List of usage examples for java.lang Double compare
public static int compare(double d1, double d2)
From source file:org.projectbuendia.client.models.ObsValue.java
/** * Compares ObsValue instances according to a total ordering such that: * - All non-null values are greater than null. * - The lowest value is the "false" Boolean value (encoded as the coded concept for "No"). * - Next are all coded values, ordered from least severe to most severe (if they can * be interpreted as having a severity); or from first to last (if they can * be interpreted as having a typical temporal sequence). * - Next is the "true" Boolean value (encoded as the coded concept for "Yes"). * - Next are all numeric values, ordered from least to greatest. * - Next are all text values, ordered lexicographically from A to Z. * - Next are all date values, ordered from least to greatest. * - Next are all instant values, ordered from least to greatest. * @param other The other Value to compare to. * @return/*from w w w. j a v a2 s.com*/ */ @Override public int compareTo(@Nullable ObsValue other) { if (other == null) return 1; int result = 0; result = Integer.compare(getTypeOrdering(), other.getTypeOrdering()); if (result != 0) return result; if (uuid != null) { result = Integer.compare(getUuidOrdering(), other.getUuidOrdering()); if (result != 0) return result; result = uuid.compareTo(other.uuid); } else if (number != null) { result = Double.compare(number, other.number); } else if (text != null) { result = text.compareTo(other.text); } else if (date != null) { result = date.compareTo(other.date); } else if (instant != null) { result = instant.compareTo(other.instant); } return result; }
From source file:org.commoncrawl.service.parser.client.ParserNode.java
@Override public int compareTo(ParserNode o) { if (_status != null && o._status != null) { int result = Double.compare(Math.floor(_status.getLoad()), Math.floor(o._status.getLoad())); if (result == 0) { result = (_status.getQueuedDocs() < o._status.getQueuedDocs()) ? -1 : (_status.getQueuedDocs() > o._status.getQueuedDocs()) ? 1 : 0; if (result == 0) { result = (_status.getActiveDocs() < o._status.getActiveDocs()) ? -1 : (_status.getActiveDocs() > o._status.getActiveDocs()) ? 1 : 0; if (result == 0) { result = (_lastTouched < o._lastTouched) ? -1 : (_lastTouched > o._lastTouched) ? 1 : 0; }//www. j a va 2 s .co m } } return result; } else if (_status != null && o._status == null) { return -1; } else if (_status == null && o._status != null) { } else { return (_lastTouched < o._lastTouched) ? -1 : (_lastTouched > o._lastTouched) ? 1 : 0; } return 0; }
From source file:com.act.biointerpretation.sarinference.ProductScorer.java
/** * Reads in scored SARs, checks them against a prediction corpus and positive inchi list to get a product ranking. * This method is static because it does not rely on any properties of the enclosing class to construct the job. * TODO: It would probably make more sense to make this its own class, i.e. <ProductScorer implements JavaRunnable> * TODO: improve the data structure used to store scored products- using an L2PredictionCorpus is pretty ugly * * @param predictionCorpus The prediction corpus to score. * @param scoredSars The scored SARs to use. * @param lcmsFile The set of positive LCMS inchis, to use in scoring. * @return A JavaRunnable to run the product scoring. *//*ww w . j a v a 2 s .c o m*/ public static JavaRunnable getProductScorer(File predictionCorpus, File scoredSars, File lcmsFile, File outputFile) { return new JavaRunnable() { @Override public void run() throws IOException { // Verify files FileChecker.verifyInputFile(predictionCorpus); FileChecker.verifyInputFile(scoredSars); FileChecker.verifyInputFile(lcmsFile); FileChecker.verifyAndCreateOutputFile(outputFile); // Build SAR node list and best sar finder SarTreeNodeList nodeList = new SarTreeNodeList(); nodeList.loadFromFile(scoredSars); BestSarFinder sarFinder = new BestSarFinder(nodeList); // Build prediction corpus L2PredictionCorpus predictions = L2PredictionCorpus.readPredictionsFromJsonFile(predictionCorpus); // Build LCMS results IonAnalysisInterchangeModel lcmsResults = new IonAnalysisInterchangeModel(); lcmsResults.loadResultsFromFile(lcmsFile); /** * Build