List of usage examples for org.apache.commons.lang ArrayUtils EMPTY_DOUBLE_ARRAY
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double array. From source file:com.opengamma.analytics.financial.var.parametric.ParametricVaRDataBundleTest.java
@Test(expectedExceptions = IllegalArgumentException.class) public void testWrongMatrixDimension() { final Matrix<double[]> m = new Matrix<double[]>() { @Override/*from www. ja v a 2 s . c o m*/ public int getNumberOfElements() { return 0; } @Override public double[] getEntry(final int... indices) { return ArrayUtils.EMPTY_DOUBLE_ARRAY; } }; new ParametricVaRDataBundle(m, COV, ORDER); }
From source file:com.opengamma.financial.analytics.model.YieldCurveNodeSensitivitiesHelper.java
public static Set<ComputedValue> getTimeLabelledSensitivitiesForCurve(final List<DoublesPair> resultList, final ValueSpecification resultSpec) { final int n = resultList.size(); final Double[] keys = new Double[n]; final double[] values = new double[n]; final Object[] labels = new Object[n]; LabelledMatrix1D<Double, Double> labelledMatrix = new DoubleLabelledMatrix1D( ArrayUtils.EMPTY_DOUBLE_OBJECT_ARRAY, ArrayUtils.EMPTY_OBJECT_ARRAY, ArrayUtils.EMPTY_DOUBLE_ARRAY); for (int i = 0; i < n; i++) { final DoublesPair pair = resultList.get(i); keys[i] = pair.first;/*from ww w . jav a2 s . c om*/ values[i] = pair.second; labels[i] = s_formatter.format(pair.first); labelledMatrix = labelledMatrix.add(pair.first, s_formatter.format(pair.first), pair.second, 1e-16); } return Collections.singleton(new ComputedValue(resultSpec, labelledMatrix)); }
From source file:gov.nih.nci.caarray.util.CaArrayUtilsTest.java
@Test public void testSplitValues() { assertTrue(/*from w w w. j ava 2 s. co m*/ Arrays.equals(ArrayUtils.EMPTY_BOOLEAN_ARRAY, CaArrayUtils.splitIntoBooleans(EMPTY_STRING, " "))); assertTrue(Arrays.equals(new boolean[] { true }, CaArrayUtils.splitIntoBooleans("true", ","))); assertTrue(Arrays.equals(new boolean[] { true, false, true }, CaArrayUtils.splitIntoBooleans("true;false;true", ";"))); assertTrue(Arrays.equals(ArrayUtils.EMPTY_SHORT_ARRAY, CaArrayUtils.splitIntoShorts(EMPTY_STRING, " "))); assertTrue(Arrays.equals(new short[] { 0 }, CaArrayUtils.splitIntoShorts("0", ","))); assertTrue(Arrays.equals(new short[] { 3, 1, 2 }, CaArrayUtils.splitIntoShorts("3;1;2", ";"))); assertTrue(Arrays.equals(ArrayUtils.EMPTY_LONG_ARRAY, CaArrayUtils.splitIntoLongs(EMPTY_STRING, " "))); assertTrue(Arrays.equals(new long[] { 5L }, CaArrayUtils.splitIntoLongs("5", ","))); assertTrue(Arrays.equals(new long[] { 15L, 19L, 13L }, CaArrayUtils.splitIntoLongs("15/19/13", "/"))); assertTrue(Arrays.equals(ArrayUtils.EMPTY_INT_ARRAY, CaArrayUtils.splitIntoInts(EMPTY_STRING, " "))); assertTrue(Arrays.equals(new int[] { 1001 }, CaArrayUtils.splitIntoInts("1001", ","))); assertTrue(Arrays.equals(new int[] { -1, 0, -2 }, CaArrayUtils.splitIntoInts("-1 0 -2", " "))); assertTrue(Arrays.equals(ArrayUtils.EMPTY_FLOAT_ARRAY, CaArrayUtils.splitIntoFloats(EMPTY_STRING, " "))); assertTrue(Arrays.equals(new float[] { 1.65f }, CaArrayUtils.splitIntoFloats("1.65", ","))); assertTrue(Arrays.equals(new float[] { 0f, 0.33f, -3.76f }, CaArrayUtils.splitIntoFloats("0 0.33 -3.76", " "))); assertTrue(Arrays.equals(ArrayUtils.EMPTY_DOUBLE_ARRAY, CaArrayUtils.splitIntoDoubles(EMPTY_STRING, " "))); assertTrue(Arrays.equals(new double[] { 1.0 / 3.0 }, CaArrayUtils.splitIntoDoubles("0.3333333333333333", ","))); assertTrue(Arrays.equals(new double[] { 0, -5.0 / 7.0, 11.0 / 13.0 }, CaArrayUtils.splitIntoDoubles("0.0;-0.7142857142857143;0.8461538461538461", ";"))); assertTrue(Arrays.equals(ArrayUtils.EMPTY_STRING_ARRAY, CaArrayUtils.splitFromCsv(EMPTY_STRING))); assertTrue(Arrays.equals(new String[] { "Iam,Dan" }, CaArrayUtils.splitFromCsv("Iam\\,Dan"))); assertTrue(Arrays.equals(new String[] { "I", "m,e", "mi ne" }, CaArrayUtils.splitFromCsv("I,m\\,e,mi ne"))); }
From source file:edu.cornell.med.icb.goby.R.FisherExact.java
