List of usage examples for org.apache.commons.math3.stat.descriptive.moment Mean evaluate
@Override public double evaluate(final double[] values) throws MathIllegalArgumentException
From source file:nl.systemsgenetics.genenetworkbackend.hpo.TestDiseaseGenePerformance.java
/** * @param args the command line arguments * @throws java.lang.Exception/* w ww . ja v a 2 s . c o m*/ */ public static void main(String[] args) throws Exception { final File diseaseGeneHpoFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\HPO\\135\\ALL_SOURCES_ALL_FREQUENCIES_diseases_to_genes_to_phenotypes.txt"); final File ncbiToEnsgMapFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\ensgNcbiId.txt"); final File hgncToEnsgMapFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\ensgHgnc.txt"); final File ensgSymbolMappingFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\ensgHgnc.txt"); final File predictionMatrixFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\hpo_predictions_zscores.txt.gz"); final File predictionMatrixCorrelationFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\hpo_predictions_pathwayCorrelation.txt"); final File significantTermsFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\hpo_predictions_bonSigTerms.txt"); final double correctedPCutoff = 0.05; final File hpoOboFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\HPO\\135\\hp.obo"); final File hpoPredictionInfoFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\hpo_predictions_auc_bonferroni.txt"); final File hposToExcludeFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\hpoToExclude.txt"); final File skewnessFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\predictions\\skewnessSummary.txt"); final boolean randomize = true; final File annotationMatrixFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\PathwayMatrix\\ALL_SOURCES_ALL_FREQUENCIES_phenotype_to_genes.txt_matrix.txt.gz"); final File backgroundForRandomize = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\Data31995Genes05-12-2017\\PCA_01_02_2018\\PathwayMatrix\\Ensembl2Reactome_All_Levels.txt_genesInPathways.txt"); //final File backgroundForRandomize = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\expressedReactomeGenes.txt"); final boolean randomizeCustomBackground = true; Map<String, String> ensgSymbolMapping = loadEnsgToHgnc(ensgSymbolMappingFile); final File outputFile; final ArrayList<String> backgroundGenes; if (randomize) { if (randomizeCustomBackground) { System.err.println("First need to fix so ranking list contains all genes in background list"); return; // backgroundGenes = loadBackgroundGenes(backgroundForRandomize); // outputFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\hpoDiseaseBenchmarkRandomizedCustomBackground.txt"); } else { backgroundGenes = null; outputFile = new File( "C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\hpoDiseaseBenchmarkRandomizedExtraNorm.txt"); } } else { backgroundGenes = null; outputFile = new File("C:\\UMCG\\Genetica\\Projects\\GeneNetwork\\hpoDiseaseBenchmarkExtraNorm.txt"); } final HashMap<String, ArrayList<String>> ncbiToEnsgMap = loadNcbiToEnsgMap(ncbiToEnsgMapFile); final HashMap<String, ArrayList<String>> hgncToEnsgMap = loadHgncToEnsgMap(hgncToEnsgMapFile); final HashSet<String> exludedHpo = loadHpoExclude(hposToExcludeFile); final SkewnessInfo skewnessInfo = new SkewnessInfo(skewnessFile); LinkedHashSet<String> significantTerms = loadSignificantTerms(significantTermsFile); DoubleMatrixDataset<String, String> predictionMatrix = DoubleMatrixDataset .loadDoubleData(predictionMatrixFile.getAbsolutePath()); DoubleMatrixDataset<String, String> predictionMatrixSignificant = predictionMatrix .viewColSelection(significantTerms); DoubleMatrixDataset<String, String> predictionMatrixSignificantCorrelationMatrix = DoubleMatrixDataset .loadDoubleData(predictionMatrixCorrelationFile.getAbsolutePath()); DiseaseGeneHpoData diseaseGeneHpoData = new DiseaseGeneHpoData(diseaseGeneHpoFile, ncbiToEnsgMap, hgncToEnsgMap, exludedHpo, new HashSet(predictionMatrix.getHashRows().keySet()), "OMIM"); //NOTE if one would use a differnt background this needs to be updated HashSet<String> diseaseGenes = new HashSet<>(diseaseGeneHpoData.getDiseaseGenes()); if (randomize) { diseaseGeneHpoData = diseaseGeneHpoData.getPermutation(1, backgroundGenes); } for (String gene : diseaseGenes) { if (!predictionMatrixSignificant.containsRow(gene)) { throw new Exception("Error: " + gene); } } int[] mapGeneIndexToDiseaseGeneIndex = new int[predictionMatrix.rows()]; ArrayList<String> predictedGenes = predictionMatrix.getRowObjects(); int g2 = 0; for (int g = 0; g < predictedGenes.size(); ++g) { mapGeneIndexToDiseaseGeneIndex[g] = diseaseGenes.contains(predictedGenes.get(g)) ? g2++ : -1; } DoubleMatrixDataset<String, String> annotationnMatrix = DoubleMatrixDataset .loadDoubleData(annotationMatrixFile.getAbsolutePath()); DoubleMatrixDataset<String, String> annotationMatrixSignificant = annotationnMatrix .viewColSelection(significantTerms); HashMap<String, MeanSd> hpoMeanSds = calculatePathayMeansOfAnnotatedGenes(predictionMatrixSignificant, annotationMatrixSignificant); Map<String, PredictionInfo> predictionInfo = HpoFinder.loadPredictionInfo(hpoPredictionInfoFile); Ontology hpoOntology = HpoFinder.loadHpoOntology(hpoOboFile); HpoFinder hpoFinder = new HpoFinder(hpoOntology, predictionInfo); final int totalGenes = predictionMatrixSignificant.rows(); final int totalDiseaseGenes = diseaseGenes.size(); final double[] geneScores = new double[totalGenes]; final double[] geneScoresDiseaseGenes = new double[totalDiseaseGenes]; final NaturalRanking naturalRanking = new NaturalRanking(NaNStrategy.FAILED, TiesStrategy.MAXIMUM); CSVWriter writer = new CSVWriter(new FileWriter(outputFile), '\t', '\0', '\0', "\n"); String[] outputLine = new String[16]; int c = 0; outputLine[c++] = "Disease"; outputLine[c++] = "Gene"; outputLine[c++] = "Hgnc"; outputLine[c++] = "Rank"; outputLine[c++] = "RankAmongDiseaseGenes"; outputLine[c++] = "Z-score"; outputLine[c++] = "HPO_skewness"; outputLine[c++] = "Other_mean_skewness"; outputLine[c++] = "Other_max_skewness"; outputLine[c++] = "HPO_phenotypic_match_score"; outputLine[c++] = "HPO_count"; outputLine[c++] = "HPO_sum_auc"; outputLine[c++] = "HPO_mean_auc"; outputLine[c++] = "HPO_median_auc"; outputLine[c++] = "HPO_terms"; outputLine[c++] = "HPO_terms_match_score"; writer.writeNext(outputLine); Random random = new Random(1); Mean meanCalculator = new Mean(); Median medianCalculator = new Median(); for (DiseaseGeneHpoData.DiseaseGene diseaseGene : diseaseGeneHpoData.getDiseaseGeneHpos()) { String gene = diseaseGene.getGene(); String disease = diseaseGene.getDisease(); if (!predictionMatrixSignificant.containsRow(gene)) { continue; } Set<String> geneHpos = diseaseGeneHpoData.getDiseaseEnsgHpos(diseaseGene); LinkedHashSet<String> geneHposPredictable = new LinkedHashSet<>(); for (String hpo : geneHpos) { geneHposPredictable .addAll(hpoFinder.getTermsToNames(hpoFinder.getPredictableTerms(hpo, correctedPCutoff))); } if (geneHposPredictable.isEmpty()) { continue; } // if(geneHposPredictable.size() > 1){ // String hpoSelected = geneHposPredictable.toArray(new String[geneHposPredictable.size()])[random.nextInt(geneHposPredictable.size())]; // geneHposPredictable = new LinkedHashSet<>(1); // geneHposPredictable.add(hpoSelected); // } DoubleMatrixDataset<String, String> predictionCaseTerms = predictionMatrixSignificant .viewColSelection(geneHposPredictable); DoubleMatrix2D predictionCaseTermsMatrix = predictionCaseTerms.getMatrix(); double denominator = Math.sqrt(geneHposPredictable.size()); for (int g = 0; g < totalGenes; ++g) { geneScores[g] = predictionCaseTermsMatrix.viewRow(g).zSum() / denominator; if (Double.isNaN(geneScores[g])) { geneScores[g] = 0; } g2 = mapGeneIndexToDiseaseGeneIndex[g]; if (g2 >= 0) { geneScoresDiseaseGenes[g2] = geneScores[g]; } } double[] geneRanks = naturalRanking.rank(geneScores); int diseaseGeneIndex = predictionMatrixSignificant.getRowIndex(gene); double[] geneRanksDiseaseGenes = naturalRanking.rank(geneScoresDiseaseGenes); int diseaseGeneIndexInDiseaseGenesOnly = mapGeneIndexToDiseaseGeneIndex[diseaseGeneIndex]; double zscore = geneScores[diseaseGeneIndex]; double rank = (totalGenes - geneRanks[diseaseGeneIndex]) + 1; double rankAmongDiseaseGenes = (totalDiseaseGenes - geneRanksDiseaseGenes[diseaseGeneIndexInDiseaseGenesOnly]) + 1; double hpoPhenotypicMatchScore = 0; StringBuilder individualMatchScore = new StringBuilder(); boolean notFirst = false; int usedHpos = 0; double[] aucs = new double[geneHposPredictable.size()]; double sumAucs = 0; int i = 0; for (String hpo : geneHposPredictable) { usedHpos++; MeanSd hpoMeanSd = hpoMeanSds.get(hpo); double hpoPredictionZ = predictionMatrixSignificant.getElement(gene, hpo); double hpoPredictionOutlierScore = ((hpoPredictionZ - hpoMeanSd.getMean()) / hpoMeanSd.getSd()); if (notFirst) { individualMatchScore.append(';'); } notFirst = true; individualMatchScore.append(hpoPredictionOutlierScore); hpoPhenotypicMatchScore += hpoPredictionOutlierScore; aucs[i++] = predictionInfo.get(hpo).getAuc(); sumAucs += predictionInfo.get(hpo).getAuc(); } double meanAuc = meanCalculator.evaluate(aucs); double medianAuc = medianCalculator.evaluate(aucs); if (usedHpos == 0) { hpoPhenotypicMatchScore = Double.NaN; } else { hpoPhenotypicMatchScore = hpoPhenotypicMatchScore / usedHpos; } String symbol = ensgSymbolMapping.get(gene); if (symbol == null) { symbol = ""; } c = 0; outputLine[c++] = disease; outputLine[c++] = gene; outputLine[c++] = symbol; outputLine[c++] = String.valueOf(rank); outputLine[c++] = String.valueOf(rankAmongDiseaseGenes); outputLine[c++] = String.valueOf(zscore); outputLine[c++] = String.valueOf(skewnessInfo.getHpoSkewness(gene)); outputLine[c++] = String.valueOf(skewnessInfo.getMeanSkewnessExHpo(gene)); outputLine[c++] = String.valueOf(skewnessInfo.getMaxSkewnessExHpo(gene)); outputLine[c++] = String.valueOf(hpoPhenotypicMatchScore); outputLine[c++] = String.valueOf(geneHposPredictable.size()); outputLine[c++] = String.valueOf(sumAucs); outputLine[c++] = String.valueOf(meanAuc); outputLine[c++] = String.valueOf(medianAuc); outputLine[c++] = String.join(";", geneHposPredictable); outputLine[c++] = individualMatchScore.toString(); writer.writeNext(outputLine); } writer.close(); }
From source file:Rotationforest.Covariance.java
/** * Computes the covariance between the two arrays. * * <p>Array lengths must match and the common length must be at least 2.</p> * * @param xArray first data array/*from w w w. j ava 2s.c om*/ * @param yArray second data array * @param biasCorrected if true, returned value will be bias-corrected * @return returns the covariance for the two arrays * @throws MathIllegalArgumentException if the arrays lengths do not match or * there is insufficient data */ public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) throws MathIllegalArgumentException { Mean mean = new Mean(); double result = 0d; int length = xArray.length; if (length != yArray.length) { throw new MathIllegalArgumentException(LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, length, yArray.length); } else if (length < 2) { throw new MathIllegalArgumentException(LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, length, 2); } else { double xMean = mean.evaluate(xArray); double yMean = mean.evaluate(yArray); for (int i = 0; i < length; i++) { double xDev = xArray[i] - xMean; double yDev = yArray[i] - yMean; result += (xDev * yDev - result) / (i + 1); } } return biasCorrected ? result * ((double) length / (double) (length - 1)) : result; }
From source file:umd.lu.thesis.pums2010.DurPredictStDev.java
public static void main(String[] args) throws Exception { DurationPrediction dp = new DurationPrediction(); File f = new File(ThesisProperties.getProperties("simulation.pums2010.duration.prediction.output")); String line;/*from w w w .j a v a2 s . c o m*/ Person2010 p = new Person2010(); int d = -1; int toy = -1; // init predictionDiffs for all runs; this is the final result. HashMap<Integer, double[]> predictionDiffs = new HashMap<>(); for (int i = 0; i < obsCount; i++) { double[] tmpList = new double[runCounts]; predictionDiffs.put(i, tmpList); } HashMap<Integer, Double> predictions = new HashMap<>(); for (int count = 0; count < runCounts; count++) { System.out.println(" " + count + "/" + runCounts); // init observations and predictions for each run/file HashMap<Integer, Double> observations = new HashMap<>(); for (int i = 0; i < obsCount; i++) { observations.put(i, 0.0); predictions.put(i, 0.0); } try (FileInputStream fstream = new FileInputStream( ThesisProperties.getProperties("simulation.pums2010.duration.prediction.input")); BufferedReader br = new BufferedReader(new InputStreamReader(fstream));) { while ((line = br.readLine()) != null) { if (!line.toLowerCase().startsWith("id")) { // Columns needed: EG(137), BO(67), BR(70), BW(75), CP(94), CZ(104), CG(85) d = Integer.parseInt(ExcelUtils.getColumnValue(ExcelUtils.eg, line)); p.setHhType(Integer.parseInt(ExcelUtils.getColumnValue(ExcelUtils.bo, line))); p.setNp(Integer.parseInt(ExcelUtils.getColumnValue(ExcelUtils.br, line))); p.setIncLevel(Integer.parseInt(ExcelUtils.getColumnValue(ExcelUtils.bw, line))); p.setEmpStatus(Integer.parseInt(ExcelUtils.getColumnValue(ExcelUtils.cp, line))); toy = Integer.parseInt(ExcelUtils.getColumnValue(ExcelUtils.cz, line)); p.setAge(Integer.parseInt(ExcelUtils.getColumnValue(ExcelUtils.cg, line))); Integer durPrediction = dp.findTourDuration(p, d, TripType.BUSINESS, toy); Integer durObservation = Integer .parseInt(ExcelUtils.getColumnValue(ExcelUtils.db, line)) > 29 ? 