map from predictions to their scores based on SAR * For each prediction, we add on auxiliary info about its SARs and score to its projector name. * TODO: build data structure to store a scored prediction, instead of hijacking the projector name. */ Map<L2Prediction, Double> predictionToScoreMap = new HashMap<>(); LOGGER.info("Scoring predictions."); for (L2Prediction prediction : predictions.getCorpus()) { String nameAppendage = lcmsResults.getLcmsDataForPrediction(prediction).toString(); // Always tack LCMS result onto name Optional<SarTreeNode> maybeBestSar = sarFinder.apply(prediction); if (maybeBestSar.isPresent()) { // If a SAR was matched, add info about it to the projector name, and put its score into the map SarTreeNode bestSar = maybeBestSar.get(); nameAppendage += ":" + bestSar.getHierarchyId() + ":" + bestSar.getRankingScore(); prediction.setProjectorName(prediction.getProjectorName() + nameAppendage); predictionToScoreMap.put(prediction, bestSar.getRankingScore()); } else { // If no SAR is found, append "NO_SAR" to the prediction, and give it a ranking score of 0 nameAppendage += "NO_SAR"; prediction.setProjectorName(prediction.getProjectorName() + nameAppendage); predictionToScoreMap.put(prediction, 0D); } } LOGGER.info("Sorting predictions in decreasing order of best associated SAR rank."); List<L2Prediction> predictionList = new ArrayList<>(predictionToScoreMap.keySet()); predictionList .sort((a, b) -> -Double.compare(predictionToScoreMap.get(a), predictionToScoreMap.get(b))); // Wrap results in a corpus and write to file. L2PredictionCorpus finalCorpus = new L2PredictionCorpus(predictionList); finalCorpus.writePredictionsToJsonFile(outputFile); LOGGER.info("Complete!."); } @Override public String toString() { return "ProductScorer:" + scoredSars.getName(); } }; }
From source file:org.deeplearning4j.util.StringGrid.java
public void sortColumnsByWordLikelihoodIncluded(final int column) { final Counter<String> counter = new Counter<>(); List<String> col = getColumn(column); for (String s : col) { StringTokenizer tokenizer = new StringTokenizer(s); while (tokenizer.hasMoreTokens()) { counter.incrementCount(tokenizer.nextToken(), 1.0); }/*from w ww. j a va 2s .co m*/ } if (counter.totalCount() <= 0.0) { log.warn("Unable to calculate probability; nothing found"); return; } //laplace smoothing counter.incrementAll(counter.keySet(), 1.0); Set<String> remove = new HashSet<>(); for (String key : counter.keySet()) if (key.length() < 2 || key.matches("[a-z]+")) remove.add(key); for (String key : remove) counter.removeKey(key); counter.pruneKeysBelowThreshold(4.0); final double totalCount = counter.totalCount(); Collections.sort(this, new Comparator<List<String>>() { @Override public int compare(List<String> o1, List<String> o2) { double c1 = sumOverTokens(counter, o1.get(column), totalCount); double c2 = sumOverTokens(counter, o2.get(column), totalCount); return Double.compare(c1, c2); } }); }
From source file:org.zanata.action.ProjectHomeAction.java
public String getCopyVersionCompletePercent(String projectSlug, String versionSlug) { CopyVersionTaskHandle handler = copyVersionManager.getCopyVersionProcessHandle(projectSlug, versionSlug); if (handler != null) { double completedPercent = (double) handler.getCurrentProgress() / (double) handler.getMaxProgress() * 100;//from w ww . j a v a 2 s . co m if (Double.compare(completedPercent, 100) == 0) { setMessage(msgs.format("jsf.copyVersion.Completed", versionSlug)); } return String.format("%1$,.2f", completedPercent); } else { return "0"; } }
From source file:eu.planets_project.pp.plato.evaluation.evaluators.FITSEvaluator.java