/** * Pass the fisher expression to R for computation. * * @param rengine The R engine to use to calcuate the results. * @param fisherExpression The string representing the expression to evaluate. * @param is2x2matrix Whether or not the data being evaluated represents a 2x2 matrix * @return The results of the evaluation (may be null if an exception interrupts the calculation) *//*from w w w . java 2 s . c o m*/ private static Result evaluateFisherExpression(final Rengine rengine, final String fisherExpression, final boolean is2x2matrix) { // evaluate the R expression if (LOG.isDebugEnabled()) { LOG.debug("About to evaluate: " + fisherExpression); } final REXP fisherResultExpression = rengine.eval(fisherExpression); if (LOG.isDebugEnabled()) { LOG.debug(fisherResultExpression); } if (fisherResultExpression == null) { return null; } // the result from R is a vector/map of values final RVector fisherResultVector = fisherResultExpression.asVector(); if (LOG.isDebugEnabled()) { LOG.debug(fisherResultVector); } // extract the p-value final REXP pValueExpression = fisherResultVector.at("p.value"); final double pValue; if (pValueExpression != null) { pValue = pValueExpression.asDouble(); } else { pValue = Double.NaN; } // extract the alternative hypothesis final REXP alternativeExpression = fisherResultVector.at("alternative"); final String alternative = alternativeExpression.asString(); if (LOG.isDebugEnabled()) { LOG.debug("alternative: " + alternative); } final AlternativeHypothesis alternativeHypothesis = AlternativeHypothesis .valueOf(StringUtils.remove(alternative, '.')); // some values are only returned when the input was a 2x2 matrix final double estimate; final double[] confidenceInterval; final double oddsRatio; if (is2x2matrix) { final REXP estimateExpression = fisherResultVector.at("estimate"); if (estimateExpression != null) { estimate = estimateExpression.asDouble(); } else { estimate = Double.NaN; } if (LOG.isDebugEnabled()) { LOG.debug(estimateExpression); LOG.debug("estimate: " + estimate); } final REXP confidenceIntervalExpression = fisherResultVector.at("conf.int"); if (confidenceIntervalExpression != null) { confidenceInterval = confidenceIntervalExpression.asDoubleArray(); } else { confidenceInterval = ArrayUtils.EMPTY_DOUBLE_ARRAY; } if (LOG.isDebugEnabled()) { LOG.debug(confidenceIntervalExpression); LOG.debug("confidenceInterval: " + ArrayUtils.toString(confidenceInterval)); } final REXP oddsRatioExpression = fisherResultVector.at("null.value"); if (oddsRatioExpression != null) { oddsRatio = oddsRatioExpression.asDouble(); } else { oddsRatio = Double.NaN; } if (LOG.isDebugEnabled()) { LOG.debug(oddsRatioExpression); LOG.debug("oddsRatio: " + ArrayUtils.toString(oddsRatio)); } } else { // these values are not present in the 2xN case estimate = Double.NaN; confidenceInterval = ArrayUtils.EMPTY_DOUBLE_ARRAY; oddsRatio = Double.NaN; } return new Result(pValue, confidenceInterval, estimate, oddsRatio, alternativeHypothesis); }
From source file:edu.cornell.med.icb.optimization.OptimizeSubSet.java
private void convertFittestToSolution(final IChromosome fittestChromosome) { if (fittestChromosome == null) { fitestSubset = null;//from w ww .j ava 2 s.com fitestParams = ArrayUtils.EMPTY_DOUBLE_ARRAY; } else { fitestSubset = ((SubsetSuperGene) fittestChromosome.getGene(0)).getSubSet(); fitestParams = FitnessFunctionAdapter.getParameterValues(fittestChromosome, allPossibleParameterValues); } }
From source file:gda.hrpd.pmac.EpicsCVScanController.java
private double[] get2Theta() { double[] x1 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try {//from w w w . ja v a 2 s.com // logger.info("gets MAC1X"); x1 = getMAC1X(); // logger.info("gets MAC1X DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC1X", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC1X", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC1X", e); } double[] x2 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try { // logger.info("gets MAC2X"); x2 = getMAC2X(); // logger.info("gets MAC2X DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC2X", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC2X", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC2X", e); } double[] x3 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try { // logger.info("gets MAC3X"); x3 = getMAC3X(); // logger.info("gets MAC3X DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC3X", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC3X", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC3X", e); } double[] x4 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try { // logger.info("gets MAC4X"); x4 = getMAC4X(); // logger.info("gets