Examples of org.ejml.data.DenseMatrix64F

org.ejml.data.DenseMatrix64F

DenseMatrix64F is a dense matrix with elements that are 64-bit floats (doubles). A matrix is the fundamental data structure in linear algebra. Unlike a sparse matrix, there is no compression in a dense matrix and every element is stored in memory. This allows for fast reads and writes to the matrix.

To keep the code manageable and the library easier to use only basic functions for accessing and editing elements are provided in this class. The numerous operations which can be performed on DenseMatrix64F are contained in various other classes, where the most common operations can be found in the {@link org.ejml.ops.CommonOps} and {@link org.ejml.ops.SpecializedOps} classes.

The matrix is stored internally in a row-major 1D array format:

data[ y*numCols + x ] = data[y][x]

For example:
data =

a₁₁ a₁₂ a₁₃ a₁₄ a₂₁ a₂₂ a₂₃ a₂₄ a₃₁ a₃₂ a₃₃ a₃₄ a₄₁ a₄₂ a₄₃ a₄₄

An alternative to working directly with DenseMatrix64 is {@link org.ejml.simple.SimpleMatrix}. SimpleMatrix is a wrapper around DenseMatrix64F that provides an easier to use object oriented way of manipulating matrices, at the cost of efficiency.
@see org.ejml.ops.CommonOps @see org.ejml.ops.SpecializedOps @see org.ejml.simple.SimpleMatrix @author Peter Abeles


    @Test
    public void testDecomposition2()
    {
        for( int i = 2; i <= 20; i++ ) {
            DenseMatrix64F A = RandomMatrices.createRandom(i,i,-1,1,rand);


            LUDecomposition<DenseMatrix64F> alg = create(i,i);
            assertTrue(alg.decompose(A));


            assertFalse(alg.isSingular());


            SimpleMatrix L = SimpleMatrix.wrap(alg.getLower(null));
            SimpleMatrix U = SimpleMatrix.wrap(alg.getUpper(null));
            SimpleMatrix P = SimpleMatrix.wrap(alg.getPivot(null));


            DenseMatrix64F A_found = P.transpose().mult(L).mult(U).getMatrix();
            assertTrue(MatrixFeatures.isIdentical(A_found,A,1e-8));
        }
    }

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        }
    }


    @Test
    public void zeroMatrix() {
        DenseMatrix64F A = new DenseMatrix64F(3,3);


        LUDecomposition<DenseMatrix64F> alg = create(3,3);


        assertTrue(alg.decompose(A));
        assertTrue(alg.isSingular());


        DenseMatrix64F L = alg.getLower(null);
        DenseMatrix64F U = alg.getUpper(null);


        DenseMatrix64F A_found = new DenseMatrix64F(3,3);
        CommonOps.mult(L,U,A_found);


        assertFalse(MatrixFeatures.hasUncountable(A_found));
        assertTrue(MatrixFeatures.isIdentical(A_found,A,1e-8));
    }

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        assertTrue(MatrixFeatures.isIdentical(A_found,A,1e-8));
    }


    @Test
    public void testSingular(){
        DenseMatrix64F A = new DenseMatrix64F(3,3, true, 1, 2, 3, 2, 4, 6, 4, 4, 0);


        LUDecomposition alg = create(3,3);
        assertTrue(alg.decompose(A));
        assertTrue(alg.isSingular());
    }

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        assertTrue(alg.isSingular());
    }


    @Test
    public void testNearlySingular(){
        DenseMatrix64F A = new DenseMatrix64F(3,3, true, 1, 2, 3, 2, 4, 6.1, 4, 4, 0);


        LUDecomposition alg = create(3,3);
        assertTrue(alg.decompose(A));
        assertFalse(alg.isSingular());
    }

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    Random rand = new Random(234324);


    @Test
    public void extract() {
        DenseMatrix64F A = RandomMatrices.createRandom(5, 5, 0, 1, rand);


        DenseMatrix64F B = new DenseMatrix64F(3,3);


        ImplCommonOps_Matrix64F.extract(A, 1, 2, B, 1, 0,2,3);


        for( int i = 1; i < 3; i++ ) {
            for( int j = 2; j < 5; j++ ) {
                assertEquals(A.get(i,j),B.get(i,j-2),1e-8);
            }
        }
    }