public HashMap<MeasurementInfoUri, Value> evaluate(Alternative alternative, SampleObject sample, DigitalObject result, List<MeasurementInfoUri> measurementInfoUris, IStatusListener listener) throws EvaluatorException { FloatFormatter formatter = new FloatFormatter(); HashMap<MeasurementInfoUri, Value> results = new HashMap<MeasurementInfoUri, Value>(); String fitsXMLResult = result.getFitsXMLString(); String fitsXMLSample = sample.getFitsXMLString(); XmlExtractor extractor = new XmlExtractor(); extractor.setNamespaceContext(new FitsNamespaceContext()); if ((fitsXMLResult != null) && (fitsXMLSample != null)) { // so we have a fits xml, lets analyse it: try {// ww w . j av a 2s.c o m StringReader reader = new StringReader(fitsXMLResult); Document fitsDocResult = extractor.getDocument(new InputSource(reader)); reader = new StringReader(fitsXMLSample); Document fitsDocSample = extractor.getDocument(new InputSource(reader)); String sampleImageCompressionScheme = extractor.extractText(fitsDocSample, "//fits:compressionScheme/text()"); String resultImageCompressionScheme = extractor.extractText(fitsDocResult, "//fits:compressionScheme/text()"); for (MeasurementInfoUri measurementInfoUri : measurementInfoUris) { Value v = null; String propertyURI = measurementInfoUri.getAsURI(); Scale scale = descriptor.getMeasurementScale(measurementInfoUri); if (scale == null) { // This means that I am not entitled to evaluate this measurementInfo and therefore supposed to skip it: continue; } if (OBJECT_FORMAT_CORRECT_WELLFORMED.equals(propertyURI)) { v = extractor.extractValue(fitsDocResult, scale, "//fits:well-formed[@status='SINGLE_RESULT']/text()", "//fits:filestatus/fits:message/text()"); } else if (OBJECT_FORMAT_CORRECT_VALID.equals(propertyURI)) { v = extractor.extractValue(fitsDocResult, scale, "//fits:filestatus/fits:valid[@status='SINGLE_RESULT']/text()", "//fits:filestatus/fits:message/text()"); } if (OBJECT_COMPRESSION_SCHEME.equals(propertyURI)) { v = extractor.extractValue(fitsDocResult, scale, "//fits:compressionScheme/text()", null); } if ((v != null) && (v.getComment() == null || "".equals(v.getComment()))) { v.setComment(SOURCE); results.put(measurementInfoUri, v); listener.updateStatus(String.format("%s: measurement: %s = %s", NAME, measurementInfoUri.getAsURI(), v.toString())); // this leaf has been processed continue; } if (OBJECT_FORMAT_CORRECT_CONFORMS.equals(propertyURI)) { if (alternative.getAction() != null) { String puid = "UNDEFINED"; FormatInfo info = alternative.getAction().getTargetFormatInfo(); if (info != null) { puid = info.getPuid(); } String fitsText = extractor.extractText(fitsDocResult, "//fits:externalIdentifier[@type='puid']/text()"); v = identicalValues(puid, fitsText, scale); } } else if ((OBJECT_IMAGE_DIMENSION_WIDTH + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:imageWidth/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:imageWidth/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_DIMENSION_HEIGHT + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:imageHeight/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:imageHeight/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_DIMENSION_ASPECTRATIO + "#equal").equals(propertyURI)) { try { int sampleHeight = Integer .parseInt(extractor.extractText(fitsDocSample, "//fits:imageHeight/text()")); int resultHeight = Integer .parseInt(extractor.extractText(fitsDocResult, "//fits:imageHeight/text()")); int sampleWidth = Integer .parseInt(extractor.extractText(fitsDocSample, "//fits:imageWidth/text()")); int resultWidth = Integer .parseInt(extractor.extractText(fitsDocResult, "//fits:imageWidth/text()")); double sampleRatio = ((double) sampleWidth) / sampleHeight; double resultRatio = ((double) resultWidth) / resultHeight; v = scale.createValue(); ((BooleanValue) v).bool(0 == Double.compare(sampleRatio, resultRatio)); v.setComment(String.format("Reference value: %s\nActual value: %s", formatter.formatFloat(sampleRatio), formatter.formatFloat(resultRatio))); } catch (NumberFormatException e) { // not all values are available - aspectRatio cannot be calculated v = scale.createValue(); v.setComment( "Image width and/or height are not available - aspectRatio cannot be calculated"); } } else if ((OBJECT_COMPRESSION_SCHEME + "#equal").equals(propertyURI)) { v = identicalValues(sampleImageCompressionScheme, resultImageCompressionScheme, scale); } else if (OBJECT_COMPRESSION_LOSSLESS.equals(propertyURI)) { // At the moment we only handle compression schemes of images if ((resultImageCompressionScheme != null) && (!"".equals(resultImageCompressionScheme))) { v = scale.createValue(); ((BooleanValue) v) .bool(FITS_COMPRESSIONSCHEME_UNCOMPRESSED.equals(resultImageCompressionScheme)); } } else if (OBJECT_COMPRESSION_LOSSY.equals(propertyURI)) { // At the moment we