List of usage examples for org.apache.commons.math3.optim.nonlinear.scalar.noderiv NelderMeadSimplex NelderMeadSimplex
public NelderMeadSimplex(final double[][] referenceSimplex)
From source file:eu.crisis_economics.abm.markets.clearing.heterogeneous.NelderMeadClearingAlgorithm.java
@Override public double applyToNetwork(final MixedClearingNetwork network) { Preconditions.checkNotNull(network); final SimplexOptimizer optimizer = new SimplexOptimizer(relErrorTarget, absErrorTarget); final ResidualCostFunction aggregateCostFunction = super.getResidualScalarCostFunction(network); final RealVector start = new ArrayRealVector(network.getNumberOfEdges()); for (int i = 0; i < network.getNumberOfEdges(); ++i) start.setEntry(i, network.getEdges().get(i).getMaximumRateAdmissibleByBothParties()); start.set(1.);/* w ww . j ava2 s. com*/ final PointValuePair result = optimizer.optimize(new MaxEval(maximumEvaluations), new ObjectiveFunction(aggregateCostFunction), GoalType.MINIMIZE, new InitialGuess(start.toArray()), new NelderMeadSimplex(network.getNumberOfEdges())); final double residualCost = result.getValue(); System.out.println("Network cleared: residual cost: " + residualCost + "."); return residualCost; }
From source file:de.bund.bfr.math.MultivariateOptimization.java
@Override public Result optimize(int nParameterSpace, int nOptimizations, boolean stopWhenSuccessful, Map<String, Double> minStartValues, Map<String, Double> maxStartValues, int maxIterations, DoubleConsumer progessListener, ExecutionContext exec) throws CanceledExecutionException { if (exec != null) { exec.checkCanceled();/*from w w w . ja v a 2s . c o m*/ } progessListener.accept(0.0); List<ParamRange> ranges = MathUtils.getParamRanges(parameters, minStartValues, maxStartValues, nParameterSpace); ranges.set(parameters.indexOf(sdParam), new ParamRange(1.0, 1, 1.0)); List<StartValues> startValuesList = MathUtils.createStartValuesList(ranges, nOptimizations, values -> optimizerFunction.value(Doubles.toArray(values)), progress -> progessListener.accept(0.5 * progress), exec); Result result = new Result(); AtomicInteger currentIteration = new AtomicInteger(); SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-10, 1e-10) { @Override public boolean converged(int iteration, PointValuePair previous, PointValuePair current) { if (super.converged(iteration, previous, current)) { return true; } return currentIteration.incrementAndGet() >= maxIterations; } }); int count = 0; for (StartValues startValues : startValuesList) { if (exec != null) { exec.checkCanceled(); } progessListener.accept(0.5 * count++ / startValuesList.size() + 0.5); try { PointValuePair optimizerResults = optimizer.optimize(new MaxEval(Integer.MAX_VALUE), new MaxIter(maxIterations), new InitialGuess(Doubles.toArray(startValues.getValues())), new ObjectiveFunction(optimizerFunction), GoalType.MAXIMIZE, new NelderMeadSimplex(parameters.size())); double logLikelihood = optimizerResults.getValue() != null ? optimizerResults.getValue() : Double.NaN; if (result.logLikelihood == null || logLikelihood > result.logLikelihood) { result = getResults(optimizerResults); if (result.logLikelihood == 0.0 || stopWhenSuccessful) { break; } } } catch (TooManyEvaluationsException | TooManyIterationsException | ConvergenceException e) { } } return result; }
From source file:hulo.localization.models.obs.GaussianProcessLDPLMean.java
