Package org.ejml.data

Examples of org.ejml.data.DenseMatrix64F

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     */
    @Test
    public void compareTo4x4() {
        double[] mat = new double[]{5 ,-2 ,-4 ,0.5, 0.1, 91, 8, 66, 1, -2, 10, -4, -0.2, 7, -4, 0.8};

        double val = NaiveDeterminant.recursive(new DenseMatrix64F(4,4,true,mat));

        DeterminantFromMinor minor = new DeterminantFromMinor(4,3);
        double minorVal = minor.compute(new DenseMatrix64F(4,4, true, mat));

        assertEquals(val,minorVal,1e-6);
    }
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        assertTrue(MatrixFeatures.isIdentity(I,1e-8));
    }

    @Test
    public void invert() {
        DenseMatrix64F L = createRandomLowerTriangular();

        DenseMatrix64F L_inv = L.copy();

        TriangularSolver.invertLower(L.data,L_inv.data,L.numRows);

        DenseMatrix64F I = new DenseMatrix64F(L.numRows,L.numCols);

        CommonOps.mult(L,L_inv,I);

        assertTrue(MatrixFeatures.isIdentity(I,1e-8));
    }
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    @Test
    public void insertIntoThis() {
        SimpleMatrix A = new SimpleMatrix(6,4);
        SimpleMatrix B = SimpleMatrix.random(3,2, 0, 1, rand);

        DenseMatrix64F A_ = A.getMatrix().copy();

        A.insertIntoThis(1,2,B);

        CommonOps.insert(B.getMatrix(), A_, 1,2);
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        if( !prefComputeU )
            throw new IllegalArgumentException("As requested U was not computed.");
        if( transpose )
            return Ut;

        U = new DenseMatrix64F(Ut.numCols,Ut.numRows);
        CommonOps.transpose(Ut,U);

        return U;
    }
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        assertTrue(MatrixFeatures.isIdentity(I,1e-8));
    }

    @Test
    public void solveL_vector() {
        DenseMatrix64F L = createRandomLowerTriangular();

        DenseMatrix64F L_inv = L.copy();
        UnrolledInverseFromMinor.inv(L_inv,L_inv);

        DenseMatrix64F B = RandomMatrices.createRandom(3,1,rand);
        DenseMatrix64F expected = RandomMatrices.createRandom(3,1,rand);
        DenseMatrix64F found = B.copy();

        TriangularSolver.solveL(L.data,found.data,3);
        CommonOps.mult(L_inv,B,expected);

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    @Test
    public void extractDiag() {
        SimpleMatrix a = SimpleMatrix.random(3,4, 0, 1, rand);

        DenseMatrix64F found = a.extractDiag().getMatrix();
        DenseMatrix64F expected = new DenseMatrix64F(3,1);

        CommonOps.extractDiag(a.getMatrix(),expected);

        EjmlUnitTests.assertEquals(found,expected,1e-8);
    }
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    @Test
    public void compareTo5x5() {
        double[] mat = new double[]{5 ,-2, -4, 0.5, -0.3, 0.1, 91, 8, 66, 13, 1, -2, 10, -4, -0.01, -0.2, 7, -4, 0.8, -22, 5, 19, -23, 0.001, 87};

        DeterminantFromMinor minor = new DeterminantFromMinor(5);
        double minorVal = minor.compute(new DenseMatrix64F(5,5, true, mat));

        assertEquals(-4745296.629148000851274,minorVal,1e-8);
    }
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        assertTrue(MatrixFeatures.isIdentical(expected,found,1e-8));
    }

    private DenseMatrix64F createRandomLowerTriangular() {
        DenseMatrix64F L = RandomMatrices.createRandom(3,3,rand);
        for( int i = 0; i < L.numRows; i++ ) {
            for( int j = i+1; j < L.numCols; j++ ) {
                L.set(i,j,0);
            }
        }
        return L;
    }
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    public void compareToNaive10x10() {
        Random rand = new Random(0xfff);

        int width = 10;

        DenseMatrix64F A = RandomMatrices.createRandom(width,width,rand);

        DeterminantFromMinor minor = new DeterminantFromMinor(width);
        double minorVal = minor.compute(new DenseMatrix64F(width,width, true, A.data));

        double recVal = NaiveDeterminant.recursive(new DenseMatrix64F(width,width, true, A.data));

        assertEquals(recVal,minorVal,1e-6);
    }
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        return L;
    }

    @Test
    public void solveL_matrix() {
        DenseMatrix64F L = createRandomLowerTriangular();

        DenseMatrix64F L_inv = L.copy();
        UnrolledInverseFromMinor.inv(L_inv,L_inv);

        DenseMatrix64F B = RandomMatrices.createRandom(3,4,rand);
        DenseMatrix64F expected = RandomMatrices.createRandom(3,4,rand);
        DenseMatrix64F found = B.copy();

        TriangularSolver.solveL(L.data,found.data,3,4);
        CommonOps.mult(L_inv,B,expected);

        assertTrue(MatrixFeatures.isIdentical(expected,found,1e-8));
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Related Classes of org.ejml.data.DenseMatrix64F

  • fr.lip6.jkernelmachines.util.algebra.ejml.EJMLMatrixOperations
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  • org.ejml.alg.block.BlockMatrixOps
  • org.ejml.alg.block.decomposition.chol.TestBlockCholeskyOuterForm
  • org.ejml.alg.block.decomposition.chol.TestBlockInnerCholesky
  • org.ejml.alg.block.decomposition.hessenberg.TestTridiagonalBlockHelper
  • org.ejml.alg.block.decomposition.qr.TestBlockHouseHolder
  • org.ejml.alg.block.linsol.chol.TestBlockCholeskyOuterSolver
  • org.ejml.alg.block.TestBlockInnerTriangularSolver
  • org.ejml.alg.block.TestBlockMatrixOps
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