Package org.ejml.simple

Examples of org.ejml.simple.SimpleMatrix

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        // check various sized matrices
        double gamma = 2.5;
        A = SimpleMatrix.random(r*2+r-1,r*2-1,-1,1,rand);

        SimpleMatrix U = A.extractMatrix(0,A.numRows(),1,2);
        U.set(0,0,0);
        U.set(1,0,1);

        SimpleMatrix V = A.extractMatrix(0,A.numRows(),2,3);
        SimpleMatrix expected = V.minus(U.mult(U.transpose().mult(V)).scale(gamma));

        BlockMatrix64F Ab = BlockMatrixOps.convert(A.getMatrix(),r);

        BlockHouseHolder.rank1UpdateMultR_Col(r,new D1Submatrix64F(Ab),1,gamma);

        for( int i = 1; i < expected.numRows(); i++ ) {
            assertEquals(expected.get(i,0),Ab.get(i,2),1e-8);
        }
    }
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    public void applyReflectorsToRow() {

        // try different offsets to make sure there are no implicit assumptions
        for( int offX = 0; offX <= r; offX += r) {
            for( int offY = 0; offY <= r; offY += r) {
                SimpleMatrix A = SimpleMatrix.random(2*r+2,2*r+2,-1,1,rand);
                A = A.mult(A.transpose());
                SimpleMatrix A_orig = A.copy();
                SimpleMatrix V = SimpleMatrix.random(r,A.numCols(),-1,1,rand);

                D1Submatrix64F Ab = insertIntoBlock(offY,offX,A,r);
                D1Submatrix64F Vb = insertIntoBlock(0,offX,V,r);

                int row = r-1;

                // manually apply "reflectors" to A
                for( int i = 0; i < row; i++ ) {
                    SimpleMatrix u = A_orig.extractVector(true,i).transpose();
                    SimpleMatrix v = V.extractVector(true,i).transpose();

                    for( int j = 0; j <= i; j++ ) {
                        u.set(j,0.0);
                    }
                    u.set(i+1,1.0);

                    A = A.plus(u.mult(v.transpose())).plus(v.mult(u.transpose()));
                }
                // apply the reflector to that row
                TridiagonalBlockHelper.applyReflectorsToRow(r,Ab,Vb,row);

                // compare to manually computed solution
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    @Test
    public void rank1UpdateMultR_TopRow() {
        double gamma = 2.5;
        A = SimpleMatrix.random(r*2+r-1,r*2-1,-1,1,rand);

        SimpleMatrix U = A.extractMatrix(0,A.numRows(),1,2);
        U.set(0,0,0);
        U.set(1,0,1);

        BlockMatrix64F Ab = BlockMatrixOps.convert(A.getMatrix(),r);

        BlockHouseHolder.rank1UpdateMultR_TopRow(r,new D1Submatrix64F(Ab),1,gamma);

        // check all the columns now
        for( int i = 0; i < r; i++ ) {
            for( int j = r; j < A.numCols(); j++ ) {
                SimpleMatrix V = A.extractMatrix(0,A.numRows(),j,j+1);
                SimpleMatrix expected = V.minus(U.mult(U.transpose().mult(V)).scale(gamma));

                assertEquals(i+" "+j,expected.get(i,0),Ab.get(i,j),1e-8);
            }
        }
    }
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        svd.setVt(CommonOps.identity(n));
        assertTrue(svd.process(values));

//        System.out.println("Vector total steps = "+svd.totalSteps);

        SimpleMatrix Ut = SimpleMatrix.wrap(svd.getUt());
        SimpleMatrix Vt = SimpleMatrix.wrap(svd.getVt());
        SimpleMatrix W = SimpleMatrix.diag(svd.diag);
//
//            Ut.mult(W).mult(V).print();
        SimpleMatrix A_found = Ut.transpose().mult(W).mult(Vt);
//            A_found.print();

        assertEquals(diag[0],A_found.get(0,0),1e-8);
        for( int i = 0; i < n-1; i++ ) {
            assertEquals(diag[i+1],A_found.get(i+1,i+1),1e-8);
            assertEquals(off[i],A_found.get(i,i+1),1e-8);
        }
    }
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        return new D1Submatrix64F(C,offRow,C.numRows,offCol,C.numCols);
    }