29 : Integer.parseInt(ExcelUtils.getColumnValue(ExcelUtils.db, line)); observations.put(durObservation, observations.get(durObservation) + 1); // durPrediction is 1 to 30. Hence the minus 1. if (durPrediction < 1 || durPrediction > obsCount) { System.out.println("*** ERROR: durPrediction too large/small: " + durPrediction); System.exit(1); } predictions.put(durPrediction - 1, predictions.get(durPrediction - 1) + 1); } } // calculate diffs: for (int i = 0; i < obsCount; i++) { predictionDiffs.get(i)[count] = predictions.get(i); } br.close(); } catch (Exception ex) { System.out.println("---------------------" + d); System.out.println("---------- "); for (int key : predictions.keySet()) { System.out.println("-- key: " + key + ", value: " + predictions.get(key)); } sLog.error(ex.getLocalizedMessage(), ex); System.exit(1); } } // output results try (FileWriter fw = new FileWriter(f); BufferedWriter bw = new BufferedWriter(fw)) { for (int i = 0; i < obsCount; i++) { StandardDeviation stDevInstance = new StandardDeviation(); double stDev = stDevInstance.evaluate(predictionDiffs.get(i)); Mean avgInstance = new Mean(); double avg = avgInstance.evaluate(predictionDiffs.get(i)); bw.write(i + "\t" + avg + "\t" + stDev + "\n"); } } catch (Exception ex) { System.out.println("---------------------" + d); sLog.error(ex.getLocalizedMessage(), ex); System.exit(1); } }
From source file:util.comparisons.ComputeIndicators.java
public static void main(String[] args) throws IOException, InterruptedException { int[] numberOfObjectivesArray = new int[] { 2, 4 }; String[] problems = new String[] { "OO_MyBatis", "OA_AJHsqldb", "OA_AJHotDraw", "OO_BCEL", "OO_JHotDraw", "OA_HealthWatcher", // "OA_TollSystems", "OO_JBoss" }; HeuristicFunctionType[] heuristicFunctions = new HeuristicFunctionType[] { HeuristicFunctionType.ChoiceFunction, HeuristicFunctionType.MultiArmedBandit }; String[] algorithms = new String[] { "NSGA-II", // "SPEA2" };//from ww w . j av a 2 s . co m MetricsUtil metricsUtil = new MetricsUtil(); DecimalFormat decimalFormatter = new DecimalFormat("0.00E0"); Mean mean = new Mean(); StandardDeviation standardDeviation = new StandardDeviation(); InvertedGenerationalDistance igd = new InvertedGenerationalDistance(); GenerationalDistance gd = new GenerationalDistance(); Spread spread = new Spread(); Coverage coverage = new Coverage(); for (int objectives : numberOfObjectivesArray) { try (FileWriter IGDWriter = new FileWriter("experiment/IGD_" + objectives + ".tex"); FileWriter spreadWriter = new FileWriter("experiment/SPREAD_" + objectives + ".tex"); FileWriter GDWriter = new FileWriter("experiment/GD_" + objectives + ".tex"); FileWriter coverageWriter = new FileWriter("experiment/COVERAGE_" + objectives + ".tex")) { StringBuilder latexTableBuilder = new StringBuilder(); latexTableBuilder.append("\\documentclass{paper}\n").append("\n") .append("\\usepackage[T1]{fontenc}\n").append("\\usepackage[latin1]{inputenc}\n") .append("\\usepackage[hidelinks]{hyperref}\n").append("\\usepackage{tabulary}\n") .append("\\usepackage{booktabs}\n").append("\\usepackage{multirow}\n") .append("\\usepackage{amsmath}\n").append("\\usepackage{mathtools}\n") .append("\\usepackage{graphicx}\n").append("\\usepackage{array}\n") .append("\\usepackage[linesnumbered,ruled,inoutnumbered]{algorithm2e}\n") .append("\\usepackage{subfigure}\n").append("\\usepackage[hypcap]{caption}\n") .append("\\usepackage{pdflscape}\n").append("\n").append("\\begin{document}\n").append("\n") .append("\\begin{landscape}\n").append("\n"); pfKnown: { latexTableBuilder.append("\\begin{table}[!htb]\n").append("\t\\centering\n") .append("\t\\def\\arraystretch{1.5}\n") // .append("\t\\setlength{\\tabcolsep}{10pt}\n") // .append("\t\\fontsize{8pt}{10pt}\\selectfont\n") .append("\t\\caption{INDICATOR found for $PF_{known}$ for ").append(objectives) .append(" objectives}\n").append("\t\\label{tab:INDICATOR ").append(objectives) .append(" objectives}\n").append("\t\\begin{tabulary}{\\linewidth}{c"); for (String algorithm : algorithms) { latexTableBuilder.append("c"); for (HeuristicFunctionType heuristicFunction : heuristicFunctions) { latexTableBuilder.append("c"); } } latexTableBuilder.append("}\n").append("\t\t\\toprule\n").append("\t\t\\textbf{System}"); for (String algorithm : algorithms) { latexTableBuilder.append(" & \\textbf{").append(algorithm).append("}"); for (HeuristicFunctionType heuristicFunction : heuristicFunctions) { latexTableBuilder.append(" & \\textbf{").append(algorithm).append("-") .append(heuristicFunction.toString()).append("}"); } } latexTableBuilder.append("\\\\\n").append("\t\t\\midrule\n"); for (String problem : problems) { NonDominatedSolutionList trueFront = new NonDominatedSolutionList(); pfTrueComposing: { for (String algorithm : algorithms) { SolutionSet mecbaFront = metricsUtil.readNonDominatedSolutionSet( "resultado/" + algorithm.toLowerCase().replaceAll("-", "") + "/" + problem + "_Comb_" + objectives + "obj/All_FUN_" + algorithm.toLowerCase().replaceAll("-", "") + "-" + problem); trueFront.addAll(mecbaFront); for (HeuristicFunctionType hyperHeuristic : heuristicFunctions) { SolutionSet front = metricsUtil.readNonDominatedSolutionSet( "experiment/" + algorithm + "/" + objectives + "objectives/" + hyperHeuristic.toString() + "/" + problem + "/FUN.txt"); trueFront.addAll(front); } } } double[][] trueFrontMatrix = trueFront.writeObjectivesToMatrix(); HashMap<String, Double> igdMap = new HashMap<>(); HashMap<String, Double> gdMap = new HashMap<>(); HashMap<String, Double> spreadMap = new HashMap<>(); HashMap<String, Double> coverageMap = new HashMap<>(); for (String algorithm : algorithms) { double[][] mecbaFront = metricsUtil .readFront("resultado/" + algorithm.toLowerCase().replaceAll("-", "") + "/" + problem + "_Comb_" + objectives + "obj/All_FUN_" + algorithm.toLowerCase().replaceAll("-", "") + "-" + problem); igdMap.put(algorithm, igd.invertedGenerationalDistance(mecbaFront, trueFrontMatrix, objectives)); gdMap.put(algorithm, gd.generationalDistance(mecbaFront, trueFrontMatrix, objectives)); spreadMap.put(algorithm, spread.spread(mecbaFront, trueFrontMatrix, objectives)); coverageMap.put(algorithm, coverage.coverage(mecbaFront, trueFrontMatrix)); for (HeuristicFunctionType heuristic : heuristicFunctions) { String heuristicS = heuristic.toString(); double[][] heuristicFront = metricsUtil.readFront("experiment/" + algorithm + "/" + objectives + "objectives/" + heuristicS + "/" + problem + "/FUN.txt"); igdMap.put(algorithm + "-" + heuristicS, igd .invertedGenerationalDistance(heuristicFront, trueFrontMatrix, objectives)); gdMap.put(algorithm + "-" + heuristicS, gd.generationalDistance(heuristicFront, trueFrontMatrix, objectives)); spreadMap.put(algorithm + "-" + heuristicS, spread.spread(heuristicFront, trueFrontMatrix, objectives)); coverageMap.put(algorithm + "-" + heuristicS, coverage.coverage(heuristicFront, trueFrontMatrix)); } } latexTableBuilder.append("\t\t").append(problem); String latexTable = latexTableBuilder.toString(); latexTableBuilder = new StringBuilder(); latexTable = latexTable.replaceAll("O[OA]\\_", "").replaceAll("ChoiceFunction", "CF") .replaceAll("MultiArmedBandit", "MAB"); IGDWriter.write(latexTable.replaceAll("INDICATOR", "IGD")); spreadWriter.write(latexTable.replaceAll("INDICATOR", "Spread")); GDWriter.write(latexTable.replaceAll("INDICATOR", "GD")); coverageWriter.write(latexTable.replaceAll("INDICATOR", "Coverage")); String bestHeuristicIGD = "NULL"; String bestHeuristicGD = "NULL"; String bestHeuristicSpread = "NULL"; String bestHeuristicCoverage = "NULL"; getBest: { double bestMeanIGD = Double.POSITIVE_INFINITY; double bestMeanGD = Double.POSITIVE_INFINITY; double bestMeanSpread = Double.NEGATIVE_INFINITY; double bestMeanCoverage = Double.NEGATIVE_INFINITY; for (String heuristic : igdMap.keySet()) { double heuristicIGD = igdMap.get(heuristic); double heuristicGD = gdMap.get(heuristic); double heuristicSpread = spreadMap.get(heuristic); double heuristicCoverage = coverageMap.get(heuristic); if (heuristicIGD < bestMeanIGD) { bestMeanIGD = heuristicIGD; bestHeuristicIGD = heuristic; } if (heuristicGD < bestMeanGD) { bestMeanGD = heuristicGD; bestHeuristicGD = heuristic; } if (heuristicSpread > bestMeanSpread) { bestMeanSpread = heuristicSpread; bestHeuristicSpread = heuristic; } if (heuristicCoverage > bestMeanCoverage) { bestMeanCoverage = heuristicCoverage; bestHeuristicCoverage = heuristic; } } } StringBuilder igdBuilder = new StringBuilder(); StringBuilder gdBuilder = new StringBuilder(); StringBuilder spreadBuilder = new StringBuilder(); StringBuilder