List of usage examples for org.apache.commons.math3.optim MaxEval MaxEval
public MaxEval(int max)
From source file:edu.cmu.tetrad.search.Ling.java
private void optimizeNonGaussianity(final int rowIndex, final DoubleMatrix2D dataSetMatrix, final double[][] W, List<Mapping> allMappings) { final List<Mapping> mappings = mappingsForRow(rowIndex, allMappings); MultivariateFunction function = new MultivariateFunction() { public double value(double[] values) { for (int i = 0; i < values.length; i++) { Mapping mapping = mappings.get(i); W[mapping.getI()][mapping.getJ()] = values[i]; }/*from w w w . jav a2s . c om*/ double v = ngFullData(rowIndex, dataSetMatrix, W); if (Double.isNaN(v)) return 10000; return -(v); } }; { double[] values = new double[mappings.size()]; for (int k = 0; k < mappings.size(); k++) { Mapping mapping = mappings.get(k); values[k] = W[mapping.getI()][mapping.getJ()]; } MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7); PointValuePair pair = search.optimize(new InitialGuess(values), new ObjectiveFunction(function), GoalType.MINIMIZE, new MaxEval(100000)); values = pair.getPoint(); for (int k = 0; k < mappings.size(); k++) { Mapping mapping = mappings.get(k); W[mapping.getI()][mapping.getJ()] = values[k]; } } }
From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java
@Test public void stockingLordTest1() { System.out.println("StockingLordMethod Test 1: Actual Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;/*from w ww.j ava2 s . com*/ for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } UserSuppliedDistributionApproximation distX = new UserSuppliedDistributionApproximation(points, xDensity); UserSuppliedDistributionApproximation distY = new UserSuppliedDistributionApproximation(points, yDensity); StockingLordMethod sl = new StockingLordMethod(irmX, irmY, distX, distY, EquatingCriterionType.Q1Q2); sl.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); sl.setIntercept(slCoefficients[0]); sl.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4788, sl.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0911, sl.getScale(), 1e-4); System.out.println(); }
From source file:com.itemanalysis.psychometrics.irt.equating.HaebaraMethodTest.java
@Test public void haebaraTest3() { System.out.println("Haebara Test 3: Normal Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;//w w w . j av a 2s .c om for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } NormalDistributionApproximation normal = new NormalDistributionApproximation(0, 1, -4, 4, 10); HaebaraMethod hb = new HaebaraMethod(irmX, irmY, normal, normal, EquatingCriterionType.Q1Q2); hb.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); org.apache.commons.math3.optim.PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(hb), org.apache.commons.math3.optim.nonlinear.scalar.GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); hb.setIntercept(slCoefficients[0]); hb.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4658, hb.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0722, hb.getScale(), 1e-4); System.out.println(); }
From source file:gdsc.smlm.ij.plugins.EMGainAnalysis.java
/** * Fit the EM-gain distribution (Gaussian * Gamma) * //w w w .j a v a 2 s . c o m * @param h * The distribution */ private void fit(int[] h) { final int[] limits = limits(h); final double[] x = getX(limits); final double[] y = getY(h, limits); Plot2 plot = new Plot2(TITLE, "ADU", "Frequency"); double yMax = Maths.max(y); plot.setLimits(limits[0], limits[1], 0, yMax); plot.setColor(Color.black); plot.addPoints(x, y, Plot2.DOT); Utils.display(TITLE, plot); // Estimate remaining parameters. // Assuming a gamma_distribution(shape,scale) then mean = shape * scale // scale = gain // shape = Photons = mean / gain double mean = getMean(h) - bias; // Note: if the bias is too high then the mean will be negative. Just move the bias. while (mean < 0) { bias -= 1; mean += 1; } double photons = mean / gain; if (simulate) Utils.log("Simulated bias=%d, gain=%s, noise=%s, photons=%s", (int) _bias, Utils.rounded(_gain), Utils.rounded(_noise), Utils.rounded(_photons)); Utils.log("Estimate bias=%d, gain=%s, noise=%s, photons=%s", (int) bias, Utils.rounded(gain), Utils.rounded(noise), Utils.rounded(photons)); final