static PointValuePair minimize(MultivariateFunction func, double[] pointInit, double[] diffPointInit) { SimplexOptimizer optimizer = new SimplexOptimizer(1e-5, 1e-10); NelderMeadSimplex simplex = new NelderMeadSimplex(diffPointInit); ObjectiveFunction objFunc = new ObjectiveFunction(func); InitialGuess initGuess = new InitialGuess(pointInit); PointValuePair pair = optimizer.optimize(maxEval, objFunc, GoalType.MINIMIZE, initGuess, simplex); return pair;//from w ww. j a va2 s . c o m }
From source file:edu.cmu.tetrad.sem.GeneralizedSemEstimator.java
private double[] optimize(MultivariateFunction function, double[] values, int optimizer) { PointValuePair pair = null;/*from w w w.ja va2s. 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:gdsc.smlm.ij.plugins.pcpalm.PCPALMMolecules.java
private double[] optimiseSimplex(float[] x, float[] y, double[] initialSolution) { // Simplex optimisation SkewNormalMultivariateFunction sn2 = new SkewNormalMultivariateFunction(initialSolution); sn2.addData(x, y);// w w w .j a va2 s . c o m NelderMeadSimplex simplex = new NelderMeadSimplex(4); SimplexOptimizer opt = new SimplexOptimizer(1e-6, 1e-10); PointValuePair solution = opt.optimize(new MaxEval(1000), new InitialGuess(initialSolution), simplex, new ObjectiveFunction(sn2), GoalType.MINIMIZE); double[] skewParameters2 = solution.getPointRef(); return skewParameters2; }
From source file:nl.systemsgenetics.cellTypeSpecificAlleleSpecificExpression.BetaBinomialTest.java
public BetaBinomialTest(ArrayList<IndividualSnpData> all_individuals) throws Exception { boolean debug = true; //basic information, get the zero instance, was checked as the same in ReadAsLinesIntoIndividualSNPdata snpName = all_individuals.get(0).getSnpName(); chromosome = all_individuals.get(0).getChromosome(); position = all_individuals.get(0).getPosition(); genotype = all_individuals.get(0).genotype; ArrayList<IndividualSnpData> het_individuals; het_individuals = UtilityMethods.isolateHeterozygotesFromIndividualSnpData(all_individuals); numberOfHets = het_individuals.size(); hetSampleNames = new ArrayList<String>(); asRef = new ArrayList<Integer>(); asAlt = new ArrayList<Integer>(); asNo = new ArrayList<Integer>(); dispersion = new ArrayList<Double>(); int total_overlap = 0; for (IndividualSnpData temp_het : het_individuals) { //Do nothing if there is no data in het_individuals hetSampleNames.add(temp_het.getSampleName()); asRef.add(temp_het.getRefNum()); asAlt.add(temp_het.getAltNum()); asNo.add(temp_het.getNoNum());//from w w w . ja va 2 s . c om dispersion.add(temp_het.getDispersion()); //this is used to check if we will continue with calculations. total_overlap += temp_het.getRefNum() + temp_het.getAltNum(); } if ((total_overlap >= GlobalVariables.minReads) && (numberOfHets >= GlobalVariables.minHets)) { // There is data to perform the binomial test, perform it. if (GlobalVariables.verbosity >= 100) { System.out.println(); System.out.println("---- Starting beta binomial LRT test estimate ----"); System.out.println("SNP name: " + snpName); System.out.println("at: chr" + chromosome + ":" + position); System.out.println("--------------------------------------------------"); System.out.println("debug:"); System.out.println("Num of hets: " + Integer.toString(numberOfHets)); System.out.println(het_individuals.get(0).getSnpName()); System.out.println(total_overlap); System.out.println("asRef: " + asRef.toString()); System.out.println("asAlt: " + asAlt.toString()); System.out.println("dispersion: " + dispersion.toString()); System.out.println("Starting Null estimation"); } Integer[] asRefArray = asRef.toArray(new Integer[asRef.size()]); Integer[] asAltArray = asAlt.toArray(new Integer[asAlt.size()]); Double[] dispArray = dispersion.toArray(new