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

Class org.apache.commons.math3.random.RandomData

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

org.apache.commons.math3.random.RandomData.nextGaussian()
tl.nist.gov/div898/handbook/eda/section3/eda3661.htm"> Normal Distribution
@param mu the mean of the distribution @param sigma the standard deviation of the distribution @return a random value following the specified Gaussian distribution @throws NotStrictlyPositiveException if {@code sigma <= 0}.

        // 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

View Full Code Here


            // 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);

View Full Code Here

        // 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

View Full Code Here


            // 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);

View Full Code Here

        // 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

View Full Code Here


            // 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);

View Full Code Here

          float[] oldSmallerVector = entry.getValue();
          float[] newLargerVector = new float[features];
          System.arraycopy(oldSmallerVector, 0, newLargerVector, 0, oldSmallerVector.length);
          // Fill in new dimensions with random values
          for (int i = oldSmallerVector.length; i < newLargerVector.length; i++) {
            newLargerVector[i] = (float) random.nextGaussian();
          }
          SimpleVectorMath.normalize(newLargerVector);          
          randomY.put(entry.getKey(), newLargerVector);
        }

View Full Code Here

        // 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

View Full Code Here


            // 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);

View Full Code Here

        // 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

View Full Code Here

0 1 2 3

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.