@Test public void testVOrthogonal() { checkOrthogonal(new SingularValueDecomposition(new DenseMatrix(testSquare)).getV()); checkOrthogonal(new SingularValueDecomposition(new DenseMatrix(testNonSquare)).getV()); checkOrthogonal(new SingularValueDecomposition(new DenseMatrix(testNonSquare).transpose()).getV()); }
public Matrix getV() { if (transpositionNeeded) { int numCols = Math.min(m + 1, n); Matrix r = new DenseMatrix(m, numCols); for (int i = 0; i < m; i++) { for (int j = 0; j < numCols; j++) { r.set(i, j, u[i][j]); } } return r; } else { return new DenseMatrix(v); } }
SingularValueDecomposition implements java.io.Serializable { public Matrix getV() { if (transpositionNeeded) { int numCols = Math.min(m + 1, n); Matrix r = new DenseMatrix(m, numCols); for (int i = 0; i < m; i++) { for (int j = 0; j < numCols; j++) { r.set(i, j, u[i][j]); } } return r; } else { return new DenseMatrix(v); } } }
SingularValueDecomposition implements java.io.Serializable { public Matrix getV() { if (transpositionNeeded) { int numCols = Math.min(m + 1, n); Matrix r = new DenseMatrix(m, numCols); for (int i = 0; i < m; i++) { for (int j = 0; j < numCols; j++) { r.set(i, j, u[i][j]); } } return r; } else { return new DenseMatrix(v); } } SingularValueDecomposition(Matrix arg); }
SingularValueDecomposition implements java.io.Serializable { public Matrix getV() { if (transpositionNeeded) { int numCols = Math.min(m + 1, n); Matrix r = new DenseMatrix(m, numCols); for (int i = 0; i < m; i++) { for (int j = 0; j < numCols; j++) { r.set(i, j, u[i][j]); } } return r; } else { return new DenseMatrix(v); } } SingularValueDecomposition(Matrix arg); double cond(); Matrix getS(); double[] getSingularValues(); Matrix getU(); Matrix getV(); double norm2(); int rank(); @Override String toString(); }
SingularValueDecomposition implements java.io.Serializable { public Matrix getV() { if (transpositionNeeded) { int numCols = Math.min(m + 1, n); Matrix r = new DenseMatrix(m, numCols); for (int i = 0; i < m; i++) { for (int j = 0; j < numCols; j++) { r.set(i, j, u[i][j]); } } return r; } else { return new DenseMatrix(v); } } SingularValueDecomposition(Matrix arg); double cond(); Matrix getS(); double[] getSingularValues(); Matrix getU(); Matrix getV(); double norm2(); int rank(); @Override String toString();  }