Double[dispersion.size()]); BetaBinomNullLikelihood betaBinomNull; betaBinomNull = new BetaBinomNullLikelihood(asRefArray, asAltArray, dispArray); nullLogLik = betaBinomNull.value(new double[] { 0.5 }); if (GlobalVariables.verbosity >= 100) { System.out.println("Starting Alt estimation"); } BetaBinomAltLikelihood betaBinomAlt; betaBinomAlt = new BetaBinomAltLikelihood(asRefArray, asAltArray, dispArray); NelderMeadSimplex simplex; simplex = new NelderMeadSimplex(2); SimplexOptimizer optimizer = new SimplexOptimizer(GlobalVariables.simplexThreshold, GlobalVariables.simplexThreshold); //numbers are to which precision you want it to be done. PointValuePair solutionAlt = optimizer.optimize(new ObjectiveFunction(betaBinomAlt), new MaxEval(GlobalVariables.maximumIterations), simplex, GoalType.MINIMIZE, new InitialGuess(new double[] { 0.5, 0.5 }), new SearchInterval(-1000.0, 1000.0)); double[] valueAlt = solutionAlt.getPoint(); altLogLik = betaBinomAlt.value(valueAlt); iterations = optimizer.getIterations(); alphaParam = valueAlt[0]; betaParam = valueAlt[1]; binomRatio = valueAlt[0] / (valueAlt[0] + valueAlt[1]); //chi squared statistic is determined based on both null and alt loglikelihoods. chiSq = 2.0 * (nullLogLik - altLogLik); //determine P value based on distribution ChiSquaredDistribution distribution = new ChiSquaredDistribution(1); pVal = 1 - distribution.cumulativeProbability(chiSq); if (GlobalVariables.verbosity >= 10) { System.out.println("\n--- Beta Binomial Test Statistics: ---"); System.out.println("LogLik of Alt converged to a threshold of " + Double.toString(GlobalVariables.simplexThreshold)); System.out.println("\tAlpha parameter: " + Double.toString(valueAlt[0])); System.out.println("\tBeta parameter: " + Double.toString(valueAlt[1])); System.out.println("\tIterations to converge: " + Integer.toString(iterations) + "\n"); System.out.println("\tNull log likelihood: " + Double.toString(nullLogLik)); System.out.println("\tAlt log likelihood: " + Double.toString(altLogLik) + "\n"); System.out.println("\tChisq statistic: " + Double.toString(chiSq)); System.out.println("\tP value: " + Double.toString(pVal)); System.out.println("------------------------------------------"); //TODO, I want to format this properly, but not necessary } testPerformed = true; //This below is to compare everything to python created by WASP //Will put the backup back into python, just to make sure. if (GlobalVariables.verbosity >= 100) { System.out.println("loglikRef = " + Double.toString(altLogLik)); System.out.println("loglik = 0"); for (int i = 0; i < asRef.size(); i++) { System.out.println("loglik += AS_betabinom_loglike(" + "[math.log(" + Double.toString(alphaParam) + ") - math.log(" + Double.toString(alphaParam) + " + " + Double.toString(betaParam) + "), " + "math.log(" + Double.toString(betaParam) + ") - math.log(" + Double.toString(alphaParam) + " + " + Double.toString(betaParam) + ")]," + Double.toString(dispersion.get(i)) + "," + Double.toString(asRef.get(i)) + "," + Double.toString(asAlt.get(i)) + "," + "0.980198, 0.005)"); } System.out.println("if allclose(-1.0 * loglik, loglikRef): correctTest += 1"); //I find exactly the correct values after running it through WASP implementation in python, after one check. //Using this as a foundation stone to write some tests: for (int i = 0; i < asRef.size(); i++) { String command = "Assert.assertEquals( " + "likelihoodFunctions.BetaBinomLogLik(" + String.format("%.15g", dispArray[i]) + "," + String.format("%.15g", alphaParam) + "," + String.format("%.15g", betaParam) + "," + Integer.toString(asRefArray[i]) + "," + Integer.toString(asAltArray[i]) + ")" + ", " + Double.toString(LikelihoodFunctions.BetaBinomLogLik(dispArray[i], alphaParam, betaParam, asRefArray[i], asAltArray[i])) + ");"; System.out.println(command); } } } }