MAC4X DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC4X", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC4X", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC4X", e); } double[] x5 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try { // logger.info("gets MAC5X"); x5 = getMAC5X(); // logger.info("gets MAC5X DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC5X", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC5X", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC5X", e); } return ArrayUtils.subarray( ArrayUtils.addAll(ArrayUtils.addAll(ArrayUtils.addAll(ArrayUtils.addAll(x1, x2), x3), x4), x5), 16500, 305000); }
From source file:gda.hrpd.pmac.EpicsCVScanController.java
private double[] getCount() { double[] y1 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try {//w w w. j ava 2s . c o m // logger.info("gets MAC1Y"); y1 = getMAC1Y(); // logger.info("gets MAC1Y DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC1Y", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC1Y", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC1Y", e); } double[] y2 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try { // logger.info("gets MAC2Y"); y2 = getMAC2Y(); // logger.info("gets MAC2Y DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC2Y", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC2Y", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC2Y", e); } double[] y3 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try { // logger.info("gets MAC3Y"); y3 = getMAC3Y(); // logger.info("gets MAC3Y DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC3Y", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC3Y", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC3Y", e); } double[] y4 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try { // logger.info("gets MAC4Y"); y4 = getMAC4Y(); // logger.info("gets MAC4Y DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC4Y", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC4Y", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC4Y", e); } double[] y5 = ArrayUtils.EMPTY_DOUBLE_ARRAY; try { // logger.info("gets MAC5Y"); y5 = getMAC5Y(); // logger.info("gets MAC5Y DONE"); } catch (TimeoutException e) { logger.error("Timeout while gets MAC5Y", e); e.printStackTrace(); } catch (CAException e) { logger.error("CAException while gets MAC5Y", e); e.printStackTrace(); } catch (InterruptedException e) { logger.error("InterruptedException while gets MAC5Y", e); } return ArrayUtils.subarray( ArrayUtils.addAll(ArrayUtils.addAll(ArrayUtils.addAll(ArrayUtils.addAll(y1, y2), y3), y4), y5), 16500, 305000); }
From source file:org.bdval.DiscoverWithGeneticAlgorithm.java
@Override public void process(final DAVOptions options) { super.process(options); //final Table inputTable = options.inputTable; System.out.println("Optimizing " + (this.optimizeMeasureName)); for (final ClassificationTask task : options.classificationTasks) { for (final GeneList geneList : options.geneLists) { int reducedNumProbeset; int iteration = 1; System.out.println("Processing gene list: " + geneList.toString()); try { int numKept; GeneList activeGeneList = geneList; IntSet sourceSet = new IntArraySet(); IntSet previousSubset = null; IntSet fitestSubSet = null; double[] fitestParamValues = ArrayUtils.EMPTY_DOUBLE_ARRAY; options.trainingPlatform = new GEOPlatformIndexed(); Table processedTable = processTable(activeGeneList, options.inputTable, options, MicroarrayTrainEvaluate.calculateLabelValueGroups(task)); // try to free some memory (we cannot call processTable anymore after this:) options.inputTable = null; System.gc();/*from w ww. j a v a 2 s. com*/ do { System.out.println("Discover markers with GA wrapper for " + task); final EvaluationMeasure evalMeasure; if (iteration == 1) { // FIRST ITERATION final ClassificationHelper helper = getClassifier(processedTable, MicroarrayTrainEvaluate.calculateLabelValueGroups(task)); final RandomEngine randomEngine = new MersenneTwister(options.randomSeed); final CrossValidation CV = new CrossValidation(helper.classifier, helper.problem, randomEngine); final boolean useR = optimizationRequiresR(); CV.useRServer(useR); // user R server only when required. CV.setRepeatNumber(cvRepeatNumber); CV.setScalerClass(options.scalerClass); if (useRServer) { CV.evaluateMeasure(optimizeMeasureName); } // CV.setScalerClass(PercentileScalingRowProcessor.class); evalMeasure = CV.crossValidation(foldNumber); System.out.println("Initial " + getPerformanceMeasureName() + " measure: " + getOptimizationMeasure(evalMeasure)); numKept = options.trainingPlatform.getProbeIds().size(); for (int i = 0; i < numKept; i++) { sourceSet.add(i); } inputIdentifiers = options.trainingPlatform; if (numKept <= numProbesets) { LOG.error("Cannot remove probesets, already below the target number."); fitestSubSet = sourceSet; break; } } else { numKept = previousSubset.size(); sourceSet = previousSubset; } localOptions = options; System.out.println("Num probeset as input : " + numKept); final SubSetFitnessFunction convergenceCriterion; final Table processedTableConstant = processedTable; convergenceCriterion = new AbstractSubSetFitnessFunction() { @Override public double evaluate(final IntSet subset, final double[] paramValues) { try { GeneList geneListFromSubset = convertSubsetToGeneList(subset); Table filteredTable = filterTable(options, processedTableConstant, geneListFromSubset); ClassificationHelper helper = getClassifier(filteredTable, MicroarrayTrainEvaluate.calculateLabelValueGroups(task)); int paramIndex = 0; for (final String parameterName : getParameterNames(discreteParameters)) { helper.parameters.setParameter(parameterName, paramValues[paramIndex++]); } helper.classifier.setParameters(helper.parameters); RandomEngine randomEngine = new MersenneTwister(options.randomSeed); CrossValidation CV = new CrossValidation(helper.classifier, helper.problem, randomEngine); final boolean useR = optimizationRequiresR(); CV.useRServer(useR); CV.setRepeatNumber(cvRepeatNumber); CV.setScalerClass(options.scalerClass); CV.evaluateMeasure(optimizeMeasureName); final EvaluationMeasure eMeasure = CV.crossValidation(foldNumber); geneListFromSubset = null; filteredTable = null; helper = null; randomEngine = null; CV = null; final double measure = getOptimizationMeasure(eMeasure); LOG.info(task.getExperimentDataFilename() + " evaluated " + getPerformanceMeasureName() + " " + measure); // double std = getOptimizationMeasureStd(eMeasure); // return measure -std; return measure; } catch (InvalidColumnException e) { LOG.error(e); System.exit(10); } catch (TypeMismatchException e) { LOG.error(e); System.exit(10); } throw new InternalError("This statement should never be reached."); } }; reducedNumProbeset = Math.max(numProbesets, (int) (numKept * ratio)); if (reducedNumProbeset == numKept) { System.out.println("Previous step optimized the same number of probesets already."); break; } else { System.out.println("Optimizing to keep " + reducedNumProbeset + " probesets."); } final OptimizeSubSet optimizationEngine = new OptimizeSubSet(sourceSet, reducedNumProbeset, convergenceCriterion, populationSize, discreteParameters); optimizationEngine.setModuloProgressReport(2); optimizationEngine.optimize(numOptimizationStepsPerIteration, .001); fitestSubSet = optimizationEngine.getFitestSubset(); fitestParamValues = optimizationEngine.getFitestParameterValues(); previousSubset = fitestSubSet; // use as gene list for the next iteration: the restriction of the previous gene list // to those probesets that were found with to have the bext F-1 measure in this iteration. activeGeneList = convertSubsetToGeneList(fitestSubSet); System.out.println("numKept: " + numKept + " reducedNumber: " + reducedNumProbeset); if (!writeGeneListFormat) { // print intermediate result for this iteration. printFeatures(options, iteration, fitestSubSet, convergenceCriterion.evaluate(fitestSubSet, fitestParamValues)); } iteration++; final GeneList geneListFromSubset = convertSubsetToGeneList(fitestSubSet); final Table fitestTable = filterTable(options, processedTable, geneListFromSubset); processedTable = fitestTable; // hint to the JVM that this would be a good time to garbage collect. System.gc(); } while (numKept > numProbesets); printFeatures(options, iteration, fitestSubSet, 0); printFitestParamValues(options, fitestParamValues, getParameterNames(discreteParameters)); } catch (Exception e) { LOG.error(e); e.printStackTrace(); System.exit(10); } } } }
From source file:org.bdval.signalquality.SimpleSignalQualityCalculator.java
