List of usage examples for org.apache.commons.math3.linear ArrayRealVector ArrayRealVector
public ArrayRealVector(double[] v1, double[] v2)
From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurement.java
public MultivariateGaussianCriterionMeasurement(List<Alternative> alternatives) { this.alternatives = new ArrayList<Alternative>(alternatives); this.meanVector = new ArrayRealVector(alternatives.size(), 0.0); this.covarianceMatrix = MatrixUtils.createRealIdentityMatrix(alternatives.size()); }
From source file:edu.oregonstate.eecs.mcplan.ml.KMeans.java
private RealVector centerOfMass(final int c) { final RealVector com = new ArrayRealVector(m_, 0); int nelements = 0; for (int i = 0; i < n_; ++i) { if (c_[i] == c) { nelements += 1;//w ww.j a v a 2 s .co m com.combineToSelf(1.0, 1.0, data_[i]); // Add in-place } } assert (nelements > 0); com.mapDivideToSelf(nelements); return com; }
From source file:iocomms.subpos.NonLinearLeastSquaresSolver.java
public Optimum solve(double[] target, double[] weights, double[] initialPoint, boolean debugInfo) { if (debugInfo) { System.out.println("Max Number of Iterations : " + MAXNUMBEROFITERATIONS); }/* ww w .j a v a 2 s.co m*/ LeastSquaresProblem leastSquaresProblem = LeastSquaresFactory.create( // function to be optimized function, // target values at optimal point in least square equation // (x0+xi)^2 + (y0+yi)^2 + ri^2 = target[i] new ArrayRealVector(target, false), new ArrayRealVector(initialPoint, false), new DiagonalMatrix(weights), null, MAXNUMBEROFITERATIONS, MAXNUMBEROFITERATIONS); return leastSquaresOptimizer.optimize(leastSquaresProblem); }
From source file:edu.stanford.cfuller.imageanalysistools.fitting.BisquareLinearFit.java
/** * Performs a robust least squares fit with bisquare weights to the supplied data. * * @param indVarValues A RealVector containing the values of the independent variable. * @param depVarValues A RealVector containing the values of the dependent variable. * @return a RealVector containing two elements: the slope of the fit and the y-intercept of the fit. *//*from w ww. jav a 2s .c o m*/ public RealVector fit(RealVector indVarValues, RealVector depVarValues) { RealVector uniformWeights = new ArrayRealVector(indVarValues.getDimension(), 1.0); RealVector lastParams = new ArrayRealVector(2, Double.MAX_VALUE); RealVector currParams = wlsFit(indVarValues, depVarValues, uniformWeights); RealVector weights = uniformWeights; RealVector leverages = this.calculateLeverages(indVarValues); int c = 0; double norm_mult = 1.0; if (!this.noIntercept) { norm_mult = 2.0; } int maxiter = 10000; while (lastParams.subtract(currParams).getNorm() > CONV_NORM * norm_mult && c++ < maxiter) { lastParams = currParams; RealVector stdAdjR = this.calculateStandardizedAdjustedResiduals(indVarValues, depVarValues, leverages, currParams); weights = calculateBisquareWeights(stdAdjR); currParams = wlsFit(indVarValues, depVarValues, weights); } return currParams; }
From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurementTest.java
@Test public void testSetMeanVector() { RealVector mu1 = new ArrayRealVector(3, 0.0); RealVector mu2 = new ArrayRealVector(new double[] { 25.3, 2.1, -3 }); JUnitUtil.testSetter(m, MultivariateGaussianCriterionMeasurement.PROPERTY_MEAN_VECTOR, mu1, mu2); }
From source file:edu.oregonstate.eecs.mcplan.ml.SequentialProjectionHashLearner.java