From source file:nl.systemsgenetics.cellTypeSpecificAlleleSpecificExpression.BetaBinomOverdispInSample.java
@SuppressWarnings({ "empty-statement", "empty-statement" })
public BetaBinomOverdispInSample(String filename) throws FileNotFoundException, IOException {
sampleName = filename;/*from w w w .j a v a2 s .c om*/
if (GlobalVariables.verbosity >= 10) {
System.out.println("\n--- Starting beta binomial dispersion estimate ---");
System.out.println("AS File: " + sampleName);
System.out.println("--------------------------------------------------");
}
ArrayList<IndividualSnpData> allSnps = new ArrayList<IndividualSnpData>();
File file = new File(filename);
FileReader fr = new FileReader(file);
BufferedReader br = new BufferedReader(fr);
String line;
while ((line = br.readLine()) != null) {
allSnps.add(new IndividualSnpData(file.getAbsolutePath(), line));
}
br.close();
fr.close();
ArrayList<IndividualSnpData> hets;
hets = UtilityMethods.isolateHeterozygotesFromIndividualSnpData(allSnps);
int numOfHets = hets.size();
int[] asRef = new int[numOfHets];
int[] asAlt = new int[numOfHets];
int totalRef = 0;
int totalAlt = 0;
for (int i = 0; i < numOfHets; i++) {
asRef[i] = hets.get(i).getRefNum();
asAlt[i] = hets.get(i).getAltNum();
totalRef += asRef[i];
totalAlt += asAlt[i];
}
if (GlobalVariables.verbosity >= 10) {
System.out.println("sample loaded");
System.out.println("\ttotal_ref = " + totalRef);
System.out.println("\ttotal_alt = " + totalAlt);
}
BetaBinomLikelihoodForOverdispersion betaBinom = new BetaBinomLikelihoodForOverdispersion(asRef, asAlt);
NelderMeadSimplex simplex;
simplex = new NelderMeadSimplex(1);
SimplexOptimizer optimizer = new SimplexOptimizer(GlobalVariables.simplexThreshold,
GlobalVariables.simplexThreshold); //numbers are to which precision you want it to be done.
PointValuePair solution = optimizer.optimize(new ObjectiveFunction(betaBinom),
new MaxEval(GlobalVariables.maximumIterations), simplex, GoalType.MINIMIZE,
new InitialGuess(new double[] { 0.5 }), new SearchInterval(0.0, 1.0));
overdispersion = solution.getFirst();
if (GlobalVariables.verbosity >= 10) {
System.out.println("Log likelihood converged to a threshold of "
+ Double.toString(GlobalVariables.simplexThreshold));
System.out.println("\tDispersion sigma: " + Double.toString(overdispersion[0]));
System.out.println("\tLog likelihood: " + Double.toString(betaBinom.value(overdispersion)));
}
}
From source file:nl.systemsgenetics.cellTypeSpecificAlleleSpecificExpression.CTSBetaBinomialTest.java
public CTSBetaBinomialTest(ArrayList<IndividualSnpData> all_individuals) throws Exception { //basic information, get the zero instance. snpName = all_individuals.get(0).getSnpName(); chromosome = all_individuals.get(0).getChromosome(); position = all_individuals.get(0).getPosition(); //isolate heterozygote individuals. ArrayList<IndividualSnpData> het_individuals = UtilityMethods .isolateHeterozygotesFromIndividualSnpData(all_individuals); numberOfHets = het_individuals.size(); hetSampleNames = new ArrayList<String>(); asRef = new ArrayList<Integer>(); asAlt = new ArrayList<Integer>(); asNo = new ArrayList<Integer>(); dispersion = new ArrayList<Double>(); cellProp = new ArrayList<Double>(); int total_overlap = 0; //Get the basic data without doing any tests. for (IndividualSnpData temp_het : het_individuals) { //Do nothing