coverageBuilder = new StringBuilder(); String[] newHeuristicFunctions = new String[heuristicFunctions.length * algorithms.length + algorithms.length]; fulfillNewHeuristics: { int i = 0; for (String algorithm : algorithms) { newHeuristicFunctions[i++] = algorithm; for (HeuristicFunctionType heuristicFunction : heuristicFunctions) { newHeuristicFunctions[i++] = algorithm + "-" + heuristicFunction.toString(); } } } for (String heuristic : newHeuristicFunctions) { igdBuilder.append(" & "); boolean bold = heuristic.equals(bestHeuristicIGD) || igdMap.get(heuristic).equals(igdMap.get(bestHeuristicIGD)); if (bold) { igdBuilder.append("\\textbf{"); } igdBuilder.append(decimalFormatter.format(igdMap.get(heuristic))); if (bold) { igdBuilder.append("}"); } gdBuilder.append(" & "); bold = heuristic.equals(bestHeuristicGD) || gdMap.get(heuristic).equals(gdMap.get(bestHeuristicGD)); if (bold) { gdBuilder.append("\\textbf{"); } gdBuilder.append(decimalFormatter.format(gdMap.get(heuristic))); if (bold) { gdBuilder.append("}"); } spreadBuilder.append(" & "); bold = heuristic.equals(bestHeuristicSpread) || spreadMap.get(heuristic).equals(spreadMap.get(bestHeuristicSpread)); if (bold) { spreadBuilder.append("\\textbf{"); } spreadBuilder.append(decimalFormatter.format(spreadMap.get(heuristic))); if (bold) { spreadBuilder.append("}"); } coverageBuilder.append(" & "); bold = heuristic.equals(bestHeuristicCoverage) || coverageMap.get(heuristic).equals(coverageMap.get(bestHeuristicCoverage)); if (bold) { coverageBuilder.append("\\textbf{"); } coverageBuilder.append(decimalFormatter.format(coverageMap.get(heuristic))); if (bold) { coverageBuilder.append("}"); } } IGDWriter.write(igdBuilder + "\\\\\n"); spreadWriter.write(spreadBuilder + "\\\\\n"); GDWriter.write(gdBuilder + "\\\\\n"); coverageWriter.write(coverageBuilder + "\\\\\n"); } latexTableBuilder = new StringBuilder(); latexTableBuilder.append("\t\t\\bottomrule\n").append("\t\\end{tabulary}\n") .append("\\end{table}\n\n"); } averages: { latexTableBuilder.append("\\begin{table}[!htb]\n").append("\t\\centering\n") .append("\t\\def\\arraystretch{1.5}\n") // .append("\t\\setlength{\\tabcolsep}{10pt}\n") // .append("\t\\fontsize{8pt}{10pt}\\selectfont\n") .append("\t\\caption{INDICATOR averages found for ").append(objectives) .append(" objectives}\n").append("\t\\label{tab:INDICATOR ").append(objectives) .append(" objectives}\n").append("\t\\begin{tabulary}{\\linewidth}{c"); for (String algorithm : algorithms) { latexTableBuilder.append("c"); for (HeuristicFunctionType heuristicFunction : heuristicFunctions) { latexTableBuilder.append("c"); } } latexTableBuilder.append("}\n").append("\t\t\\toprule\n").append("\t\t\\textbf{System}"); for (String algorithm : algorithms) { latexTableBuilder.append(" & \\textbf{").append(algorithm).append("}"); for (HeuristicFunctionType heuristicFunction : heuristicFunctions) { latexTableBuilder.append(" & \\textbf{").append(algorithm).append("-") .append(heuristicFunction.toString()).append("}"); } } latexTableBuilder.append("\\\\\n").append("\t\t\\midrule\n"); for (String problem : problems) { NonDominatedSolutionList trueFront = new NonDominatedSolutionList(); pfTrueComposing: { for (String algorithm : algorithms) { SolutionSet mecbaFront = metricsUtil.readNonDominatedSolutionSet( "resultado/" + algorithm.toLowerCase().replaceAll("-", "") + "/" + problem + "_Comb_" + objectives + "obj/All_FUN_" + algorithm.toLowerCase().replaceAll("-", "") + "-" + problem); trueFront.addAll(mecbaFront); for (HeuristicFunctionType hyperHeuristic : heuristicFunctions) { SolutionSet front = metricsUtil.readNonDominatedSolutionSet( "experiment/" + algorithm + "/" + objectives + "objectives/" + hyperHeuristic.toString() + "/" + problem + "/FUN.txt"); trueFront.addAll(front); } } } double[][] trueFrontMatrix = trueFront.writeObjectivesToMatrix(); HashMap<String, double[]> igdMap = new HashMap<>(); HashMap<String, double[]> gdMap = new HashMap<>(); HashMap<String, double[]> spreadMap = new HashMap<>(); HashMap<String, double[]> coverageMap = new HashMap<>(); mocaito: { for (String algorithm : algorithms) { double[] mecbaIGDs = new double[EXECUTIONS]; double[] mecbaGDs = new double[EXECUTIONS]; double[] mecbaSpreads = new double[EXECUTIONS]; double[] mecbaCoverages = new double[EXECUTIONS]; for (int i = 0; i < EXECUTIONS; i++) { double[][] mecbaFront = metricsUtil.readFront("resultado/" + algorithm.toLowerCase().replaceAll("-", "") + "/" + problem + "_Comb_" + objectives + "obj/FUN_" + algorithm.toLowerCase().replaceAll("-", "") + "-" + problem + "-" + i + ".NaoDominadas"); mecbaIGDs[i] = igd.invertedGenerationalDistance(mecbaFront, trueFrontMatrix, objectives); mecbaGDs[i] = gd.generationalDistance(mecbaFront, trueFrontMatrix, objectives); mecbaSpreads[i] = spread.spread(mecbaFront, trueFrontMatrix, objectives); mecbaCoverages[i] = coverage.coverage(mecbaFront, trueFrontMatrix); } igdMap.put(algorithm, mecbaIGDs); gdMap.put(algorithm, mecbaGDs); spreadMap.put(algorithm, mecbaSpreads); coverageMap.put(algorithm, mecbaCoverages); } } for (String algorithm : algorithms) { for (HeuristicFunctionType heuristic : heuristicFunctions) { String heuristicS = heuristic.toString(); double[] hhIGDs = new double[EXECUTIONS]; double[] hhGDs = new double[EXECUTIONS]; double[] hhSpreads = new double[EXECUTIONS]; double[] hhCoverages = new double[EXECUTIONS]; for (int i = 0; i < EXECUTIONS; i++) { double[][] hhFront = metricsUtil .readFront("experiment/" + algorithm + "/" + objectives + "objectives/" + heuristicS + "/" + problem + "/EXECUTION_" + i + "/FUN.txt"); hhIGDs[i] = igd.invertedGenerationalDistance(hhFront, trueFrontMatrix, objectives); hhGDs[i] = gd.generationalDistance(hhFront, trueFrontMatrix, objectives); hhSpreads[i] = spread.spread(hhFront, trueFrontMatrix, objectives); hhCoverages[i] = coverage.coverage(hhFront, trueFrontMatrix); } igdMap.put(algorithm + "-" + heuristicS, hhIGDs); gdMap.put(algorithm + "-" + heuristicS, hhGDs); spreadMap.put(algorithm + "-" + heuristicS, hhSpreads); coverageMap.put(algorithm + "-" + heuristicS, hhCoverages); } } HashMap<String, HashMap<String, Boolean>> igdResult = KruskalWallisTest.test(igdMap); HashMap<String, HashMap<String, Boolean>> gdResult = KruskalWallisTest.test(gdMap); HashMap<String, HashMap<String, Boolean>> spreadResult = KruskalWallisTest.test(spreadMap); HashMap<String, HashMap<String, Boolean>> coverageResult = KruskalWallisTest .test(coverageMap); latexTableBuilder.append("\t\t").append(problem); String latexTable = latexTableBuilder.toString(); latexTable = latexTable.replaceAll("O[OA]\\_", "").replaceAll("ChoiceFunction", "CF") .replaceAll("MultiArmedBandit", "MAB"); IGDWriter.write(latexTable.replaceAll("INDICATOR", "IGD")); spreadWriter.write(latexTable.replaceAll("INDICATOR", "Spread")); GDWriter.write(latexTable.replaceAll("INDICATOR", "GD")); coverageWriter.write(latexTable.replaceAll("INDICATOR", "Coverage")); latexTableBuilder = new StringBuilder(); String bestHeuristicIGD = "NULL"; String bestHeuristicGD = "NULL"; String bestHeuristicSpread = "NULL"; String bestHeuristicCoverage = "NULL"; getBest: { double bestMeanIGD = Double.POSITIVE_INFINITY; double bestMeanGD = Double.POSITIVE_INFINITY; double bestMeanSpread = Double.NEGATIVE_INFINITY; double bestMeanCoverage = Double.NEGATIVE_INFINITY; for (String heuristic : igdMap.keySet()) { double heuristicMeanIGD = mean.evaluate(igdMap.get(heuristic)); double heuristicMeanGD = mean.evaluate(gdMap.get(heuristic)); double heuristicMeanSpread = mean.evaluate(spreadMap.get(heuristic)); double heuristicMeanCoverage = mean.evaluate(coverageMap.get(heuristic)); if (heuristicMeanIGD < bestMeanIGD) { bestMeanIGD = heuristicMeanIGD; bestHeuristicIGD = heuristic; } if (heuristicMeanGD < bestMeanGD) { bestMeanGD = heuristicMeanGD; bestHeuristicGD = heuristic; } if (heuristicMeanSpread > bestMeanSpread) { bestMeanSpread = heuristicMeanSpread; bestHeuristicSpread = heuristic; } if (heuristicMeanCoverage > bestMeanCoverage) { bestMeanCoverage = heuristicMeanCoverage; bestHeuristicCoverage = heuristic; } } } StringBuilder igdBuilder = new StringBuilder(); StringBuilder gdBuilder = new StringBuilder(); StringBuilder spreadBuilder = new StringBuilder(); StringBuilder coverageBuilder = new StringBuilder(); String[] newHeuristicFunctions = new String[heuristicFunctions.length * algorithms.length + algorithms.length]; fulfillNewHeuristics: { int i = 0; for (String algorithm : algorithms) { newHeuristicFunctions[i++] = algorithm; for (HeuristicFunctionType heuristicFunction : heuristicFunctions) { newHeuristicFunctions[i++] = algorithm + "-" + heuristicFunction.toString(); } } } for (String heuristic : newHeuristicFunctions) { igdBuilder.append(" & "); boolean bold = heuristic.equals(bestHeuristicIGD) || !igdResult.get(heuristic).get(bestHeuristicIGD); if (bold) { igdBuilder.append("\\textbf{"); } igdBuilder.append(decimalFormatter.format(mean.evaluate(igdMap.get(heuristic))) + " (" + decimalFormatter.format(standardDeviation.evaluate(igdMap.get(heuristic))) + ")"); if (bold) { igdBuilder.append("}"); } gdBuilder.append(" & "); bold = heuristic.equals(bestHeuristicGD) || !gdResult.get(heuristic).get(bestHeuristicGD); if (bold) { gdBuilder.append("\\textbf{"); } gdBuilder.append(decimalFormatter.format(mean.evaluate(gdMap.get(heuristic))) + " (" + decimalFormatter.format(standardDeviation.evaluate(gdMap.get(heuristic))) + ")"); if (bold) { gdBuilder.append("}"); } spreadBuilder.append(" & "); bold = heuristic.equals(bestHeuristicSpread) || !spreadResult.get(heuristic).get(bestHeuristicSpread); if (bold) { spreadBuilder.append("\\textbf{"); } spreadBuilder.append(decimalFormatter.format(mean.evaluate(spreadMap.get(heuristic))) + " (" + decimalFormatter.format(standardDeviation.evaluate(spreadMap.get(heuristic))) + ")"); if (bold) { spreadBuilder.append("}"); } coverageBuilder.append(" & "); bold = heuristic.equals(bestHeuristicCoverage) || !coverageResult.get(heuristic).get(bestHeuristicCoverage); if (bold) { coverageBuilder.append("\\textbf{"); } coverageBuilder .append(decimalFormatter.format(mean.evaluate(coverageMap.get(heuristic)))) .append(" (") .append(decimalFormatter .format(standardDeviation.evaluate(coverageMap.get(heuristic)))) .append(")"); if (bold) { coverageBuilder.append("}"); } } IGDWriter.write(igdBuilder + "\\\\\n"); spreadWriter.write(spreadBuilder + "\\\\\n"); GDWriter.write(gdBuilder + "\\\\\n"); coverageWriter.write(coverageBuilder + "\\\\\n"); } latexTableBuilder.append("\t\t\\bottomrule\n").append("\t\\end{tabulary}\n") .append("\\end{table}\n\n"); } latexTableBuilder.append("\\end{landscape}\n\n").append("\\end{document}"); String latexTable = latexTableBuilder.toString(); IGDWriter.write(latexTable); spreadWriter.write(latexTable); GDWriter.write(latexTable); coverageWriter.write(latexTable); } } }
From source file:util.ManageResults.java
/** * // www . j a va2 s . co m * @param fun * @return return the average */ private static double getAverage(List<Double> fun) { double[] funArray = new double[fun.size()]; for (int i = 0; i < funArray.length; i++) { funArray[i] = fun.get(i); } Mean mean = new Mean(); return mean.evaluate(funArray); }
From source file:util.ResultsUtil.java
public static double getAverage(List<Double> fun) { double[] funArray = new double[fun.size()]; for (int i = 0; i < funArray.length; i++) { funArray[i] = fun.get(i); }/*w w w . j a va2 s. com*/ Mean mean = new Mean(); return mean.evaluate(funArray); }
From source file:utils.ZTransform.java
private void createZTransform(ArrayList<CMatrix> selectedMatrices) { if (selectedMatrices.isEmpty()) { Messenger.showWarningMessage("Please select datasets to continue.", "Empty selection"); return;/*from ww w. java 2 s . c om*/ } for (CMatrix matrix : selectedMatrices) { if (!(matrix instanceof DoubleCMatrix)) { Messenger.showWarningMessage( "Dataset '" + matrix + "' is not a numeric matrix.\nOperation aborted.", "Data type error."); return; } } //selected matrices for (CMatrix matrix : selectedMatrices) { ArrayList<Double> values = new ArrayList<>(); DoubleCMatrix newMatrix = new DoubleCMatrix(matrix.getName() + " z transformed", matrix.getNumRows(), matrix.getNumColumns()); // for (int i = 0; i < matrix.getNumRows(); i++) { for (int j = 0; j < matrix.getNumColumns(); j++) { try { Double value = (Double) matrix.getValue(i, j); if (value == null || value.isInfinite() || value.isNaN()) { continue; } else { values.add(value); } } catch (Exception e) { } } //loop of num columns } //loop of num rows double[] v = new double[values.size()]; for (int i = 0; i < v.length; i++) { v[i] = values.get(i); } Mean mean = new Mean(); StandardDeviation sd = new StandardDeviation(); double mu = mean.evaluate(v); double sigma = sd.evaluate(v); // System.out.println("Mean: " + mu + " sd:" + sigma); for (int i = 0; i < matrix.getNumRows(); i++) { for (int j = 0; j < matrix.getNumColumns(); j++) { Double value = (Double) matrix.getValue(i, j); if (value == null || value.isInfinite() || value.isNaN()) { continue; } else { newMatrix.setValue(i, j, (value - mu) / sigma); } } } // for (int i = 0; i < matrix.getNumRows(); i++) { newMatrix.setRowLabel(i, matrix.getRowLabel(i)); } // for (int i = 0; i < matrix.getNumColumns(); i++) { newMatrix.setColLabel(i, matrix.getColLabel(i)); } CoolMapMaster.addNewBaseMatrix(newMatrix); } //end of matrices }