Examples of org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()

Class org.apache.commons.math3.random.JDKRandomGenerator

Examples of org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()

org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()
Returns the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence. @return the next pseudorandom, Gaussian ("normally") distributeddouble value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence

        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict();


            // Simulate the process
            pNoise.setEntry(0, processNoise * rand.nextGaussian());


            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement

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            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement
            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());


            // z = H * x + m_noise
            RealVector z = H.operate(x).add(mNoise);


            filter.correct(z);

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        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict(u);


            // Simulate the process
            RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.nextGaussian());


            // x = A * x + B * u + pNoise
            x = A.operate(x).add(B.operate(u)).add(pNoise);


            // Simulate the measurement

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            // x = A * x + B * u + pNoise
            x = A.operate(x).add(B.operate(u)).add(pNoise);


            // Simulate the measurement
            double mNoise = measurementNoise * rand.nextGaussian();


            // z = H * x + m_noise
            RealVector z = H.operate(x).mapAdd(mNoise);


            filter.correct(z);

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        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict();


            // Simulate the process
            pNoise.setEntry(0, processNoise * rand.nextGaussian());


            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement

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            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement
            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());


            // z = H * x + m_noise
            RealVector z = H.operate(x).add(mNoise);


            filter.correct(z);

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        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict(u);


            // Simulate the process
            RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.nextGaussian());


            // x = A * x + B * u + pNoise
            x = A.operate(x).add(B.operate(u)).add(pNoise);


            // Simulate the measurement

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            // x = A * x + B * u + pNoise
            x = A.operate(x).add(B.operate(u)).add(pNoise);


            // Simulate the measurement
            double mNoise = measurementNoise * rand.nextGaussian();


            // z = H * x + m_noise
            RealVector z = H.operate(x).mapAdd(mNoise);


            filter.correct(z);

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        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict();


            // Simulate the process
            pNoise.setEntry(0, processNoise * rand.nextGaussian());


            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement

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            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement
            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());


            // z = H * x + m_noise
            RealVector z = H.operate(x).add(mNoise);


            filter.correct(z);

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