if there is no data in het_individuals hetSampleNames.add(temp_het.getSampleName()); asRef.add(temp_het.getRefNum()); asAlt.add(temp_het.getAltNum()); asNo.add(temp_het.getNoNum());/* w w w.j a va 2 s . c o m*/ dispersion.add(temp_het.getDispersion()); cellProp.add(temp_het.getCellTypeProp()); //this is used to check if we will continue with calculations. //BASED on the minHets and MinReads total_overlap += temp_het.getRefNum() + temp_het.getAltNum(); } //Check if we do a test. if ((total_overlap >= GlobalVariables.minReads) && (numberOfHets >= GlobalVariables.minHets)) { // There is data to perform the binomial test, perform it. if (GlobalVariables.verbosity >= 100) { System.out.println(); System.out.println("SNP name: " + snpName); System.out.println("at: chr" + chromosome + ":" + position); System.out.println("------------------------------------------------------"); System.out.println("Num of hets: " + Integer.toString(numberOfHets)); System.out.println(het_individuals.get(0).getSnpName()); System.out.println(total_overlap); System.out.println("asRef: " + asRef.toString()); System.out.println("asAlt: " + asAlt.toString()); System.out.println("dispersion: " + dispersion.toString()); System.out.println("cellProp: " + cellProp.toString()); System.out.println("Starting non-CTS Beta binomial Null estimation"); } /* Going to do this in a two step procedure: 1. Determine a 2 parameter null the same way as what is done in the beta binomial alt, with starting values: This will be the actual null model in the CTS beta binomial output 2. Determine the cell type specific proportion, but now we use a cell type specific model as the alternative. */ Integer[] asRefArray = asRef.toArray(new Integer[asRef.size()]); Integer[] asAltArray = asAlt.toArray(new Integer[asAlt.size()]); Double[] dispArray = dispersion.toArray(new Double[dispersion.size()]); Double[] cellPropArray = cellProp.toArray(new Double[cellProp.size()]); try { BetaBinomAltLikelihood betaBinomNull; betaBinomNull = new BetaBinomAltLikelihood(asRefArray, asAltArray, dispArray); NelderMeadSimplex simplex; simplex = new NelderMeadSimplex(2); SimplexOptimizer optimizer = new SimplexOptimizer(GlobalVariables.simplexThreshold, GlobalVariables.simplexThreshold); //numbers are to which precision you want it to be done. PointValuePair solutionNull = optimizer.optimize(new ObjectiveFunction(betaBinomNull), new MaxEval(GlobalVariables.maximumIterations), simplex, GoalType.MINIMIZE, new InitialGuess(new double[] { 0.5, 0.5 }), new SearchInterval(-1000.0, 1000.0)); double[] valueNull = solutionNull.getPoint(); nullLogLik = betaBinomNull.value(valueNull); nulliterations = optimizer.getIterations(); NullAlphaParam = valueNull[0]; NullBetaParam = valueNull[1]; NullbinomRatio = valueNull[0] / (valueNull[0] + valueNull[1]); if (GlobalVariables.verbosity >= 100) { System.out.println("LogLik of Null converged to a threshold of " + Double.toString(GlobalVariables.simplexThreshold)); System.out.println("\tNull Alpha parameter: " + Double.toString(valueNull[0])); System.out.println("\tNull Beta parameter: " + Double.toString(valueNull[1])); System.out.println("\tIterations to converge: " + Integer.toString(nulliterations) + "\n"); } //CHECK WHAT THE version does in terms of loglik. CTSbetaBinomialAltLikelihoodVersion2 CTSbetaBinomAlt; CTSbetaBinomAlt = new CTSbetaBinomialAltLikelihoodVersion2(asRefArray, asAltArray, dispArray, cellPropArray); // Going to make some initialGuesses, because sometimes // this falls into a local minimum with the MLE, when only the // null parameters are used