Java Code Examples for org.apache.commons.math3.linear.RealMatrix#setEntry()

/**
 * Derives a correlation matrix from a covariance matrix.
 *
 * <p>Uses the formula <br/>
 * <code>r(X,Y) = cov(X,Y)/s(X)s(Y)</code> where
 * <code>r(&middot,&middot;)</code> is the correlation coefficient and
 * <code>s(&middot;)</code> means standard deviation.</p>
 *
 * @param covarianceMatrix the covariance matrix
 * @return correlation matrix
 */
public RealMatrix covarianceToCorrelation(RealMatrix covarianceMatrix) {
    int nVars = covarianceMatrix.getColumnDimension();
    RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
    for (int i = 0; i < nVars; i++) {
        double sigma = FastMath.sqrt(covarianceMatrix.getEntry(i, i));
        outMatrix.setEntry(i, i, 1d);
        for (int j = 0; j < i; j++) {
            double entry = covarianceMatrix.getEntry(i, j) /
                   (sigma * FastMath.sqrt(covarianceMatrix.getEntry(j, j)));
            outMatrix.setEntry(i, j, entry);
            outMatrix.setEntry(j, i, entry);
        }
    }
    return outMatrix;
}
 

/**
 * Derives a correlation matrix from a covariance matrix.
 *
 * <p>Uses the formula <br/>
 * <code>r(X,Y) = cov(X,Y)/s(X)s(Y)</code> where
 * <code>r(&middot,&middot;)</code> is the correlation coefficient and
 * <code>s(&middot;)</code> means standard deviation.</p>
 *
 * @param covarianceMatrix the covariance matrix
 * @return correlation matrix
 */
public RealMatrix covarianceToCorrelation(RealMatrix covarianceMatrix) {
    int nVars = covarianceMatrix.getColumnDimension();
    RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
    for (int i = 0; i < nVars; i++) {
        double sigma = FastMath.sqrt(covarianceMatrix.getEntry(i, i));
        outMatrix.setEntry(i, i, 1d);
        for (int j = 0; j < i; j++) {
            double entry = covarianceMatrix.getEntry(i, j) /
                   (sigma * FastMath.sqrt(covarianceMatrix.getEntry(j, j)));
            outMatrix.setEntry(i, j, entry);
            outMatrix.setEntry(j, i, entry);
        }
    }
    return outMatrix;
}
 

/**
 * Get the covariance matrix.
 * @return covariance matrix
 */
public RealMatrix getResult() {

    int dimension = sums.length;
    RealMatrix result = MatrixUtils.createRealMatrix(dimension, dimension);

    if (n > 1) {
        double c = 1.0 / (n * (isBiasCorrected ? (n - 1) : n));
        int k = 0;
        for (int i = 0; i < dimension; ++i) {
            for (int j = 0; j <= i; ++j) {
                double e = c * (n * productsSums[k++] - sums[i] * sums[j]);
                result.setEntry(i, j, e);
                result.setEntry(j, i, e);
            }
        }
    }

    return result;

}
 

private void assertReducedPanelPInverseCounts(final PCACoveragePoN filePoN, final PCACoveragePoN ramPoN) {
    final RealMatrix ramNormalizedCounts = ramPoN.getReducedPanelPInverseCounts();
    final RealMatrix fileNormalizedCounts = filePoN.getReducedPanelPInverseCounts();

    Assert.assertEquals(ramNormalizedCounts.subtract(fileNormalizedCounts).getNorm(), 0, 1e-9);
    ramNormalizedCounts.setEntry(3, 4, 500000);
    Assert.assertEquals(ramNormalizedCounts.getEntry(3, 4), 500000, 1e-9);
    Assert.assertNotEquals(ramPoN.getReducedPanelPInverseCounts().getEntry(3, 4), 500000, 1e-9);

    Assert.assertFalse(ramNormalizedCounts.subtract(fileNormalizedCounts).getNorm() < 1e-9);

    final RealMatrix fileNormalizedCounts2 = filePoN.getReducedPanelPInverseCounts();
    Assert.assertFalse(ramNormalizedCounts.subtract(fileNormalizedCounts2).getNorm() < 1e-9);
}
 

/**
 * Computes the square-root of the weight matrix.
 *
 * @param m Symmetric, positive-definite (weight) matrix.
 * @return the square-root of the weight matrix.
 */
private RealMatrix squareRoot(RealMatrix m) {
    if (m instanceof DiagonalMatrix) {
        final int dim = m.getRowDimension();
        final RealMatrix sqrtM = new DiagonalMatrix(dim);
        for (int i = 0; i < dim; i++) {
           sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
        }
        return sqrtM;
    } else {
        final EigenDecomposition dec = new EigenDecomposition(m);
        return dec.getSquareRoot();
    }
}
 

private static void test1() throws Exception{
    MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet("/Users/chengli/Downloads/scene/train_test_split/train", DataSetType.ML_CLF_DENSE,true);
    RealMatrix data = new Array2DRowRealMatrix(dataSet.getNumDataPoints(),dataSet.getNumFeatures());
    for (int i=0;i<dataSet.getNumDataPoints();i++){
        for (int j=0;j<dataSet.getNumFeatures();j++){
            data.setEntry(i,j,dataSet.getRow(i).get(j));
        }
    }

    int numComponents=10;
    GMM gmm = new GMM(dataSet.getNumFeatures(),numComponents, data);

