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}.

            /* Make adjustments for sign */
            if (x < 0.0) {
                if (y > 0.0)
                    refdfp = field.getPi().add(refdfp);
                else
                    refdfp = refdfp.subtract(field.getPi());
            }


            double ref = refdfp.toDouble();
            double err = (tst - ref) / ref;

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            /* Make adjustments for sign */
            if (x < 0.0) {
                if (y > 0.0)
                    refdfp = field.getPi().add(refdfp);
                else
                    refdfp = refdfp.subtract(field.getPi());
            }


            double ref = refdfp.toDouble();
            double err = (tst - ref) / ref;

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            Dfp h = step.divide(field.newDfp(100000));
            for (int j = 0; j < 20; ++j) {
                Dfp x = field.newDfp(j).multiply(step);
                Dfp[] y  = interpolator.value(x);
                Dfp[] yP = interpolator.value(x.add(h));
                Dfp[] yM = interpolator.value(x.subtract(h));
                Assert.assertEquals(p.length, y.length);
                for (int k = 0; k < p.length; ++k) {
                    Assert.assertEquals(p[k].value(x.getReal()),
                                        y[k].getReal(),
                                        1.0e-8 * FastMath.abs(p[k].value(x.getReal())));

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            /* Make adjustments for sign */
            if (x < 0.0) {
                if (y > 0.0)
                    refdfp = field.getPi().add(refdfp);
                else
                    refdfp = refdfp.subtract(field.getPi());
            }


            double ref = refdfp.toDouble();
            double err = (tst - ref) / ref;

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            /* Make adjustments for sign */
            if (x < 0.0) {
                if (y > 0.0)
                    refdfp = field.getPi().add(refdfp);
                else
                    refdfp = refdfp.subtract(field.getPi());
            }


            double ref = refdfp.toDouble();
            double err = (tst - ref) / ref;

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                                     double[] sample,
                                     double learningRate) {
        final ArrayRealVector c = new ArrayRealVector(current, false);
        final ArrayRealVector s = new ArrayRealVector(sample, false);
        // c + learningRate * (s - c)
        return s.subtract(c).mapMultiplyToSelf(learningRate).add(c).toArray();
    }
}

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                                     double[] sample,
                                     double learningRate) {
        final ArrayRealVector c = new ArrayRealVector(current, false);
        final ArrayRealVector s = new ArrayRealVector(sample, false);
        // c + learningRate * (s - c)
        return s.subtract(c).mapMultiplyToSelf(learningRate).add(c).toArray();
    }
}

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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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        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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        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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