Examples of org.apache.commons.math.linear.RealMatrix.subtract()

Class org.apache.commons.math.linear.RealMatrix

Examples of org.apache.commons.math.linear.RealMatrix.subtract()

org.apache.commons.math.linear.RealMatrix.subtract()
Compute this minus m. @param m matrix to be subtracted @return this - m @throws IllegalArgumentException if m is not the same size as this

        RealMatrix u = transformer.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), 1.0e-14);
        RealMatrix b = transformer.getB();
        assertEquals(0, b.subtract(bRef).getNorm(), 1.0e-14);
        RealMatrix v = transformer.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), 1.0e-14);


        // check the same cached instance is returned the second time
        assertTrue(u == transformer.getU());
        assertTrue(b == transformer.getB());
        assertTrue(v == transformer.getV());

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                { -51.0 / 1024,  85.0 / 1024,  45.0 / 1024, -75.0 / 1024 },
                { -75.0 / 1024,  45.0 / 1024,  85.0 / 1024, -51.0 / 1024 },
                {  45.0 / 1024, -75.0 / 1024, -51.0 / 1024,  85.0 / 1024 }
        }, false);
        assertEquals(0.0,
                     fullCovariance.subtract(svd.getCovariance(0.0)).getNorm(),
                     1.0e-14);


        RealMatrix halfCovariance = new Array2DRowRealMatrix(new double[][] {
                {   5.0 / 1024,  -3.0 / 1024,   5.0 / 1024,  -3.0 / 1024 },
                {  -3.0 / 1024,   5.0 / 1024,  -3.0 / 1024,   5.0 / 1024 },

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                {  -3.0 / 1024,   5.0 / 1024,  -3.0 / 1024,   5.0 / 1024 },
                {   5.0 / 1024,  -3.0 / 1024,   5.0 / 1024,  -3.0 / 1024 },
                {  -3.0 / 1024,   5.0 / 1024,  -3.0 / 1024,   5.0 / 1024 }
        }, false);
        assertEquals(0.0,
                     halfCovariance.subtract(svd.getCovariance(6.0)).getNorm(),
                     1.0e-14);


    }


    /** test A = USVt */

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                { 3.0 / 5.0, -4.0 / 5.0 }
        });


        // check values against known references
        RealMatrix u = svd.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), normTolerance);
        RealMatrix s = svd.getS();
        assertEquals(0, s.subtract(sRef).getNorm(), normTolerance);
        RealMatrix v = svd.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), normTolerance);

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        // check values against known references
        RealMatrix u = svd.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), normTolerance);
        RealMatrix s = svd.getS();
        assertEquals(0, s.subtract(sRef).getNorm(), normTolerance);
        RealMatrix v = svd.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), normTolerance);


        // check the same cached instance is returned the second time
        assertTrue(u == svd.getU());

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        RealMatrix u = svd.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), normTolerance);
        RealMatrix s = svd.getS();
        assertEquals(0, s.subtract(sRef).getNorm(), normTolerance);
        RealMatrix v = svd.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), normTolerance);


        // check the same cached instance is returned the second time
        assertTrue(u == svd.getU());
        assertTrue(s == svd.getS());
        assertTrue(v == svd.getV());

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        // check values against known references
        SingularValueDecomposition svd =
            new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testNonSquare));
        RealMatrix u = svd.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), normTolerance);
        RealMatrix s = svd.getS();
        assertEquals(0, s.subtract(sRef).getNorm(), normTolerance);
        RealMatrix v = svd.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), normTolerance);

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        SingularValueDecomposition svd =
            new SingularValueDecompositionImpl(MatrixUtils.createRealMatrix(testNonSquare));
        RealMatrix u = svd.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), normTolerance);
        RealMatrix s = svd.getS();
        assertEquals(0, s.subtract(sRef).getNorm(), normTolerance);
        RealMatrix v = svd.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), normTolerance);


        // check the same cached instance is returned the second time
        assertTrue(u == svd.getU());

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        RealMatrix u = svd.getU();
        assertEquals(0, u.subtract(uRef).getNorm(), normTolerance);
        RealMatrix s = svd.getS();
        assertEquals(0, s.subtract(sRef).getNorm(), normTolerance);
        RealMatrix v = svd.getV();
        assertEquals(0, v.subtract(vRef).getNorm(), normTolerance);


        // check the same cached instance is returned the second time
        assertTrue(u == svd.getU());
        assertTrue(s == svd.getS());
        assertTrue(v == svd.getV());

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        RealMatrix   xRef = createTestMatrix(r, q, BlockRealMatrix.BLOCK_SIZE + 3);
        RealMatrix   b    = a.multiply(xRef);
        RealMatrix   x = new QRDecompositionImpl(a).getSolver().solve(b);


        // too many equations, the system cannot be solved at all
        assertTrue(x.subtract(xRef).getNorm() / (p * q) > 0.01);


        // the last unknown should have been set to 0
        assertEquals(0.0, x.getSubMatrix(p, q - 1, 0, x.getColumnDimension() - 1).getNorm());


    }

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