Examples of org.apache.commons.math3.complex.Complex.subtract()

Package org.apache.commons.math3.complex

Class org.apache.commons.math3.complex.Complex

Examples of org.apache.commons.math3.complex.Complex.subtract()

org.apache.commons.math3.complex.Complex.subtract()
Returns a {@code Complex} whose value is{@code (this - subtrahend)}. Uses the definitional formula
```
  (a + bi) - (c + di) = (a-c) + (b-d)i  
```
If either {@code this} or {@code subtrahend} has a {@code NaN]} value in either part,{@link #NaN} is returned; otherwise infinite and {@code NaN} values arereturned in the parts of the result according to the rules for {@link java.lang.Double} arithmetic. @param subtrahend value to be subtracted from this {@code Complex}. @return {@code this - subtrahend}. @throws NullArgumentException if {@code subtrahend} is {@code null}.

         * Verify that residuals computed using the hat matrix are close to
         * what we get from direct computation, i.e. r = (I - H) y
         */
        double[] residuals = model.estimateResiduals();
        RealMatrix I = MatrixUtils.createRealIdentityMatrix(10);
        double[] hatResiduals = I.subtract(hat).operate(model.getY()).toArray();
        TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
    }


    /**
     * test calculateYVariance

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        stateEstimation = stateEstimation.add(kalmanGain.operate(innovation));


        // update covariance of prediction error
        // P(k) = (I - K * H) * P(k)-
        RealMatrix identity = MatrixUtils.createRealIdentityMatrix(kalmanGain.getRowDimension());
        errorCovariance = identity.subtract(kalmanGain.multiply(measurementMatrix)).multiply(errorCovariance);
    }
}

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           { 0.0,  -32 / s1553, -23 / s1553 }
       });


       // check values against known references
       RealMatrix u = transformer.getU();
       Assert.assertEquals(0, u.subtract(uRef).getNorm(), 1.0e-14);
       RealMatrix b = transformer.getB();
       Assert.assertEquals(0, b.subtract(bRef).getNorm(), 1.0e-14);
       RealMatrix v = transformer.getV();
       Assert.assertEquals(0, v.subtract(vRef).getNorm(), 1.0e-14);

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       // check values against known references
       RealMatrix u = transformer.getU();
       Assert.assertEquals(0, u.subtract(uRef).getNorm(), 1.0e-14);
       RealMatrix b = transformer.getB();
       Assert.assertEquals(0, b.subtract(bRef).getNorm(), 1.0e-14);
       RealMatrix v = transformer.getV();
       Assert.assertEquals(0, v.subtract(vRef).getNorm(), 1.0e-14);


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

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


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

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                { 0.0, -1.0 }
        });


        // check values against known references
        RealMatrix u = transformer.getU();
        Assert.assertEquals(0, u.subtract(uRef).getNorm(), 1.0e-14);
        RealMatrix b = transformer.getB();
        Assert.assertEquals(0, b.subtract(bRef).getNorm(), 1.0e-14);
        RealMatrix v = transformer.getV();
        Assert.assertEquals(0, v.subtract(vRef).getNorm(), 1.0e-14);

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        // check values against known references
        RealMatrix u = transformer.getU();
        Assert.assertEquals(0, u.subtract(uRef).getNorm(), 1.0e-14);
        RealMatrix b = transformer.getB();
        Assert.assertEquals(0, b.subtract(bRef).getNorm(), 1.0e-14);
        RealMatrix v = transformer.getV();
        Assert.assertEquals(0, v.subtract(vRef).getNorm(), 1.0e-14);


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

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


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

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         * Verify that residuals computed using the hat matrix are close to
         * what we get from direct computation, i.e. r = (I - H) y
         */
        double[] residuals = model.estimateResiduals();
        RealMatrix I = MatrixUtils.createRealIdentityMatrix(10);
        double[] hatResiduals = I.subtract(hat).operate(model.getY()).toArray();
        TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
    }


    /**
     * test calculateYVariance

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         * Verify that residuals computed using the hat matrix are close to
         * what we get from direct computation, i.e. r = (I - H) y
         */
        double[] residuals = model.estimateResiduals();
        RealMatrix I = MatrixUtils.createRealIdentityMatrix(10);
        double[] hatResiduals = I.subtract(hat).operate(model.getY()).toArray();
        TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
    }


    /**
     * test calculateYVariance

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