Package org.apache.commons.math3.random

Examples of org.apache.commons.math3.random.RandomGenerator

112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
        final List<T> parent2Rep = second.getRepresentation();
        // and of the children
        final List<T> child1Rep = new ArrayList<T>(length);
        final List<T> child2Rep = new ArrayList<T>(length);

        final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();

        for (int index = 0; index < length; index++) {

            if (random.nextDouble() < ratio) {
                // swap the bits -> take other parent
                child1Rep.add(parent2Rep.get(index));
                child2Rep.add(parent1Rep.get(index));
            } else {
                child1Rep.add(parent1Rep.get(index));
View Full Code Here
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
        final List<T> parent2Rep = second.getRepresentation();
        // and of the children
        final List<T> child1Rep = new ArrayList<T>(length);
        final List<T> child2Rep = new ArrayList<T>(length);

        final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();

        List<T> c1 = child1Rep;
        List<T> c2 = child2Rep;

        int remainingPoints = crossoverPoints;
        int lastIndex = 0;
        for (int i = 0; i < crossoverPoints; i++, remainingPoints--) {
            // select the next crossover point at random
            final int crossoverIndex = 1 + lastIndex + random.nextInt(length - lastIndex - remainingPoints);

            // copy the current segment
            for (int j = lastIndex; j < crossoverIndex; j++) {
                c1.add(parent1Rep.get(j));
                c2.add(parent2Rep.get(j));
View Full Code Here
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
     * @return the population for the next generation.
     */
    public Population nextGeneration(final Population current) {
        Population nextGeneration = current.nextGeneration();

        RandomGenerator randGen = getRandomGenerator();

        while (nextGeneration.getPopulationSize() < nextGeneration.getPopulationLimit()) {
            // select parent chromosomes
            ChromosomePair pair = getSelectionPolicy().select(current);

            // crossover?
            if (randGen.nextDouble() < getCrossoverRate()) {
                // apply crossover policy to create two offspring
                pair = getCrossoverPolicy().crossover(pair.getFirst(), pair.getSecond());
            }

            // mutation?
            if (randGen.nextDouble() < getMutationRate()) {
                // apply mutation policy to the chromosomes
                pair = new ChromosomePair(
                    getMutationPolicy().mutate(pair.getFirst()),
                    getMutationPolicy().mutate(pair.getSecond()));
            }
View Full Code Here
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
        final List<T> child2 = new ArrayList<T>(length);
        // sets of already inserted items for quick access
        final Set<T> child1Set = new HashSet<T>(length);
        final Set<T> child2Set = new HashSet<T>(length);

        final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();
        // choose random points, making sure that lb < ub.
        int a = random.nextInt(length);
        int b;
        do {
            b = random.nextInt(length);
        } while (a == b);
        // determine the lower and upper bounds
        final int lb = FastMath.min(a, b);
        final int ub = FastMath.max(a, b);
View Full Code Here
60
61
62
63
64
65
66
67
68
69
70
                                            double b,
                                            double sigma,
                                            double lo,
                                            double hi,
                                            long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        slope = a;
        intercept = b;
        error = new NormalDistribution(rng, 0, sigma,
                                       NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        x = new UniformRealDistribution(rng, lo, hi,
View Full Code Here
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
        assertVectorEquals(expectedInitialState, filter.getStateEstimation());

        RealVector pNoise = new ArrayRealVector(1);
        RealVector mNoise = new ArrayRealVector(1);

        RandomGenerator rand = new JDKRandomGenerator();
        // 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
            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
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
        // check the initial state
        double[] expectedInitialState = new double[] { 0.0, 0.0 };
        assertVectorEquals(expectedInitialState, filter.getStateEstimation());

        RandomGenerator rand = new JDKRandomGenerator();

        RealVector tmpPNoise = new ArrayRealVector(
                new double[] { FastMath.pow(dt, 2d) / 2d, dt });

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

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

            filter.correct(z);
View Full Code Here
385
386
387
388
389
390
391
392
393
394
395
        final ProcessModel pm = new DefaultProcessModel(A, B, Q, initialState, initialErrorCovariance);
        final MeasurementModel mm = new DefaultMeasurementModel(H, R);
        final KalmanFilter filter = new KalmanFilter(pm, mm);

        final RandomGenerator rng = new Well19937c(1000);
        final NormalDistribution dist = new NormalDistribution(rng, 0, measurementNoise);

        for (int i = 0; i < iterations; i++) {
            // get the "real" cannonball position
            double x = cannonball.getX();
View Full Code Here
52
53
54
55
56
57
58
59
60
61
62
                                      double y,
                                      double radius,
                                      double xSigma,
                                      double ySigma,
                                      long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
View Full Code Here
92
93
94
95
96
97
98
99
100
101
102
    }

    @Test
    public void testGetters() {
        final DistanceMeasure measure = new CanberraDistance();
        final RandomGenerator random = new JDKRandomGenerator();
        final FuzzyKMeansClusterer<DoublePoint> clusterer =
                new FuzzyKMeansClusterer<DoublePoint>(3, 2.0, 100, measure, 1e-6, random);

        Assert.assertEquals(3, clusterer.getK());
        Assert.assertEquals(2.0, clusterer.getFuzziness(), 1e-6);
View Full Code Here
TOP

Related Classes of org.apache.commons.math3.random.RandomGenerator

  • net.myrrix.online.factorizer.als.AlternatingLeastSquares
  • net.myrrix.online.som.SelfOrganizingMaps
  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.LogitTest
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.interpolation.BicubicSplineInterpolatingFunctionTest
  • org.apache.commons.math3.analysis.interpolation.BicubicSplineInterpolatorTest
    • Copyright © 2018 www.massapicom. All rights reserved.
    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.