/** * @param X Each column is an unlabeled data point. Will be modified to have 0 mean. * @param XL Each column is a labeled data point. Will be modified to have 0 mean. * @param S S_ij = 1 if XL.get(i) similar to XL.get(j), -1 if not similar, 0 if unknown * @param K Number of hash bits/*from ww w. j av a 2 s . c om*/ * @param eta Regularization parameter * @param alpha Learning rate (it's more like a boosting weight) */ public SequentialProjectionHashLearner(final RealMatrix X, final RealMatrix XL, final RealMatrix S, final int K, final double eta, final double alpha) { assert (K <= 64); // Because we're going to hash into a long this.X = X; this.XL = XL; this.S = S; this.K = K; this.eta = eta; this.alpha = alpha; Xi_ = X; Si_ = S; final VectorMeanVarianceAccumulator mu = new VectorMeanVarianceAccumulator(X.getRowDimension()); for (int j = 0; j < X.getColumnDimension(); ++j) { mu.add(X.getColumn(j)); } b = new ArrayRealVector(mu.mean(), false); for (int i = 0; i < X.getRowDimension(); ++i) { for (int j = 0; j < X.getColumnDimension(); ++j) { final double x = X.getEntry(i, j); X.setEntry(i, j, x / b.getEntry(i)); } } for (int i = 0; i < XL.getRowDimension(); ++i) { for (int j = 0; j < XL.getColumnDimension(); ++j) { final double x = XL.getEntry(i, j); XL.setEntry(i, j, x / b.getEntry(i)); } } XLt = XL.transpose(); }
From source file:edu.stanford.cfuller.colocalization3d.fitting.P3DFitter.java
/** * Fits the distances between the two channels of a set of objects to a p3d distribution. * /*from w ww . j a va 2s .co m*/ * @param objects the ImageObjects whose distances will be fit * @param diffs a RealVector containing the scalar distances between the channels of the ImageObjects, in the same order. * * @return a RealVector containing the parameters for the distribution fit: first the mean parameter, second the standard deviation parameter */ public RealVector fit(List<ImageObject> objects, RealVector diffs) { P3dObjectiveFunction of = new P3dObjectiveFunction(); of.setR(diffs); final double tol = 1e-12; NelderMeadMinimizer nmm = new NelderMeadMinimizer(tol); double initialMean = diffs.getL1Norm() / diffs.getDimension(); double initialWidth = diffs.mapSubtract(initialMean) .map(new org.apache.commons.math3.analysis.function.Power(2)).getL1Norm() / diffs.getDimension(); initialWidth = Math.sqrt(initialWidth); RealVector startingPoint = new ArrayRealVector(2, 0.0); startingPoint.setEntry(0, initialMean); startingPoint.setEntry(1, initialWidth); RealVector parametersMin = null; if (this.parameters.hasKey(ROBUST_P3D_FIT_PARAM)) { double cutoff = this.parameters.getDoubleValueForKey(ROBUST_P3D_FIT_PARAM); of.setMinProb(cutoff); } return nmm.optimize(of, startingPoint); }
From source file:edu.utexas.cs.tactex.ChargeEstimatorTest.java
@Test public void testEstimateCharge() { // test estimating one charge: estimateCharge() ArrayRealVector customerEnergy = new ArrayRealVector(7 * 24, 1.6); TariffSpecification spec1 = new TariffSpecification(brokerContext.getBroker(), PowerType.CONSUMPTION); spec1.addRate(new Rate().withValue(-1.0)); TestHelperUtils.addToRepo(spec1, tariffRepo, timeService); double expected = 7 * 24 * 1.6 * -1.0; double actual = chargeEstimatorDefault.estimateCharge(customerEnergy, spec1); assertEquals("fixed rate", expected, actual, 1e-6); // test estimating several charges: estimateTariffCharges() TariffSpecification spec2 = new TariffSpecification(brokerContext.getBroker(), PowerType.CONSUMPTION); spec2.addRate(new Rate().withValue(-2.0)); TestHelperUtils.addToRepo(spec2, tariffRepo, timeService); List<TariffSpecification> tariffSpecs = new ArrayList<TariffSpecification>(); tariffSpecs.add(spec1);/*from w w w .j av a 2s . co m*/ tariffSpecs.add(spec2); CustomerInfo customer1 = new CustomerInfo("Austin", 1); CustomerInfo customer2 = new CustomerInfo("Dallas", 1); CustomerInfo customer3 = new CustomerInfo("Houston", 1); CustomerInfo customer4 = new CustomerInfo("NYC", 2); List<CustomerInfo> customers = new ArrayList<CustomerInfo>(); customers.add(customer1); customers.add(customer2); customers.add(customer3); customers.add(customer4); ArrayRealVector energy1 = new ArrayRealVector(7 * 24, 1.0); ArrayRealVector