List of usage examples for org.apache.commons.math3.random CorrelatedRandomVectorGenerator CorrelatedRandomVectorGenerator
public CorrelatedRandomVectorGenerator(double[] mean, RealMatrix covariance, double small, NormalizedRandomGenerator generator)
From source file:com.itemanalysis.psychometrics.mixture.MvNormalComponentDistribution.java
public void generateStartValues(RealMatrix x, RealMatrix mean, RealMatrix cov) { JDKRandomGenerator jg = new JDKRandomGenerator(); NormalizedRandomGenerator rg = new GaussianRandomGenerator(jg); CorrelatedRandomVectorGenerator sg = null; sg = new CorrelatedRandomVectorGenerator(mean.getColumn(0), cov, 0.00001, rg); mu = new Array2DRowRealMatrix(sg.nextVector()); }
From source file:com.analog.lyric.dimple.test.solvers.sumproduct.TestSampledFactors.java
/** * Adapted from MATLAB test4 in tests/algoGaussian/testSampledFactors.m *///from w ww. ja v a 2 s . c om @Test public void sampledComplexProduct() { // NOTE: test may fail if seed is changed! We keep the number of samples down so that the test doesn't // take too long. Increasing the samples produces better results. testRand.setSeed(42); try (CurrentModel cur = using(new FactorGraph())) { final Complex a = complex("a"); final Complex b = complex("b"); final Complex c = product(a, b); double[] aMean = new double[] { 10, 10 }; RealMatrix aCovariance = randCovariance(2); a.setPrior(new MultivariateNormal(aMean, aCovariance.getData())); double[] bMean = new double[] { -20, 20 }; RealMatrix bCovariance = randCovariance(2); b.setPrior(new MultivariateNormalParameters(bMean, bCovariance.getData())); GaussianRandomGenerator normalGenerator = new GaussianRandomGenerator(testRand); CorrelatedRandomVectorGenerator aGenerator = new CorrelatedRandomVectorGenerator(aMean, aCovariance, 1e-12, normalGenerator); CorrelatedRandomVectorGenerator bGenerator = new CorrelatedRandomVectorGenerator(bMean, bCovariance, 1e-12, normalGenerator); StorelessCovariance expectedCov = new StorelessCovariance(2); final int nSamples = 10000; RealVector expectedMean = MatrixUtils.createRealVector(new double[2]); double[] cSample = new double[2]; for (int i = 0; i < nSamples; ++i) { double[] aSample = aGenerator.nextVector(); double[] bSample = bGenerator.nextVector(); // Compute complex product cSample[0] = aSample[0] * bSample[0] - aSample[1] * bSample[1]; cSample[1] = aSample[0] * bSample[1] + aSample[1] * bSample[0]; expectedMean.addToEntry(0, cSample[0]); expectedMean.addToEntry(1, cSample[1]); expectedCov.increment(cSample); } expectedMean.mapDivideToSelf(nSamples); // normalize SumProductSolverGraph sfg = requireNonNull(cur.graph.setSolverFactory(new SumProductSolver())); sfg.setOption(GibbsOptions.numSamples, nSamples); sfg.solve(); MultivariateNormalParameters cBelief = requireNonNull(c.getBelief()); RealVector observedMean = MatrixUtils.createRealVector(cBelief.getMean()); double scaledMeanDistance = expectedMean.getDistance(observedMean) / expectedMean.getNorm(); // System.out.format("expectedMean = %s\n", expectedMean); // System.out.format("observedMean = %s\n", observedMean); // System.out.println(scaledMeanDistance); assertEquals(0.0, scaledMeanDistance, .02); RealMatrix expectedCovariance = expectedCov.getCovarianceMatrix(); RealMatrix observedCovariance = MatrixUtils.createRealMatrix(cBelief.getCovariance()); RealMatrix diffCovariance = expectedCovariance.subtract(observedCovariance); double scaledCovarianceDistance = diffCovariance.getNorm() / expectedCovariance.getNorm(); // System.out.println(expectedCovariance); // System.out.println(expectedCovariance.getNorm()); // System.out.println(diffCovariance); // System.out.println(diffCovariance.getNorm()); // System.out.println(diffCovariance.getNorm() / expectedCovariance.getNorm()); assertEquals(0.0, scaledCovarianceDistance, .2); } }
From source file:inputHandling.DataGenCorrCommons.java
