Package org.apache.commons.math.linear

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

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    private void checkAEqualUSVt(RealMatrix matrix) {
        BiDiagonalTransformer transformer = new BiDiagonalTransformer(matrix);
        RealMatrix u = transformer.getU();
        RealMatrix b = transformer.getB();
        RealMatrix v = transformer.getV();
        double norm = u.multiply(b).multiply(v.transpose()).subtract(matrix).getNorm();
        Assert.assertEquals(0, norm, 1.0e-14);
    }

    @Test
    public void testUOrthogonal() {
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                }
            }
        }

        // Compute and return Hat matrix
        return Q.multiply(augI).multiply(Q.transpose());
    }

    /**
     * <p>Returns the sum of squared deviations of Y from its mean.</p>
     *
 
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    @Override
    protected RealMatrix calculateBetaVariance() {
        int p = X.getColumnDimension();
        RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
        RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
        return Rinv.multiply(Rinv.transpose());
    }

}
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    public void testLTTransposed() throws MathException {
        RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
        CholeskyDecomposition llt = new CholeskyDecompositionImpl(matrix);
        RealMatrix l  = llt.getL();
        RealMatrix lt = llt.getLT();
        double norm = l.subtract(lt.transpose()).getNorm();
        assertEquals(0, norm, 1.0e-15);
    }

    /** test matrices values */
    @Test
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    private void checkAEqualUSVt(RealMatrix matrix) {
        BiDiagonalTransformer transformer = new BiDiagonalTransformer(matrix);
        RealMatrix u = transformer.getU();
        RealMatrix b = transformer.getB();
        RealMatrix v = transformer.getV();
        double norm = u.multiply(b).multiply(v.transpose()).subtract(matrix).getNorm();
        Assert.assertEquals(0, norm, 1.0e-14);
    }

    @Test
    public void testUOrthogonal() {
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    public void testLTTransposed() throws MathException {
        RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
        CholeskyDecomposition llt = new CholeskyDecompositionImpl(matrix);
        RealMatrix l  = llt.getL();
        RealMatrix lt = llt.getLT();
        double norm = l.subtract(lt.transpose()).getNorm();
        assertEquals(0, norm, 1.0e-15);
    }

    /** test matrices values */
    @Test
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    private void checkAEqualUSVt(RealMatrix matrix) {
        BiDiagonalTransformer transformer = new BiDiagonalTransformer(matrix);
        RealMatrix u = transformer.getU();
        RealMatrix b = transformer.getB();
        RealMatrix v = transformer.getV();
        double norm = u.multiply(b).multiply(v.transpose()).subtract(matrix).getNorm();
        assertEquals(0, norm, 1.0e-14);
    }

    public void testUOrthogonal() {
        checkOrthogonal(new BiDiagonalTransformer(MatrixUtils.createRealMatrix(testSquare)).getU());
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    public void checkAEqualUSVt(final RealMatrix matrix) {
        SingularValueDecomposition svd = new SingularValueDecompositionImpl(matrix);
        RealMatrix u = svd.getU();
        RealMatrix s = svd.getS();
        RealMatrix v = svd.getV();
        double norm = u.multiply(s).multiply(v.transpose()).subtract(matrix).getNorm();
        assertEquals(0, norm, normTolerance);

    }

    /** test that U is orthogonal */
 
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                }
            }
        }
       
        // Compute and return Hat matrix
        return Q.multiply(augI).multiply(Q.transpose());
    }
  
    /**
     * Loads new x sample data, overriding any previous sample
     *
 
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    @Override
    protected RealMatrix calculateBetaVariance() {
        int p = X.getColumnDimension();
        RealMatrix Raug = qr.getR().getSubMatrix(0, p - 1 , 0, p - 1);
        RealMatrix Rinv = new LUDecompositionImpl(Raug).getSolver().getInverse();
        return Rinv.multiply(Rinv.transpose());
    }
   

    /**
     * <p>Calculates the variance on the Y by OLS.
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