as a starting point. double nonCTSprop; nonCTSprop = valueNull[0] / (valueNull[0] + valueNull[1]); ArrayList<InitialGuess> GuessList = new ArrayList<InitialGuess>(); GuessList.add(new InitialGuess(new double[] { 0.0, nonCTSprop })); GuessList.add(new InitialGuess(new double[] { 0.25, 0.5 })); GuessList.add(new InitialGuess(new double[] { 0.75, 0.5 })); simplex = new NelderMeadSimplex(2); ArrayList<double[]> OptimizerResults = new ArrayList<double[]>(); Double[] OptimizedLogLik = new Double[8]; int i = 0; int lowestIndices = -1; double lowestLogLik = 0; for (InitialGuess IGuess : GuessList) { PointValuePair solutionAlt = optimizer.optimize(new ObjectiveFunction(CTSbetaBinomAlt), new MaxEval(500), simplex, GoalType.MINIMIZE, IGuess, new SearchInterval(0, 1)); double[] valueAlt = solutionAlt.getPoint(); OptimizerResults.add(valueAlt); OptimizedLogLik[i] = CTSbetaBinomAlt.value(valueAlt); //determine lowest loglik. if (i == 0) { lowestIndices = 0; lowestLogLik = OptimizedLogLik[i]; } else if (OptimizedLogLik[i] < lowestLogLik) { lowestIndices = i; lowestLogLik = OptimizedLogLik[i]; } if (GlobalVariables.verbosity >= 100) { System.out.println("\nAlt Loglik convergence of starting coordinates" + Arrays.toString(IGuess.getInitialGuess())); System.out.println("\tFinal parameters: " + Arrays.toString(valueAlt)); System.out.println("\tLogLik: " + Double.toString(OptimizedLogLik[i])); } i++; } //Now select the lowest LogLik. double[] bestParams = OptimizerResults.get(lowestIndices); altLogLik = OptimizedLogLik[lowestIndices]; binomRatioCellType = bestParams[0]; binomRatioResidual = bestParams[1]; //chi squared statistic is determined based on both null and alt loglikelihoods. chiSq = LikelihoodFunctions.ChiSqFromLogLik(nullLogLik, altLogLik); //determine P value based on distribution pVal = LikelihoodFunctions.determinePvalFrom1DFchiSq(chiSq); if (GlobalVariables.verbosity >= 10) { System.out.println("\n--- Starting cell type specific beta binomial LRT test estimate ---"); System.out.println("LogLik of converged to a threshold of " + Double.toString(GlobalVariables.simplexThreshold) + "\n"); System.out.println( "\tCellType Binomial ratio: " + Double.toString(binomRatioCellType) + "\n"); System.out.println( "\tResidual Binomial ratio: " + Double.toString(binomRatioResidual) + "\n"); System.out.println("\tNull log likelihood: " + Double.toString(nullLogLik)); System.out.println("\tAlt log likelihood: " + Double.toString(altLogLik) + "\n"); System.out.println("\tChisq statistic: " + Double.toString(chiSq)); System.out.println("\tP value: " + Double.toString(pVal)); System.out.println("-----------------------------------------------------------------------"); } testConverged = true; } catch (TooManyEvaluationsException e) { if (GlobalVariables.verbosity >= 1) { System.out.println("WARNING: Did not converge to a solution for SNP " + snpName + "in cell type specific beta binomial"); System.out.println(" After " + Integer.toString(GlobalVariables.maximumIterations) + " iterations."); System.out.println(" Continue-ing with the next."); } } //Finally test was done, so we say the test was performed. testPerformed = true; } }
From source file:org.dawnsci.plotting.tools.powdercheck.PowderCheckJob.java