    @Test
    public void multA_u() {
        SimpleMatrix A = SimpleMatrix.random(2*r+2,2*r+2,-1,1,rand);
        // make a symmetric so that this mult will work
        A = A.transpose().mult(A);

        BlockMatrix64F Ab = BlockMatrixOps.convert(A.getMatrix(),r);
        BlockMatrix64F V = new BlockMatrix64F(r,Ab.numCols,r);

        int row = 1;

        SimpleMatrix u = A.extractVector(true,row).transpose();
        for( int i = 0; i <= row; i++ ) {
            u.set(i,0);
        }
        u.set(row+1,1);

        SimpleMatrix v = A.mult(u).transpose();

        TridiagonalBlockHelper.multA_u(r,new D1Submatrix64F(Ab),
                new D1Submatrix64F(V),row);

        for( int i = row+1; i < A.numCols(); i++ ) {
            assertEquals(v.get(i),V.get(row,i),1e-8);
        }
    }
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    @Test
    public void rank1UpdateMultL_Row() {
        double gamma = 2.5;
        A = SimpleMatrix.random(r*2+r-1,r*2+r-1,-1,1,rand);

        SimpleMatrix U = A.extractMatrix(1,2,0,A.numCols()).transpose();
        U.set(0,0);
        U.set(1,1);

        SimpleMatrix expected = A.minus( A.mult(U).mult(U.transpose()).scale(gamma) );

        BlockMatrix64F Ab = BlockMatrixOps.convert(A.getMatrix(),r);

        BlockHouseHolder.rank1UpdateMultL_Row(r,new D1Submatrix64F(Ab),1,1,gamma);

        for( int j = 1; j < expected.numCols(); j++ ) {
            assertEquals(expected.get(2,j),Ab.get(2,j),1e-8);
        }
    }
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        return quality(orig,svd.getU(null,false),svd.getW(null),svd.getV(null,true));
    }

    public static double quality( DenseMatrix64F orig , DenseMatrix64F U , DenseMatrix64F W , DenseMatrix64F Vt )
    {
        SimpleMatrix _U = SimpleMatrix.wrap(U);
        SimpleMatrix _W = SimpleMatrix.wrap(W);
        SimpleMatrix _Vt = SimpleMatrix.wrap(Vt);

        SimpleMatrix foundA = _U.mult(_W).mult(_Vt);

        return SpecializedOps.diffNormF(orig,foundA.getMatrix())/foundA.normF();
    }
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     * @param eig EVD of the original matrix. Not modified.
     * @return The quality of the decomposition.
     */
    public static double quality( DenseMatrix64F orig , EigenDecomposition<DenseMatrix64F> eig )
    {
        SimpleMatrix A = SimpleMatrix.wrap(orig);
        SimpleMatrix V = SimpleMatrix.wrap(EigenOps.createMatrixV(eig));
        SimpleMatrix D = SimpleMatrix.wrap(EigenOps.createMatrixD(eig));

        SimpleMatrix L = A.mult(V);
        SimpleMatrix R = V.mult(D);

        SimpleMatrix diff = L.minus(R);

        double top = diff.normF();
        double bottom = L.normF();

        double error = top/bottom;

        return error;
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        n = A.numCols;

        min = Math.min(m,n);

        U = SimpleMatrix.identity(m);
        B = new SimpleMatrix(A);
        V = SimpleMatrix.identity(n);

        int max = Math.max(m,n);
        u = new DenseMatrix64F(max,1);
    }
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            for( int i = k+1; i < m; i++ ) {
                u[i] /= nu;
            }

            SimpleMatrix Q_k = SimpleMatrix.wrap(SpecializedOps.createReflector(this.u,nu/tau));
            U = U.mult(Q_k);
            B = Q_k.mult(B);
        }
    }
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