model._output._v = svdJ.getU().getMatrix(0, atqJ.getRowDimension() - 1, 0, _parms._nv - 1).getArray();
Separable(SingularValueDecomposition svd) { final int nrows = svd.getU().getRowDimension(); this.row = new float[nrows]; this.col = new float[nrows]; final float factor = (float) Math.sqrt(svd.getS().get(0, 0)); for (int i = 0; i < nrows; i++) { this.row[i] = (float) svd.getU().get(i, 0) * factor; this.col[i] = (float) svd.getV().get(i, 0) * factor; } }
Separable(SingularValueDecomposition svd) { final int nrows = svd.getU().getRowDimension(); this.row = new float[nrows]; this.col = new float[nrows]; final float factor = (float) Math.sqrt(svd.getS().get(0, 0)); for (int i = 0; i < nrows; i++) { this.row[i] = (float) svd.getU().get(i, 0) * factor; this.col[i] = (float) svd.getV().get(i, 0) * factor; } }
public ArrayList<Vector3D> eigenVectors() { // Extract singular values Matrix mat = svd().getU(); ArrayList<Vector3D> res = new ArrayList<Vector3D>(3); for (int i = 0; i < 3; i++) { res.add(new Vector3D(mat.get(0, i), mat.get(1, i), mat.get(2, i))); } return res; }
Matrix mat = svd.getU(); double tmp = hypot(mat.get(0, 0), mat.get(1, 0)); double phi, theta, psi;
/** * Given an approximate rotation matrix Q (that doesn't necessarily conform * properly to a rotation matrix), return the best estimate of a rotation * matrix R such that the Frobenius norm is minimised: min||r-Q||^2_F. * <p> * Fundamentally, this works by performing an SVD of the matrix, setting all * the singular values to 1 and then reconstructing the input. * * @param approx * the initial guess * @return the rotation matrix */ public static Matrix approximateRotationMatrix(Matrix approx) { final SingularValueDecomposition svd = approx.svd(); return svd.getU().times(svd.getV().transpose()); }
final SingularValueDecomposition Svd = Cov.svd(); // Cov = USV' final Matrix S = Svd.getS(); final Matrix U = Svd.getU();
void metricUpgrade(Matrix R) { assert ((R.getRowDimension() == 3) && (R.getColumnDimension() == 3)); SingularValueDecomposition svd = R.svd(); Matrix X = svd.getU().times(svd.getV().transpose()); Matrix W = Matrix.identity(3, 3); W.set(2, 2, X.det()); R.setMatrix(0, 3 - 1, 0, 3 - 1, svd.getU().times(W).times(svd.getV().transpose())); }
u = svd.getU(); v = svd.getV();
void metricUpgrade(Matrix R) { assert ((R.getRowDimension() == 3) && (R.getColumnDimension() == 3)); SingularValueDecomposition svd = R.svd(); Matrix X = svd.getU().times(svd.getV().transpose()); Matrix W = Matrix.identity(3, 3); W.set(2, 2, X.det()); R.setMatrix(0, 3 - 1, 0, 3 - 1, svd.getU().times(W).times(svd.getV().transpose())); }
public Matrix[] svd() { if (getColumnCount() > getRowCount()) { SingularValueDecomposition svd = new SingularValueDecomposition(matrix.transpose()); Matrix u = new JamaDenseDoubleMatrix2D(svd.getV()); Matrix s = new JamaDenseDoubleMatrix2D(svd.getS().transpose()); Matrix v = new JamaDenseDoubleMatrix2D(svd.getU()); return new Matrix[] { u, s, v }; } else { SingularValueDecomposition svd = new SingularValueDecomposition(matrix); Matrix u = new JamaDenseDoubleMatrix2D(svd.getU()); Matrix s = new JamaDenseDoubleMatrix2D(svd.getS()); Matrix v = new JamaDenseDoubleMatrix2D(svd.getV()); return new Matrix[] { u, s, v }; } }
public Matrix[] svd() { if (getColumnCount() > getRowCount()) { SingularValueDecomposition svd = new SingularValueDecomposition(matrix.transpose()); Matrix u = new JamaDenseDoubleMatrix2D(svd.getV()); Matrix s = new JamaDenseDoubleMatrix2D(svd.getS().transpose()); Matrix v = new JamaDenseDoubleMatrix2D(svd.getU()); return new Matrix[] { u, s, v }; } else { SingularValueDecomposition svd = new SingularValueDecomposition(matrix); Matrix u = new JamaDenseDoubleMatrix2D(svd.getU()); Matrix s = new JamaDenseDoubleMatrix2D(svd.getS()); Matrix v = new JamaDenseDoubleMatrix2D(svd.getV()); return new Matrix[] { u, s, v }; } }
u = svd.getU(); v = svd.getV(); sinvm = new Matrix(sinv);
Matrix U = svd.getU(); // U Left Matrix final Matrix S = svd.getS(); // W final Matrix V = svd.getV(); // VT Right Matrix
0, 3 - 1, svd.getU().times(svals).times(svd.getV().transpose()) .times(M.transpose()));
model._output._v = svdJ.getU().getMatrix(0, atqJ.getRowDimension() - 1, 0, _parms._nv - 1).getArray();
0, 3 - 1, svd.getU().times(svals).times(svd.getV().transpose()) .times(M.transpose()));
check(A,SVD.getU().times(SVD.getS().times(SVD.getV().transpose()))); try_success("SingularValueDecomposition...",""); } catch ( java.lang.RuntimeException e ) {