energy2 = new ArrayRealVector(7 * 24, 2.0); ArrayRealVector energy3 = new ArrayRealVector(7 * 24, 3.0); ArrayRealVector energy4 = new ArrayRealVector(7 * 24, 4.0); HashMap<CustomerInfo, HashMap<TariffSpecification, ShiftedEnergyData>> customer2energy = new HashMap<CustomerInfo, HashMap<TariffSpecification, ShiftedEnergyData>>(); customer2energy.put(customer1, new HashMap<TariffSpecification, ShiftedEnergyData>()); customer2energy.put(customer2, new HashMap<TariffSpecification, ShiftedEnergyData>()); customer2energy.put(customer3, new HashMap<TariffSpecification, ShiftedEnergyData>()); customer2energy.put(customer4, new HashMap<TariffSpecification, ShiftedEnergyData>()); customer2energy.get(customer1).put(spec1, new ShiftedEnergyData(energy1, 0.0)); customer2energy.get(customer2).put(spec1, new ShiftedEnergyData(energy2, 0.0)); customer2energy.get(customer3).put(spec1, new ShiftedEnergyData(energy3, 0.0)); customer2energy.get(customer4).put(spec1, new ShiftedEnergyData(energy4, 0.0)); customer2energy.get(customer1).put(spec2, new ShiftedEnergyData(energy1, 0.0)); customer2energy.get(customer2).put(spec2, new ShiftedEnergyData(energy2, 0.0)); customer2energy.get(customer3).put(spec2, new ShiftedEnergyData(energy3, 0.0)); customer2energy.get(customer4).put(spec2, new ShiftedEnergyData(energy4, 0.0)); HashMap<CustomerInfo, HashMap<TariffSpecification, Double>> estimatedCharges = chargeEstimatorDefault .estimateRelevantTariffCharges(tariffSpecs, customer2energy); expected = 7 * 24 * 1.0 * -1.0; actual = estimatedCharges.get(customer1).get(spec1); assertEquals("fixed rate, customer1, spec1", expected, actual, 1e-6); expected = 7 * 24 * 2.0 * -1.0; actual = estimatedCharges.get(customer2).get(spec1); assertEquals("fixed rate, customer2, spec1", expected, actual, 1e-6); expected = 7 * 24 * 3.0 * -1.0; actual = estimatedCharges.get(customer3).get(spec1); assertEquals("fixed rate, customer3, spec1", expected, actual, 1e-6); // charge computation is for 1 customer - therefore should ignore population number (here 2) expected = 7 * 24 * 4.0 * -1.0; actual = estimatedCharges.get(customer4).get(spec1); assertEquals("fixed rate, customer4, spec1", expected, actual, 1e-6); expected = 7 * 24 * 1.0 * -2.0; actual = estimatedCharges.get(customer1).get(spec2); assertEquals("fixed rate, customer1, spec2", expected, actual, 1e-6); expected = 7 * 24 * 2.0 * -2.0; actual = estimatedCharges.get(customer2).get(spec2); assertEquals("fixed rate, customer2, spec2", expected, actual, 1e-6); expected = 7 * 24 * 3.0 * -2.0; actual = estimatedCharges.get(customer3).get(spec2); assertEquals("fixed rate, customer3, spec2", expected, actual, 1e-6); // charge computation is for 1 customer - therefore should ignore population number (here 2) expected = 7 * 24 * 4.0 * -2.0; actual = estimatedCharges.get(customer4).get(spec2); assertEquals("fixed rate, customer4, spec2", expected, actual, 1e-6); }
From source file:edu.duke.cs.osprey.tupexp.IterativeCGTupleFitter.java
RealVector applyAtA(RealVector rv) {
//first apply A
double Arv[] = calcFitVals(rv);
//then apply A^T to Arv
double ans[] = new double[numTup];
for (int s = 0; s < numSamp; s++) {
if (isSampleRestrained(s)) {
ArrayList<Integer> sampTup = tupIndMat.calcSampleTuples(samples.get(s));
for (int t : sampTup)
ans[t] += Arv[s];//from w ww . ja va 2 s .c o m
}
}
//damping
if (curCoeffs != null) {//not first iteration
for (int t = 0; t < numTup; t++)
ans[t] += damperLambda * rv.getEntry(t);
}
return new ArrayRealVector(ans, false);//make RealVector without copying ans
}
From source file:fi.smaa.jsmaa.model.MultivariateGaussianCriterionMeasurementTest.java
@Test(expected = IllegalArgumentException.class) public void testSetWrongSizeMeanVector() { m.setMeanVector(new ArrayRealVector(4, 0.0)); }