    GMMTrainer trainer = new GMMTrainer(data, gmm);


    for (int i=1;i<=5;i++){
        System.out.println("iteration = "+i);
        trainer.iterate();
        double logLikelihood = IntStream.range(0,dataSet.getNumDataPoints()).parallel()
                .mapToDouble(j->gmm.logDensity(data.getRowVector(j))).sum();
        System.out.println("log likelihood = "+logLikelihood);
        Serialization.serialize(gmm, "/Users/chengli/tmp/gmm/model_iter_"+i);
        for (int k=0;k<gmm.getNumComponents();k++){
            FileUtils.writeStringToFile(new File("/Users/chengli/tmp/gmm/mean_iter_"+i+"_component_"+(k+1)),
                    gmm.getGaussianDistributions()[k].getMean().toString().replace("{","")
                            .replace("}","").replace(";",","));
        }
    }


}
 

/**
 * Computes the correlation matrix for the columns of the
 * input matrix.
 *
 * @param matrix matrix with columns representing variables to correlate
 * @return correlation matrix
 */
public RealMatrix computeCorrelationMatrix(RealMatrix matrix) {
    int nVars = matrix.getColumnDimension();
    RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
    for (int i = 0; i < nVars; i++) {
        for (int j = 0; j < i; j++) {
          double corr = correlation(matrix.getColumn(i), matrix.getColumn(j));
          outMatrix.setEntry(i, j, corr);
          outMatrix.setEntry(j, i, corr);
        }
        outMatrix.setEntry(i, i, 1d);
    }
    return outMatrix;
}
 

private static RealMatrix createRealMatrix(Dataset a) {
	if (a.getRank() != 2) {
		throw new IllegalArgumentException("Dataset must be rank 2");
	}
	int[] shape = a.getShapeRef();
	IndexIterator it = a.getIterator(true);
	int[] pos = it.getPos();
	RealMatrix m = MatrixUtils.createRealMatrix(shape[0], shape[1]);
	while (it.hasNext()) {
		m.setEntry(pos[0], pos[1], a.getElementDoubleAbs(it.index));
	}
	return m;
}
 

public CorrelatedRandomVectorGeneratorTest() {
    mean = new double[] { 0.0, 1.0, -3.0, 2.3 };

    RealMatrix b = MatrixUtils.createRealMatrix(4, 3);
    int counter = 0;
    for (int i = 0; i < b.getRowDimension(); ++i) {
        for (int j = 0; j < b.getColumnDimension(); ++j) {
            b.setEntry(i, j, 1.0 + 0.1 * ++counter);
        }
    }
    RealMatrix bbt = b.multiply(b.transpose());
    covariance = MatrixUtils.createRealMatrix(mean.length, mean.length);
    for (int i = 0; i < covariance.getRowDimension(); ++i) {
        covariance.setEntry(i, i, bbt.getEntry(i, i));
        for (int j = 0; j < covariance.getColumnDimension(); ++j) {
            double s = bbt.getEntry(i, j);
            covariance.setEntry(i, j, s);
            covariance.setEntry(j, i, s);
        }
    }

    RandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(17399225432l);
    GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
    generator = new CorrelatedRandomVectorGenerator(mean,
                                                    covariance,
                                                    1.0e-12 * covariance.getNorm(),
                                                    rawGenerator);
}
 

public RealMatrix toApacheMatrix(final IScope scope) {
	final RealMatrix rm = new Array2DRowRealMatrix(numRows, numCols);
	for (int i = 0; i < numCols; i++) {
		for (int j = 0; j < numRows; j++) {
			final double val = get(scope, i, j);
			rm.setEntry(j, i, val);
		}
	}
	return rm;
}
 

/**
 * Lanczos tridiagonalization for a symmetric matrix C to make s * s tridiagonal matrix T.
 *
 * @see http://www.cas.mcmaster.ca/~qiao/publications/spie05.pdf
 * @param C target symmetric matrix
 * @param a initial vector
 * @param T result is stored here
 */
public static void lanczosTridiagonalization(@Nonnull final RealMatrix C,
        @Nonnull final double[] a, @Nonnull final RealMatrix T) {
    Preconditions.checkArgument(Arrays.deepEquals(C.getData(), C.transpose().getData()),
        "Target matrix C must be a symmetric matrix");
    Preconditions.checkArgument(C.getColumnDimension() == a.length,
        "Column size of A and length of a should be same");
    Preconditions.checkArgument(T.getRowDimension() == T.getColumnDimension(),
        "T must be a square matrix");

    int s = T.getRowDimension();

    // initialize T with zeros
    T.setSubMatrix(new double[s][s], 0, 0);

    RealVector a0 = new ArrayRealVector(a.length);
    RealVector r = new ArrayRealVector(a);

    double beta0 = 1.d;

    for (int i = 0; i < s; i++) {
        RealVector a1 = r.mapDivide(beta0);
        RealVector Ca1 = C.operate(a1);

        double alpha1 = a1.dotProduct(Ca1);

        r = Ca1.add(a1.mapMultiply(-1.d * alpha1)).add(a0.mapMultiply(-1.d * beta0));

        double beta1 = r.getNorm();

        T.setEntry(i, i, alpha1);
        if (i - 1 >= 0) {
            T.setEntry(i, i - 1, beta0);
        }
        if (i + 1 < s) {
            T.setEntry(i, i + 1, beta1);
        }

        a0 = a1.copy();
        beta0 = beta1;
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1,
                               RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1,
                               RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix 1.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1, RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix 1.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1, RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix 1.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1, RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix 1.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1, RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix 1.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1, RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix 1.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1, RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}
 

/**
 * Copies a column from m1 to m2.
 *
 * @param m1 Source matrix 1.
 * @param col1 Source column.
 * @param m2 Target matrix.
 * @param col2 Target column.
 */
private static void copyColumn(final RealMatrix m1, int col1, RealMatrix m2, int col2) {
    for (int i = 0; i < m1.getRowDimension(); i++) {
        m2.setEntry(i, col2, m1.getEntry(i, col1));
    }
}