    RealMatrix C = new Array2DRowRealMatrix(p, p);
    C.walkInRowOrder(new DefaultRealMatrixChangingVisitor() {
        @Override
        public double visit(int row, int column, double value) {
            return gam / (double) p;
        }
    });

    RealMatrix X = I.subtract(C).multiply(L2).multiply(N);
    double sum = MatrixUtils.sumMatrix(MatrixUtils.multiplyElements(L2, X));
    functionValue = sum / 4.0;
    gradient = MatrixUtils.multiplyElements(L, X);

}

---

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.
 *//*from   ww  w.  j  a  v a  2 s .  c om*/
@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:com.clust4j.algo.preprocess.YeoJohnsonTransformer.java

/**
 * Inverse transform your matrix.//from   w  ww.  j  av a  2  s  .com
 */
@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:io.yields.math.concepts.operator.Smoothness.java

private boolean isConstant(RealMatrix matrix) {
    double refElement = matrix.getEntry(0, 0);
    return !IntStream.range(0, matrix.getRowDimension()).asDoubleStream()
            .filter(element -> Math.abs(refElement - element) > EPS).findAny().isPresent();
}

---

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

/**
 * Computes the function value for varimax rotation.
 *
 * @param L matrix of factor loadings./*from w w  w .j  a va 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;

}

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