Package org.apache.mahout.math.matrix.impl

Examples of org.apache.mahout.math.matrix.impl.DenseDoubleMatrix2D


    @Test
    public void testComputeOrthogonalityIdenticalColumns()
    {
        /** A matrix with identical columns */
        DoubleMatrix2D identical = new DenseDoubleMatrix2D(new double [] []
        {
            {
                0.00, 0.00, 0.00
            },
            {
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    }

    @Test
    public void testMinSparseness()
    {
        final DoubleMatrix2D sparse = new DenseDoubleMatrix2D(2, 2);
        assertThat(MatrixUtils.computeSparseness(sparse)).isEqualTo(0);
    }
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    }

    @Test
    public void testMaxSparseness()
    {
        final DoubleMatrix2D sparse = new DenseDoubleMatrix2D(2, 2);
        sparse.assign(3);
        assertThat(MatrixUtils.computeSparseness(sparse)).isEqualTo(1);
    }
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    }

    @Test
    public void frobeniusNorm()
    {
        assertThat(MatrixUtils.frobeniusNorm(new DenseDoubleMatrix2D(new double [] []
        {
            {
                1, -1
            },
            {
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    }

    @Test
    public void sortedRowsView()
    {
        final DenseDoubleMatrix2D input = new DenseDoubleMatrix2D(new double [] []
        {
            {
                1, -1
            },
            {
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    /**
     *
     */
    private DoubleMatrix2D nonZeroColumnMatrix()
    {
        return new DenseDoubleMatrix2D(new double [] []
        {
            {
                -1.00, 0.00, 2.00, 10.00
            },
            {
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    /**
     *
     */
    private DoubleMatrix2D zeroColumnMatrix()
    {
        return new DenseDoubleMatrix2D(new double [] []
        {
            {
                0.00, 0.00, 0.00, -7.00
            },
            {
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    {
        final VectorSpaceModelContext vsmContext = context.vsmContext;
        if (vsmContext.termDocumentMatrix.columns() == 0
            || vsmContext.termDocumentMatrix.rows() == 0)
        {
            context.baseMatrix = new DenseDoubleMatrix2D(
                vsmContext.termDocumentMatrix.rows(),
                vsmContext.termDocumentMatrix.columns());
            return;
        }
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        factory.setK(2);

        PartialSingularValueDecomposition factorization = (PartialSingularValueDecomposition) factory
            .factorize(A);

        DoubleMatrix2D expectedU = new DenseDoubleMatrix2D(new double [] []
        {
            {
                0, 0.752646
            },
            {
                -0.651927, 0
            },
            {
                -0.651927, 0
            },
            {
                -0.387277, 0
            },
            {
                0, 0.658425
            }
        });

        DoubleMatrix2D expectedV = new DenseDoubleMatrix2D(new double [] []
        {
            {
                -0.557873, 0
            },
            {
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    }

    @Test
    public void testUnorderedNMFED()
    {
        DoubleMatrix2D expectedU = new DenseDoubleMatrix2D(new double [] []
        {
            {
                6.1201e-011, 0.99592
            },
            {
                1.3886, 6.9024e-070
            },
            {
                1.3886, 8.2856e-070
            },
            {
                0.82488, 7.4263e-066
            },
            {
                5.8999e-011, 0.87124
            }
        });

        DoubleMatrix2D expectedV = new DenseDoubleMatrix2D(new double [] []
        {
            {
                0.43087, 1.0265e-069
            },
            {
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