int max = (int) x[x.length - 1]; double[] g = pdf(max, photons, gain, noise, (int) bias); plot.setColor(Color.blue); plot.addPoints(x, g, Plot2.LINE); Utils.display(TITLE, plot); // Perform a fit CustomPowellOptimizer o = new CustomPowellOptimizer(1e-6, 1e-16, 1e-6, 1e-16); double[] startPoint = new double[] { photons, gain, noise, bias }; final int maxEval = 3000; // Set bounds double[] lower = new double[] { 0, 0.5 * gain, 0, bias - noise }; double[] upper = new double[] { (limits[1] - limits[0]) / gain, 2 * gain, gain, bias + noise }; // Restart until converged. // TODO - Maybe fix this with a better optimiser. This needs to be tested on real data. PointValuePair solution = null; for (int iter = 0; iter < 3; iter++) { IJ.showStatus("Fitting histogram ... Iteration " + iter); try { // Basic Powell optimiser MultivariateFunction fun = getFunction(limits, y, max, maxEval); PointValuePair optimum = o.optimize(new MaxEval(maxEval), new ObjectiveFunction(fun), GoalType.MINIMIZE, new InitialGuess((solution == null) ? startPoint : solution.getPointRef())); if (solution == null || optimum.getValue() < solution.getValue()) { solution = optimum; } } catch (Exception e) { } try { // Bounded Powell optimiser MultivariateFunction fun = getFunction(limits, y, max, maxEval); MultivariateFunctionMappingAdapter adapter = new MultivariateFunctionMappingAdapter(fun, lower, upper); PointValuePair optimum = o.optimize(new MaxEval(maxEval), new ObjectiveFunction(adapter), GoalType.MINIMIZE, new InitialGuess(adapter .boundedToUnbounded((solution == null) ? startPoint : solution.getPointRef()))); double[] point = adapter.unboundedToBounded(optimum.getPointRef()); optimum = new PointValuePair(point, optimum.getValue()); if (solution == null || optimum.getValue() < solution.getValue()) { solution = optimum; } } catch (Exception e) { } } IJ.showStatus(""); IJ.showProgress(1); if (solution == null) { Utils.log("Failed to fit the distribution"); return; } double[] point = solution.getPointRef(); photons = point[0]; gain = point[1]; noise = point[2]; bias = (int) Math.round(point[3]); String label = String.format("Fitted bias=%d, gain=%s, noise=%s, photons=%s", (int) bias, Utils.rounded(gain), Utils.rounded(noise), Utils.rounded(photons)); Utils.log(label); if (simulate) { Utils.log("Relative Error bias=%s, gain=%s, noise=%s, photons=%s", Utils.rounded(relativeError(bias, _bias)), Utils.rounded(relativeError(gain, _gain)), Utils.rounded(relativeError(noise, _noise)), Utils.rounded(relativeError(photons, _photons))); } // Show the PoissonGammaGaussian approximation double[] f = null; if (showApproximation) { f = new double[x.length]; PoissonGammaGaussianFunction fun = new PoissonGammaGaussianFunction(1.0 / gain, noise); final double expected = photons * gain; for (int i = 0; i < f.length; i++) { f[i] = fun.likelihood(x[i] - bias, expected); //System.out.printf("x=%d, g=%f, f=%f, error=%f\n", (int) x[i], g[i], f[i], // gdsc.smlm.fitting.utils.DoubleEquality.relativeError(g[i], f[i])); } yMax = Maths.maxDefault(yMax, f); } // Replot g = pdf(max, photons, gain, noise, (int) bias); plot = new Plot2(TITLE, "ADU", "Frequency"); plot.setLimits(limits[0], limits[1], 0, yMax * 1.05); plot.setColor(Color.black); plot.addPoints(x, y, Plot2.DOT); plot.setColor(Color.red); plot.addPoints(x, g, Plot2.LINE); plot.addLabel(0, 0, label); if (showApproximation) { plot.setColor(Color.blue); plot.addPoints(x, f, Plot2.LINE); } Utils.display(TITLE, plot); }
From source file:edu.cmu.tetrad.sem.GeneralizedSemEstimator.java
private double[] optimize(MultivariateFunction function, double[] values, int optimizer) { PointValuePair pair = null;/*from ww w .ja v a 2s. co m*/ if (optimizer == 1) { // 0.01, 0.000001 //2.0D * FastMath.ulp(1.0D), 1e-8 MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7); pair = search.optimize(new InitialGuess(values), new ObjectiveFunction(function), GoalType.MINIMIZE, new MaxEval(100000)); } else if (optimizer == 2) { MultivariateOptimizer search = new SimplexOptimizer(1e-7, 1e-7); pair = search.optimize(new InitialGuess(values), new ObjectiveFunction(function), GoalType.MINIMIZE, new MaxEval(100000), new NelderMeadSimplex(values.length)); } else if (optimizer == 3) { int