Examples of SimpleMatrix

com.alibaba.security.simpleimage.analyze.sift.matrix.SimpleMatrix
类SimpleMatrix.java的实现描述：TODO 类实现描述 @author axman 2013-3-25 下午4:41:17
org.ejml.simple.SimpleMatrix
nist.gov/javanumerics/jama/
@author Peter Abeles

Examples of org.ejml.simple.SimpleMatrix

        DenseMatrix64F A = RandomMatrices.createRandom(6,6,rand);
        DenseMatrix64F u = RandomMatrices.createRandom(6,1,rand);
        DenseMatrix64F w = RandomMatrices.createRandom(6,1,rand);
        double gamma = -45;


        SimpleMatrix _A = SimpleMatrix.wrap(A);
        SimpleMatrix _u = SimpleMatrix.wrap(u);
        SimpleMatrix _w = SimpleMatrix.wrap(w);


        SimpleMatrix expected = _A.plus(_u.mult(_w.transpose()).scale(gamma));
        DenseMatrix64F found = A.copy();


        VectorVectorMult.rank1Update(gamma,found,u,w);


        EjmlUnitTests.assertEquals(expected.getMatrix(),found,1e-8);
    }

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix

//            System.out.println("width!!!  "+width);
            double betas[] = new double[ r ];
            for( int i = 0; i < r; i++ )
                betas[i] = i + 0.5;


            SimpleMatrix A = SimpleMatrix.random(r,width,-1,1,rand);


            // Compute W directly using SimpleMatrix
            SimpleMatrix v = A.extractVector(true,0);
            v.set(0,0);
            v.set(1,1);
            SimpleMatrix Y = v;
            SimpleMatrix W = v.scale(-betas[0]);


            for( int i = 1; i < A.numRows(); i++ ) {
                v = A.extractVector(true,i);


                for( int j = 0; j <= i; j++ )
                    v.set(j,0);
                if( i+1 < A.numCols())
                    v.set(i+1,1);


                SimpleMatrix z = v.transpose().plus(W.transpose().mult(Y.mult(v.transpose()))).scale(-betas[i]);


                W = W.combine(i,0,z.transpose());
                Y = Y.combine(i,0,v);
            }


            // now compute it using the block matrix stuff
            BlockMatrix64F Ab = BlockMatrixOps.convert(A.getMatrix(),r);

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix


        // 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);
        }
    }

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix

    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

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix

    @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);
            }
        }
    }

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix

        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);
        }
    }

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix

        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);
        }
    }

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix

    @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);
        }
    }

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix

        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();
    }

View Full Code Here

Examples of org.ejml.simple.SimpleMatrix

     * @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;

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.