/** * Compute the signal quality between the two sets of data. * Input maps have a key of the probe id, such as "AA799301_Probe1" * Map value per key is a double[] of values. * <p/>//from w w w. java2 s. c o m * One idea Fabien had is to write a TSV file that contains * ModelID Feature p-Value * for all models and then do various post processing on that one * file using a separate tool instead of doing the entire calculation here? * * @param model the model we are writing * @param modelId the model id we are calculating the signal quality for * @param classToDataMapMap map of classes + "-training"/"-validation" to the * map of feature to raw data. */ @Override public void calculatePValues(final BDVModel model, final String modelId, final String[] allClasses, final Map<String, Map<String, double[]>> classToDataMapMap) { // Call calculate in AbstractSignalQualityCalculator first super.calculatePValues(model, modelId, allClasses, classToDataMapMap); // System.out.println("(simple) Calculating signal quality for model " + modelId); // Calculate pValues and write them // Aquire the ENITRE feature set from both training and validation allFeaturesSet.clear(); for (final String sampleClass : allClasses) { final Map<String, double[]> trainingDataMap = classToDataMapMap.get(sampleClass + "-training"); final Map<String, double[]> validationDataMap = classToDataMapMap.get(sampleClass + "-validation"); allFeaturesSet.addAll(trainingDataMap.keySet()); allFeaturesSet.addAll(validationDataMap.keySet()); } for (final String featureId : allFeaturesSet) { double[] trainingData = null; double[] validationData = null; try { data.clear(); data.put("model-id", modelId); data.put("feature", featureId); for (int classIndex = 0; classIndex < allClasses.length; classIndex++) { final String classId = allClasses[classIndex]; final String classIdAppend = "[" + CLASS_TRANSLATION[classIndex] + "]"; trainingData = classToDataMapMap.get(classId + "-training").get(featureId); if (trainingData == null) { trainingData = ArrayUtils.EMPTY_DOUBLE_ARRAY; } validationData = classToDataMapMap.get(classId + "-validation").get(featureId); if (validationData == null) { validationData = ArrayUtils.EMPTY_DOUBLE_ARRAY; } if (trainingData.length == 0 || validationData.length == 0) { if (trainingData.length == 0) { writeData(String.format("# modelId=%s, featureId=%s, class=%s has no training data.", modelId, featureId, classId)); } if (validationData.length == 0) { writeData(String.format("# modelId=%s, featureId=%s, class=%s has no validation data.", modelId, featureId, classId)); } continue; } // System.out.printf("Training data size = %s Validation data size = %d%n", // trainingData.length, validationData.length); if (dataToPvalueScript == null) { dataToPvalueScript = RScript.createFromResource("rscripts/data_to_pvalue.R"); } dataToPvalueScript.setInput("x", trainingData); dataToPvalueScript.setInput("y", validationData); dataToPvalueScript.setOutput("p_value", RDataObjectType.Double); dataToPvalueScript.setOutput("test_statistic", RDataObjectType.Double); dataToPvalueScript.setOutput("sum_rank_features", RDataObjectType.DoubleArray); dataToPvalueScript.execute(); final double pvalue = dataToPvalueScript.getOutputDouble("p_value"); final double testStatistic = dataToPvalueScript.getOutputDouble("test_statistic"); final double[] sumRankFeatures = dataToPvalueScript.getOutputDoubleArray("sum_rank_features"); data.put("p-value" + classIdAppend, pvalue); data.put("test-statistics" + classIdAppend, testStatistic); data.put("t1" + classIdAppend, sumRankFeatures[0]); data.put("t2" + classIdAppend, sumRankFeatures[1]); if (model != null) { data.put("mean" + classIdAppend, model.getTrainingSetMeanValue(featureId)); data.put("range" + classIdAppend, model.getTrainingSetRangeValue(featureId)); } else { data.put("mean" + classIdAppend, -1d); data.put("range" + classIdAppend, -1d); } data.put("training-values" + classIdAppend, trainingData); data.put("validation-values" + classIdAppend, validationData); } writeData(data); } catch (Exception e) { LOG.error("Could not calculate KS-test. " + "Error data written to comment in output file"); writeData(String.format("# ERROR WITH %s.%s.trainingData=%s", modelId, featureId, ArrayUtils.toString(trainingData))); writeData(String.format("# ERROR WITH %s.%s.validationData=%s", modelId, featureId, ArrayUtils.toString(validationData))); } } }