dim = values.length; int additionalInterpolationPoints = 0; final int numIterpolationPoints = 2 * dim + 1 + additionalInterpolationPoints; BOBYQAOptimizer search = new BOBYQAOptimizer(numIterpolationPoints); pair = search.optimize(new MaxEval(100000), new ObjectiveFunction(function), GoalType.MINIMIZE, new InitialGuess(values), SimpleBounds.unbounded(dim)); } else if (optimizer == 4) { MultivariateOptimizer search = new CMAESOptimizer(3000000, .05, false, 0, 0, new MersenneTwister(), false, new SimplePointChecker<PointValuePair>(0.5, 0.5)); pair = search.optimize(new MaxEval(30000), new ObjectiveFunction(function), GoalType.MINIMIZE, new InitialGuess(values), new CMAESOptimizer.Sigma(new double[values.length]), new CMAESOptimizer.PopulationSize(1000)); } else if (optimizer == 5) { // 0.01, 0.000001 //2.0D * FastMath.ulp(1.0D), 1e-8 MultivariateOptimizer search = new PowellOptimizer(.05, .05); pair = search.optimize(new InitialGuess(values), new ObjectiveFunction(function), GoalType.MINIMIZE, new MaxEval(100000)); } else if (optimizer == 6) { MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7); pair = search.optimize(new InitialGuess(values), new ObjectiveFunction(function), GoalType.MAXIMIZE, new MaxEval(10000)); } return pair.getPoint(); }
From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java
@Test public void stockingLordTest2() { System.out.println("StockingLordMethod Test 2: Uniform Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;/* w w w. ja v a2 s.c o m*/ for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } UniformDistributionApproximation uniform = new UniformDistributionApproximation(-4, 4, 10); StockingLordMethod sl = new StockingLordMethod(irmX, irmY, uniform, uniform, EquatingCriterionType.Q1Q2); sl.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); sl.setIntercept(slCoefficients[0]); sl.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4532, sl.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0962, sl.getScale(), 1e-4); System.out.println(); }
From source file:gdsc.smlm.fitting.BinomialFitter.java
/** * Fit the binomial distribution (n,p) to the cumulative histogram. Performs fitting assuming a fixed n value and * attempts to optimise p./*from w ww.j av a2 s . c o m*/ * * @param histogram * The input histogram * @param mean * The histogram mean (used to estimate p). Calculated if NaN. * @param n * The n to evaluate * @param zeroTruncated * True if the model should ignore n=0 (zero-truncated binomial) * @return The best fit (n, p) * @throws IllegalArgumentException * If any of the input data values are negative * @throws IllegalArgumentException * If any fitting a zero truncated binomial and there are no values above zero */ public PointValuePair fitBinomial(double[] histogram, double mean, int n, boolean zeroTruncated) { if (Double.isNaN(mean)) mean = getMean(histogram); if (zeroTruncated && histogram[0] > 0) { log("Fitting zero-truncated histogram but there are zero values - Renormalising to ignore zero"); double cumul = 0; for (int i = 1; i < histogram.length; i++) cumul += histogram[i]; if (cumul == 0) throw new IllegalArgumentException( "Fitting zero-truncated histogram but there are no non-zero values"); histogram[0] = 0; for (int i = 1; i < histogram.length; i++) histogram[i] /= cumul; } int nFittedPoints = Math.min(histogram.length, n + 1) - ((zeroTruncated) ? 1 : 0); if (nFittedPoints < 1) { log("No points to fit (%d): Histogram.length = %d, n = %d, zero-truncated = %b", nFittedPoints, histogram.length, n, zeroTruncated); return null; } // The model is only fitting the probability p // For a binomial n*p = mean => p = mean/n double[] initialSolution = new double[] { FastMath.min(mean / n, 1) }; // Create the function BinomialModelFunction function = new BinomialModelFunction(histogram, n, zeroTruncated); double[] lB = new double[1]; double[] uB = new double[] { 1 }; SimpleBounds bounds = new SimpleBounds(lB, uB); // Fit // CMAESOptimizer or BOBYQAOptimizer support bounds // CMAESOptimiser based on Matlab code: // https://www.lri.fr/~hansen/cmaes.m // Take the defaults from the Matlab documentation int maxIterations = 2000; double stopFitness = 0; //Double.NEGATIVE_INFINITY; boolean isActiveCMA = true; int