private List<PowderCheckResult> fitPeaksToTrace(final Dataset xIn, final Dataset yIn, Dataset baselineIn) { resultList.clear();//from w w w . j a v a 2s . co m List<HKL> spacings = CalibrationFactory.getCalibrationStandards().getCalibrant().getHKLs(); final double[] qVals = new double[spacings.size()]; for (int i = 0; i < spacings.size(); i++) { if (xAxis == XAxis.ANGLE) qVals[i] = 2 * Math.toDegrees(Math.asin((metadata.getDiffractionCrystalEnvironment().getWavelength() / (2 * spacings.get(i).getDNano() * 10)))); else qVals[i] = (Math.PI * 2) / (spacings.get(i).getDNano() * 10); } double qMax = xIn.max().doubleValue(); double qMin = xIn.min().doubleValue(); List<Double> qList = new ArrayList<Double>(); int count = 0; for (double q : qVals) { if (q > qMax || q < qMin) continue; count++; qList.add(q); } double minPeak = Collections.min(qList); double maxPeak = Collections.max(qList); int minXidx = ROISliceUtils.findPositionOfClosestValueInAxis(xIn, minPeak) - EDGE_PIXEL_NUMBER; int maxXidx = ROISliceUtils.findPositionOfClosestValueInAxis(xIn, maxPeak) + EDGE_PIXEL_NUMBER; int maxSize = xIn.getSize(); minXidx = minXidx < 0 ? 0 : minXidx; maxXidx = maxXidx > maxSize - 1 ? maxSize - 1 : maxXidx; final Dataset x = xIn.getSlice(new int[] { minXidx }, new int[] { maxXidx }, null); final Dataset y = yIn.getSlice(new int[] { minXidx }, new int[] { maxXidx }, null); y.setName("Fit"); Dataset baseline = baselineIn.getSlice(new int[] { minXidx }, new int[] { maxXidx }, null); List<APeak> peaks = Generic1DFitter.fitPeaks(x, y, Gaussian.class, count + 10); List<PowderCheckResult> initResults = new ArrayList<PowderCheckResult>(); CompositeFunction cf = new CompositeFunction(); for (APeak peak : peaks) cf.addFunction(peak); double limit = findMatchLimit(qList, cf); while (cf.getNoOfFunctions() != 0 && !qList.isEmpty()) findMatches(initResults, qList, cf, limit); final CompositeFunction cfFinal = compositeFunctionFromResults(initResults); double[] initParam = new double[cfFinal.getFunctions().length * 3]; { int i = 0; for (IFunction func : cfFinal.getFunctions()) { initParam[i++] = func.getParameter(0).getValue(); initParam[i++] = func.getParameter(1).getValue(); initParam[i++] = func.getParameter(2).getValue(); } } final Dataset yfit = DatasetFactory.zeros(x, Dataset.FLOAT64); MultivariateOptimizer opt = new SimplexOptimizer(REL_TOL, ABS_TOL); MultivariateFunction fun = new MultivariateFunction() { @Override public double value(double[] arg0) { int j = 0; for (IFunction func : cfFinal.getFunctions()) { double[] p = func.getParameterValues(); p[0] = arg0[j++]; p[1] = arg0[j++]; p[2] = arg0[j++]; func.setParameterValues(p); } for (int i = 0; i < yfit.getSize(); i++) { yfit.set(cfFinal.val(x.getDouble(i)), i); } return y.residual(yfit); } }; opt.optimize(new InitialGuess(initParam), GoalType.MINIMIZE, new ObjectiveFunction(fun), new MaxEval(MAX_EVAL), new NelderMeadSimplex(initParam.length)); Dataset fit = Maths.add(yfit, baseline); fit.setName("Fit"); Dataset residual = Maths.subtract(y, yfit); residual.setName("Residual"); system.updatePlot1D(x, Arrays.asList(new IDataset[] { fit, residual }), null); setPlottingSystemAxes(); for (int i = 0; i < cfFinal.getNoOfFunctions(); i++) { resultList.add(new PowderCheckResult(cfFinal.getFunction(i), initResults.get(i).getCalibrantQValue())); } return resultList; }
From source file:org.hawkular.datamining.forecast.models.AbstractModelOptimizer.java
protected void optimize(double[] initialGuess, MultivariateFunctionMappingAdapter costFunction) { // Nelder-Mead Simplex SimplexOptimizer optimizer = new SimplexOptimizer(0.0001, 0.0001); PointValuePair unBoundedResult = optimizer.optimize(GoalType.MINIMIZE, new MaxIter(MAX_ITER), new MaxEval(MAX_EVAL), new InitialGuess(initialGuess), new ObjectiveFunction(costFunction), new NelderMeadSimplex(initialGuess.length)); result = costFunction.unboundedToBounded(unBoundedResult.getPoint()); }