Java DenseMatrix.getData方法代码示例

import no.uib.cipr.matrix.DenseMatrix; //导入方法依赖的package包/类
public static DenseMatrix multBLASAdd(double alpha, DenseMatrix B, DenseMatrix C) {

        double[] Bd = B.getData(), Cd = C.getData();
        int CRows = C.numRows();
        int cCols = C.numColumns();
        int BRows = B.numRows();
        int bCols = B.numColumns();


        BLAS.getInstance().dgemm("N", "N",
                C.numRows(), C.numColumns(),
                B.numColumns(), alpha,
                Bd,
                Math.max(1, B.numRows()), Bd, Math.max(1, B.numRows()), 0.0, Cd,
                Math.max(1, C.numRows()));

        return C;
    }
 

import no.uib.cipr.matrix.DenseMatrix; //导入方法依赖的package包/类
private double sumLogFactorial(DenseMatrix fv) {
	double sum = 0;
	final double[] data = fv.getData();
	for (int i = 0; i < fv.numColumns(); i++) {
		final int fvi = (int) data[i];
		if (logFacCache.contains(fvi))
		{
			sum += logFacCache.get(fvi);
		}
		else {
			for (int j = 1; j < fvi + 1; j++) {
				sum += Math.log(j);
			}
		}
	}
	return sum;
}
 

import no.uib.cipr.matrix.DenseMatrix; //导入方法依赖的package包/类
/**
 * Solve the general problem A x = L B x. 
 * The returned eigenvalues are not ordered.
 *
 * @param A symmetric matrix A
 * @param B symmetric matrix B
 * @return The eigenvectors x and eigenvalues L.
 */
public static IndependentPair<Matrix, double[]> symmetricGeneralisedEigenvectors(Matrix A, Matrix B) {
	if ((A.getRowDimension() != A.getColumnDimension()) || (B.getRowDimension() != B.getColumnDimension())) 
    	throw new IllegalArgumentException("Input matrices must be square");
	
	int dim = A.getRowDimension();
	DenseMatrix vecs = new DenseMatrix(A.getArray());
	DenseVector W = new DenseVector(dim);
    
	sygvd(1, "V", "U", vecs, new DenseMatrix(B.getArray()), W);
    
	Matrix evecs = new Matrix(dim, dim);
	final double[][] evecsData = evecs.getArray();
	final double[] vecsData = vecs.getData();
	for (int r=0; r<dim; r++)
		for (int c=0; c<dim; c++)
			evecsData[r][c] = vecsData[r + c * dim];
	
    return new IndependentPair<Matrix, double[]>(evecs, W.getData());
}
 

import no.uib.cipr.matrix.DenseMatrix; //导入方法依赖的package包/类
/**
 * Solve the general problem A x = L B x. 
 * The returned eigenvalues ordered in a decreasing manner.
 *
 * @param A symmetric matrix A
 * @param B symmetric matrix B
 * @return The eigenvectors x and eigenvalues L.
 */
public static IndependentPair<Matrix, double[]> symmetricGeneralisedEigenvectorsSorted(Matrix A, Matrix B) {
	if ((A.getRowDimension() != A.getColumnDimension()) || (B.getRowDimension() != B.getColumnDimension())) 
    	throw new IllegalArgumentException("Input matrices must be square");
	
	int dim = A.getRowDimension();
	DenseMatrix vecs = new DenseMatrix(A.getArray());
	DenseVector W = new DenseVector(dim);
    
	sygvd(1, "V", "U", vecs, new DenseMatrix(B.getArray()), W);
    
	Matrix evecs = new Matrix(dim, dim);
	final double[][] evecsData = evecs.getArray();
	final double[] vecsData = vecs.getData();
	final double[] valsData = W.getData();
	
	int [] indices = ArrayUtils.range(0, valsData.length-1);
	ArrayUtils.parallelQuicksortDescending(valsData, indices);
	
	for (int r=0; r<dim; r++)
		for (int c=0; c<dim; c++)
			evecsData[r][c] = vecsData[r + indices[c] * dim];
	
    return new IndependentPair<Matrix, double[]>(evecs, valsData);
}
 

import no.uib.cipr.matrix.DenseMatrix; //导入方法依赖的package包/类
public static <
	V extends Vertex<V, E, F>,
	E extends Edge<V, E, F>,
	F extends Face<V, E, F>
> double[][] getTensor(
	double[] p,
	double scale,
	KdTree<V, E, F> kd,
	AdapterSet a
 ){
	Collection<F> faces = KdUtility.collectFacesInRadius(kd, p, scale);
	Collection<E> edges = KdUtility.collectEdgesInRadius(kd, p, scale);
	double area=0;
	for(F f :faces){
		area += area(f, a);
	}
	DenseMatrix matrix = new DenseMatrix(3,3);
	double beta = 0;
	double edgeLength = 0;
	for(E e :edges){
		beta = getAngle(e, a);
		edgeLength = getLength(e, a);
		double[][] T = getEdgeCurvatureTensor(e, a);
		DenseMatrix tmp = new DenseMatrix(T);
		matrix.add(beta * edgeLength, tmp);
	}
	matrix.scale(1/area);
	double[][] result = {
		{matrix.getData()[0], matrix.getData()[3], matrix.getData()[6]},
		{matrix.getData()[1], matrix.getData()[4], matrix.getData()[7]},
		{matrix.getData()[2], matrix.getData()[5], matrix.getData()[8]}
	};
	return result;
}
 

import no.uib.cipr.matrix.DenseMatrix; //导入方法依赖的package包/类
/**
 * Convert a {@link DenseMatrix} to a {@link Matrix}.
 * 
 * @param mjt
 *            {@link DenseMatrix} to convert
 * @param nrows
 *            number of rows to copy
 * @param ncols
 *            number of columns to copy
 * @return converted matrix.
 */
public static Matrix convert(DenseMatrix mjt, int nrows, int ncols) {
	final double[][] d = new double[nrows][ncols];

	final double[] mjtd = mjt.getData();
	for (int r = 0; r < nrows; r++) {
		for (int c = 0; c < ncols; c++) {
			d[r][c] = mjtd[r + c * d.length];
		}
	}
	return new Matrix(d);
}