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     * Checks to see how it handles getLower getUpper functions with and without
     * a matrix being provided.
     */
    @Test
    public void getLower_getUpper() {
        DenseMatrix64F A = new DenseMatrix64F(3,3, true, 5, 2, 3, 1.5, -2, 8, -3, 4.7, -0.5);


        LUDecomposition<DenseMatrix64F> alg = create(3,3);


        alg.decompose(A);


        DenseMatrix64F L_provided = RandomMatrices.createRandom(3,3,rand);
        DenseMatrix64F U_provided = RandomMatrices.createRandom(3,3,rand);


        assertTrue(L_provided == alg.getLower(L_provided));
        assertTrue(U_provided == alg.getUpper(U_provided));


        DenseMatrix64F L_ret = alg.getLower(null);
        DenseMatrix64F U_ret = alg.getUpper(null);


        assertTrue(MatrixFeatures.isEquals(L_provided,L_ret));
        assertTrue(MatrixFeatures.isEquals(U_provided,U_ret));
    }

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        assertTrue(MatrixFeatures.isEquals(U_provided,U_ret));
    }


    @Test
    public void testFat() {
        DenseMatrix64F A = new DenseMatrix64F(2,3, true, 1, 2, 3, 2, 4, 6.1);


        LUDecomposition<DenseMatrix64F> alg = create(2,3);


        assertTrue(alg.decompose(A));
//        assertFalse(alg.isSingular());


        SimpleMatrix L = SimpleMatrix.wrap(alg.getLower(null));
        SimpleMatrix U = SimpleMatrix.wrap(alg.getUpper(null));
        SimpleMatrix P = SimpleMatrix.wrap(alg.getPivot(null));


        DenseMatrix64F A_found = P.mult(L).mult(U).getMatrix();


        assertTrue(MatrixFeatures.isIdentical(A_found,A,1e-8));
    }

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        assertTrue(MatrixFeatures.isIdentical(A_found,A,1e-8));
    }


    @Test
    public void testTall() {
        DenseMatrix64F A = new DenseMatrix64F(3,2, true, 1, 2, 3, 2, 4, 6.1);


        LUDecomposition<DenseMatrix64F> alg = create(3,2);


        assertTrue(alg.decompose(A));
//        assertFalse(alg.isSingular());


        SimpleMatrix L = SimpleMatrix.wrap(alg.getLower(null));
        SimpleMatrix U = SimpleMatrix.wrap(alg.getUpper(null));
        SimpleMatrix P = SimpleMatrix.wrap(alg.getPivot(null));


        DenseMatrix64F A_found = P.transpose().mult(L).mult(U).getMatrix();


        assertTrue(MatrixFeatures.isIdentical(A_found,A,1e-8));
    }

View Full Code Here


    Random rand = new Random(234324);


    @Test
    public void extract() {
        DenseMatrix64F A = RandomMatrices.createRandom(5, 5, 0, 1, rand);


        DenseMatrix64F B = new DenseMatrix64F(3,3);


        ImplCommonOps_DenseMatrix64F.extract(A, 1, 2, B, 1, 0,2,3);


        for( int i = 1; i < 3; i++ ) {
            for( int j = 2; j < 5; j++ ) {
                assertEquals(A.get(i,j),B.get(i,j-2),1e-8);
            }
        }
    }

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    @Test
    public void getDiagonal() {
        for( int width = 1; width < 20; width += 2 ) {


            DenseMatrix64F A = RandomMatrices.createSymmetric(width,-1,1,rand);


            TridiagonalSimilarDecomposition<DenseMatrix64F> alg = createDecomposition();


            assertTrue(safeDecomposition(alg,A));


            DenseMatrix64F T = alg.getT(null);


            double diag[] = new double[width];
            double off[] = new double[width];


            alg.getDiagonal(diag,off);
            assertEquals(T.get(0,0),diag[0],1e-8);
            for( int i = 1; i < width; i++ ) {
                assertEquals(T.get(i,i),diag[i],1e-8);
                assertEquals(T.get(i-1,i),off[i-1],1e-8);
            }
        }
    }

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