Package org.apache.commons.math.linear

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

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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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    protected RealVector calculateBeta() {
        RealMatrix OI = getOmegaInverse();
        RealMatrix XT = X.transpose();
        RealMatrix XTOIX = XT.multiply(OI).multiply(X);
        RealMatrix inverse = new LUDecompositionImpl(XTOIX).getSolver().getInverse();
        return inverse.multiply(XT).multiply(OI).operate(Y);
    }

    /**
     * Calculates the variance on the beta.
     * <pre>
 
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        RealMatrix matrix = MatrixUtils.createRealMatrix(testData);
        LUDecomposition lu = new LUDecompositionImpl(matrix);
        RealMatrix l = lu.getL();
        RealMatrix u = lu.getU();
        RealMatrix p = lu.getP();
        double norm = l.multiply(u).subtract(p.multiply(matrix)).getNorm();
        assertEquals(0, norm, normTolerance);

        matrix = MatrixUtils.createRealMatrix(testDataMinus);
        lu = new LUDecompositionImpl(matrix);
        l = lu.getL();
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        matrix = MatrixUtils.createRealMatrix(testDataMinus);
        lu = new LUDecompositionImpl(matrix);
        l = lu.getL();
        u = lu.getU();
        p = lu.getP();
        norm = l.multiply(u).subtract(p.multiply(matrix)).getNorm();
        assertEquals(0, norm, normTolerance);

        matrix = MatrixUtils.createRealIdentityMatrix(17);
        lu = new LUDecompositionImpl(matrix);
        l = lu.getL();
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        matrix = MatrixUtils.createRealIdentityMatrix(17);
        lu = new LUDecompositionImpl(matrix);
        l = lu.getL();
        u = lu.getU();
        p = lu.getP();
        norm = l.multiply(u).subtract(p.multiply(matrix)).getNorm();
        assertEquals(0, norm, normTolerance);

        matrix = MatrixUtils.createRealMatrix(singular);
        lu = new LUDecompositionImpl(matrix);
        assertFalse(lu.getSolver().isNonSingular());
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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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    protected RealVector calculateBeta() {
        RealMatrix OI = getOmegaInverse();
        RealMatrix XT = X.transpose();
        RealMatrix XTOIX = XT.multiply(OI).multiply(X);
        RealMatrix inverse = new LUDecompositionImpl(XTOIX).getSolver().getInverse();
        return inverse.multiply(XT).multiply(OI).operate(Y);
    }

    /**
     * Calculates the variance on the beta by GLS.
     * <pre>
 
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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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