diagonalOnly = 0; int checkFeasableCount = 1; RandomGenerator random = new Well19937c(); boolean generateStatistics = false; ConvergenceChecker<PointValuePair> checker = new SimpleValueChecker(1e-6, 1e-10); // The sigma determines the search range for the variables. It should be 1/3 of the initial search region. OptimizationData sigma = new CMAESOptimizer.Sigma(new double[] { (uB[0] - lB[0]) / 3 }); OptimizationData popSize = new CMAESOptimizer.PopulationSize((int) (4 + Math.floor(3 * Math.log(2)))); try { PointValuePair solution = null; boolean noRefit = maximumLikelihood; if (n == 1 && zeroTruncated) { // No need to fit solution = new PointValuePair(new double[] { 1 }, 0); noRefit = true; } else { GoalType goalType = (maximumLikelihood) ? GoalType.MAXIMIZE : GoalType.MINIMIZE; // Iteratively fit CMAESOptimizer opt = new CMAESOptimizer(maxIterations, stopFitness, isActiveCMA, diagonalOnly, checkFeasableCount, random, generateStatistics, checker); for (int iteration = 0; iteration <= fitRestarts; iteration++) { try { // Start from the initial solution PointValuePair result = opt.optimize(new InitialGuess(initialSolution), new ObjectiveFunction(function), goalType, bounds, sigma, popSize, new MaxIter(maxIterations), new MaxEval(maxIterations * 2)); //System.out.printf("CMAES Iter %d initial = %g (%d)\n", iteration, result.getValue(), // opt.getEvaluations()); if (solution == null || result.getValue() < solution.getValue()) { solution = result; } } catch (TooManyEvaluationsException e) { } catch (TooManyIterationsException e) { } if (solution == null) continue; try { // Also restart from the current optimum PointValuePair result = opt.optimize(new InitialGuess(solution.getPointRef()), new ObjectiveFunction(function), goalType, bounds, sigma, popSize, new MaxIter(maxIterations), new MaxEval(maxIterations * 2)); //System.out.printf("CMAES Iter %d restart = %g (%d)\n", iteration, result.getValue(), // opt.getEvaluations()); if (result.getValue() < solution.getValue()) { solution = result; } } catch (TooManyEvaluationsException e) { } catch (TooManyIterationsException e) { } } if (solution == null) return null; } if (noRefit) { // Although we fit the log-likelihood, return the sum-of-squares to allow // comparison across different n double p = solution.getPointRef()[0]; double ss = 0; double[] obs = function.p; double[] exp = function.getP(p); for (int i = 0; i < obs.length; i++) ss += (obs[i] - exp[i]) * (obs[i] - exp[i]); return new PointValuePair(solution.getPointRef(), ss); } // We can do a LVM refit if the number of fitted points is more than 1 else if (nFittedPoints > 1) { // Improve SS fit with a gradient based LVM optimizer LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(); try { final BinomialModelFunctionGradient gradientFunction = new BinomialModelFunctionGradient( histogram, n, zeroTruncated); PointVectorValuePair lvmSolution = optimizer.optimize(new MaxIter(3000), new MaxEval(Integer.MAX_VALUE), new ModelFunctionJacobian(new MultivariateMatrixFunction() { public double[][] value(double[] point) throws IllegalArgumentException { return gradientFunction.jacobian(point); } }), new ModelFunction(gradientFunction), new Target(gradientFunction.p), new Weight(gradientFunction.getWeights()), new InitialGuess(solution.getPointRef())); double ss = 0; double[] obs = gradientFunction.p; double[] exp = lvmSolution.getValue(); for (int i = 0; i < obs.length; i++) ss += (obs[i] - exp[i]) * (obs[i] - exp[i]); // Check the pValue is valid since the LVM is not bounded. double p = lvmSolution.getPointRef()[0]; if (ss < solution.getValue() && p <= 1 && p >= 0) { //log("Re-fitting improved the SS from %s to %s (-%s%%)", // Utils.rounded(solution.getValue(), 4), Utils.rounded(ss, 4), // Utils.rounded(100 * (solution.getValue() - ss) / solution.getValue(), 4)); return new PointValuePair(lvmSolution.getPoint(), ss); } } catch (TooManyIterationsException e) { log("Failed to re-fit: Too many iterations (%d)", optimizer.getIterations()); } catch (ConvergenceException e) { log("Failed to re-fit: %s", e.getMessage()); } catch (Exception e) { // Ignore this ... } } return solution; } catch (Exception e) { log("Failed to fit Binomial distribution with N=%d : %s", n, e.getMessage()); } return null; }