@Override public TupleList genData(int dimensions, int tupleCount) { logger.info("Generating uniform correlated Data with " + tupleCount + " Tuples in " + dimensions + " dimensions, Coeff.: -" + this.coeff); genMatrices(dimensions);/*from w w w . j ava 2 s . c o m*/ RealMatrix covariance = MatrixUtils.createRealMatrix(cov); RandomGenerator rg = new JDKRandomGenerator(Math.round(seed)); UniformRandomGenerator rawGenerator = new UniformRandomGenerator(rg); double small = 1.0e-12 * covariance.getNorm(); CorrelatedRandomVectorGenerator generator = new CorrelatedRandomVectorGenerator(mean, covariance, small, rawGenerator); // Generate the Tuples TupleList tupleList = new TupleList(dimensions); for (int j = 0; j < tupleCount; j++) { double[] randomVector = generator.nextVector(); Tuple tuple = new Tuple(randomVector); tupleList.add(tuple); } return tupleList; }
From source file:inputHandling.DataGenCorrGaussCommons.java
@Override public TupleList genData(int dimensions, int tupleCount) { logger.info("Generating gaussian correlated Data with " + tupleCount + " Tuples in " + dimensions + " dimensions, Coeff.: -" + this.coeff); genMatrices(dimensions);/* ww w. j ava 2s. c o m*/ RealMatrix covariance = MatrixUtils.createRealMatrix(cov); RandomGenerator rg = new JDKRandomGenerator(Math.round(seed)); GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg); double small = 1.0e-12 * covariance.getNorm(); CorrelatedRandomVectorGenerator generator = new CorrelatedRandomVectorGenerator(mean, covariance, small, rawGenerator); // Generate the Tuples TupleList tupleList = new TupleList(dimensions); for (int j = 0; j < tupleCount; j++) { double[] randomVector = generator.nextVector(); Tuple tuple = new Tuple(randomVector); tupleList.add(tuple); } return tupleList; }
From source file:inputHandling.DataGenAntiCorrCommons.java
@Override public TupleList genData(int dimensions, int tupleCount) { logger.info("Generating uniform anti-correlated Data with " + tupleCount + " Tuples in " + dimensions + " dimensions, Coeff.: -" + this.coeff); genMatrices(dimensions);//from w w w . j ava2 s . co m RealMatrix covariance = MatrixUtils.createRealMatrix(cov); RandomGenerator rg = new JDKRandomGenerator(Math.round(seed)); UniformRandomGenerator rawGenerator = new UniformRandomGenerator(rg); double small = 1.0e-12 * covariance.getNorm(); CorrelatedRandomVectorGenerator generator = new CorrelatedRandomVectorGenerator(mean, covariance, small, rawGenerator); TupleList tupleList = new TupleList(dimensions); // Invert the Values, to receive Anti-Correlation for (int j = 0; j < tupleCount; j++) { double[] randomVector = generator.nextVector(); for (int i = 0; i < dimensions; i++) { if (j % 2 == 0 && i % 2 == 0) randomVector[i] = 2 * mean[i] - randomVector[i]; else if (j % 2 != 0 && i % 2 != 0) randomVector[i] = 2 * mean[i] - randomVector[i]; else randomVector[i] = randomVector[i]; } Tuple tuple = new Tuple(randomVector); tupleList.add(tuple); } return tupleList; }
From source file:inputHandling.DataGenAntiCorrGaussCommons.java
@Override public TupleList genData(int dimensions, int tupleCount) { logger.info("Generating gaussian anti-correlated Data with " + tupleCount + " Tuples in " + dimensions + " dimensions, Coeff.: -" + this.coeff); genMatrices(dimensions);/*from w w w .ja v a 2 s. c om*/ RealMatrix covariance = MatrixUtils.createRealMatrix(cov); RandomGenerator rg = new JDKRandomGenerator(Math.round(seed)); GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg); double small = 1.0e-12 * covariance.getNorm(); CorrelatedRandomVectorGenerator generator = new CorrelatedRandomVectorGenerator(mean, covariance, small, rawGenerator); TupleList tupleList = new TupleList(dimensions); // Invert the Values, to receive Anti-Correlation for (int j = 0; j < tupleCount; j++) { double[] randomVector = generator.nextVector(); for (int i = 0; i < dimensions; i++) { if (j % 2 == 0 && i % 2 == 0) randomVector[i] = 2 * mean[i] - randomVector[i]; else if (j % 2 != 0 && i % 2 != 0) randomVector[i] = 2 * mean[i] - randomVector[i]; else randomVector[i] = randomVector[i]; } Tuple tuple = new Tuple(randomVector); tupleList.add(tuple); } return tupleList; }