Examples of 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

Examples of org.ejml.data.DenseMatrix64F


    Random rand = new Random(234);


    @Test
    public void general() {
        DenseMatrix64F A = RandomMatrices.createRandom(5,4,rand);
        DenseMatrix64F x = RandomMatrices.createRandom(4,1,rand);
        DenseMatrix64F y = new DenseMatrix64F(5,1);


        LinearSolver<DenseMatrix64F> solver = LinearSolverFactory.general(A.numRows, A.numCols);


        standardTest(A, x, y, solver);
    }

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Examples of org.ejml.data.DenseMatrix64F

        standardTest(A, x, y, solver);
    }


    @Test
    public void linear() {
        DenseMatrix64F A = RandomMatrices.createRandom(4,4,rand);
        DenseMatrix64F x = RandomMatrices.createRandom(4,1,rand);
        DenseMatrix64F y = new DenseMatrix64F(4,1);


        LinearSolver<DenseMatrix64F> solver = LinearSolverFactory.linear(A.numRows);


        standardTest(A, x, y, solver);
    }

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Examples of org.ejml.data.DenseMatrix64F

        standardTest(A, x, y, solver);
    }


    @Test
    public void leastSquares() {
        DenseMatrix64F A = RandomMatrices.createRandom(5,4,rand);
        DenseMatrix64F x = RandomMatrices.createRandom(4,1,rand);
        DenseMatrix64F y = new DenseMatrix64F(5,1);


        LinearSolver<DenseMatrix64F> solver = LinearSolverFactory.leastSquares(A.numRows,A.numCols);


        standardTest(A, x, y, solver);
    }

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Examples of org.ejml.data.DenseMatrix64F

        standardTest(A, x, y, solver);
    }


    @Test
    public void symmetric() {
        DenseMatrix64F A = RandomMatrices.createSymmPosDef(5,rand);
        DenseMatrix64F x = RandomMatrices.createRandom(5,1,rand);
        DenseMatrix64F y = new DenseMatrix64F(5,1);


        LinearSolver<DenseMatrix64F> solver = LinearSolverFactory.symmPosDef(A.numCols);


        standardTest(A, x, y, solver);
    }

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Examples of org.ejml.data.DenseMatrix64F

        standardTest(A, x, y, solver);
    }


    @Test
    public void adjustable() {
        DenseMatrix64F A = RandomMatrices.createRandom(5,4,rand);
        DenseMatrix64F x = RandomMatrices.createRandom(4,1,rand);
        DenseMatrix64F y = new DenseMatrix64F(5,1);


        AdjustableLinearSolver solver = LinearSolverFactory.adjustable();


        standardTest(A, x, y, solver);


        // remove the last observation
        solver.removeRowFromA(y.numRows-1);


        // compute the adjusted solution
        y.numRows--;
        DenseMatrix64F x_adj = new DenseMatrix64F(4,1);
        solver.solve(y,x_adj);


        // The solution should still be the same
        assertTrue(MatrixFeatures.isIdentical(x,x_adj,1e-8));
    }

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Examples of org.ejml.data.DenseMatrix64F

                              LinearSolver<DenseMatrix64F> solver) {
        solver = new LinearSolverSafe<DenseMatrix64F>(solver);


        CommonOps.mult(a,x,y);


        DenseMatrix64F x_found = new DenseMatrix64F(x.numRows,1);


        assertTrue(solver.setA(a));
        solver.solve(y,x_found);


        assertTrue(MatrixFeatures.isIdentical(x,x_found,1e-8));

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Examples of org.ejml.data.DenseMatrix64F

     */
    @Test
    public void compareToLU() {


        for( int N = 2; N <= UnrolledInverseFromMinor.MAX; N++ ) {
            DenseMatrix64F A = RandomMatrices.createRandom(N,N,rand);


            DenseMatrix64F expected = new DenseMatrix64F(N,N);
            DenseMatrix64F found = new DenseMatrix64F(N,N);


            // first compute inverse by LU
            LUDecompositionAlt alg = new LUDecompositionAlt();
            LinearSolverLu solver = new LinearSolverLu(alg);

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Examples of org.ejml.data.DenseMatrix64F

        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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Examples of org.ejml.data.DenseMatrix64F

        if( !prefComputeV )
            throw new IllegalArgumentException("As requested V was not computed.");
        if( transpose )
            return Vt;


        V = new DenseMatrix64F(Vt.numCols,Vt.numRows);
        CommonOps.transpose(Vt,V);


        return V;
    }

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Examples of org.ejml.data.DenseMatrix64F

    public DenseMatrix64F getW( DenseMatrix64F W ) {
        int m = compact ? numSingular : numRows;
        int n = compact ? numSingular : numCols;


        if( W == null )
            W = new DenseMatrix64F(m,n);
        else {
            W.reshape(m,n, false);
            W.zero();
        }

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