From source file:com.itemanalysis.psychometrics.irt.equating.HaebaraMethodTest.java
@Test public void haebaraTest4() { System.out.println("Haebara Test 4: Actual Distribution -backwards"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;// w ww. j a va 2 s . c om for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } UserSuppliedDistributionApproximation distX = new UserSuppliedDistributionApproximation(points, xDensity); UserSuppliedDistributionApproximation distY = new UserSuppliedDistributionApproximation(points, yDensity); HaebaraMethod hb = new HaebaraMethod(irmX, irmY, distX, distY, EquatingCriterionType.Q1); hb.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); org.apache.commons.math3.optim.PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(hb), org.apache.commons.math3.optim.nonlinear.scalar.GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] hbCoefficients = optimum.getPoint(); hb.setIntercept(hbCoefficients[0]); hb.setScale(hbCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + hbCoefficients[0] + " A = " + hbCoefficients[1]); assertEquals(" Intercept test", -0.4710, hb.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0798, hb.getScale(), 1e-4); System.out.println(); }
From source file:edu.cmu.tetrad.search.Mimbuild2.java
private void optimizeNonMeasureVariancesQuick(Node[][] indicators, TetradMatrix measurescov, TetradMatrix latentscov, double[][] loadings, int[][] indicatorIndices) { int count = 0; for (int i = 0; i < indicators.length; i++) { for (int j = i; j < indicators.length; j++) { count++;/*from ww w . j a v a 2s. co m*/ } } for (int i = 0; i < indicators.length; i++) { for (int j = 0; j < indicators[i].length; j++) { count++; } } double[] values = new double[count]; count = 0; for (int i = 0; i < indicators.length; i++) { for (int j = i; j < indicators.length; j++) { values[count++] = latentscov.get(i, j); } } for (int i = 0; i < indicators.length; i++) { for (int j = 0; j < indicators[i].length; j++) { values[count++] = loadings[i][j]; } } Function1 function1 = new Function1(indicatorIndices, measurescov, loadings, latentscov, count); MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7); PointValuePair pair = search.optimize(new InitialGuess(values), new ObjectiveFunction(function1), GoalType.MINIMIZE, new MaxEval(100000)); minimum = pair.getValue(); }
From source file:com.itemanalysis.psychometrics.irt.equating.StockingLordMethodTest.java
@Test public void stockingLordTest3() { System.out.println("StockingLordMethod Test 3: Normal Distribution"); int n = aX.length; LinkedHashMap<String, ItemResponseModel> irmX = new LinkedHashMap<String, ItemResponseModel>(); LinkedHashMap<String, ItemResponseModel> irmY = new LinkedHashMap<String, ItemResponseModel>(); ItemResponseModel irm;//from w w w.jav a2s . com for (int i = 0; i < n; i++) { String name = "V" + i; irm = new Irm3PL(aX[i], bX[i], cX[i], 1.0); irmX.put(name, irm); irm = new Irm3PL(aY[i], bY[i], cY[i], 1.0); irmY.put(name, irm); } NormalDistributionApproximation normal = new NormalDistributionApproximation(0, 1, -4, 4, 10); StockingLordMethod sl = new StockingLordMethod(irmX, irmY, normal, normal, EquatingCriterionType.Q1Q2); sl.setPrecision(4); double[] startValues = { 0, 1 }; int numIterpolationPoints = 2 * 2;//two dimensions A and B BOBYQAOptimizer underlying = new BOBYQAOptimizer(numIterpolationPoints); RandomGenerator g = new JDKRandomGenerator(); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g)); MultiStartMultivariateOptimizer optimizer = new MultiStartMultivariateOptimizer(underlying, 10, generator); PointValuePair optimum = optimizer.optimize(new MaxEval(1000), new ObjectiveFunction(sl), GoalType.MINIMIZE, SimpleBounds.unbounded(2), new InitialGuess(startValues)); double[] slCoefficients = optimum.getPoint(); sl.setIntercept(slCoefficients[0]); sl.setScale(slCoefficients[1]); System.out.println(" Iterations: " + optimizer.getEvaluations()); System.out.println(" fmin: " + optimum.getValue()); System.out.println(" B = " + slCoefficients[0] + " A = " + slCoefficients[1]); assertEquals(" Intercept test", -0.4788, sl.getIntercept(), 1e-4); assertEquals(" Scale test", 1.0901, sl.getScale(), 1e-4); System.out.println(); }