only handle compression schemes of images if ((resultImageCompressionScheme != null) && (!"".equals(resultImageCompressionScheme))) { v = scale.createValue(); ((BooleanValue) v).bool( !FITS_COMPRESSIONSCHEME_UNCOMPRESSED.equals(resultImageCompressionScheme)); } } else if ((OBJECT_IMAGE_COLORENCODING_BITSPERSAMPLE + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:bitsPerSample/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:bitsPerSample/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_COLORENCODING_SAMPLESPERPIXEL + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:samplesPerPixel/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:samplesPerPixel/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_PHOTOMETRICINTERPRETATION_COLORSPACE + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:colorSpace/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:colorSpace/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_PHOTOMETRICINTERPRETATION_COLORPROFILE_ICCPROFILE + "#equal") .equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:iccProfileName/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:iccProfileName/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_SPATIALMETRICS_SAMPLINGFREQUENCYUNIT + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:samplingFrequencyUnit/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:samplingFrequencyUnit/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_SPATIALMETRICS_XSAMPLINGFREQUENCY + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:xSamplingFrequency/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:xSamplingFrequency/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_SPATIALMETRICS_YSAMPLINGFREQUENCY + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:ySamplingFrequency/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:ySamplingFrequency/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_METADATA + "#equal").equals(propertyURI)) { // we use the equal metric. reserve PRESERVED metric for later and get it right. HashMap<String, String> sampleMetadata = extractor.extractValues(fitsDocSample, "//fits:exiftool/*[local-name() != 'rawdata']"); HashMap<String, String> resultMetadata = extractor.extractValues(fitsDocResult, "//fits:exiftool/*[local-name() != 'rawdata']"); v = preservedValues(sampleMetadata, resultMetadata, scale); } else if ((OBJECT_IMAGE_METADATA_PRODUCER + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:ImageCreation/ImageProducer/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:ImageCreation/ImageProducer/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_METADATA_SOFTWARE + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:creatingApplicationName/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:creatingApplicationName/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_METADATA_CREATIONDATE + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:ImageCreation/DateTimeCreated/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:ImageCreation/DateTimeCreated/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_METADATA_LASTMODIFIED + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:fileinfo/lastmodified/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:fileinfo/lastmodified/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_METADATA_DESCRIPTION + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:exiftool/ImageDescription/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:exiftool/ImageDescription/text()"); v = identicalValues(sampleValue, resultValue, scale); } else if ((OBJECT_IMAGE_METADATA_ORIENTATION + "#equal").equals(propertyURI)) { String sampleValue = extractor.extractText(fitsDocSample, "//fits:exiftool/Orientation/text()"); String resultValue = extractor.extractText(fitsDocResult, "//fits:exiftool/Orientation/text()"); v = identicalValues(sampleValue, resultValue, scale); } if (v != null) { v.setComment(v.getComment() + SOURCE); results.put(measurementInfoUri, v); listener.updateStatus(String.format("%s: evaluated measurement: %s = %s", NAME, measurementInfoUri.getAsURI(), v.toString())); } else { listener.updateStatus(String.format("%s: no evaluator found for measurement: %s", NAME, measurementInfoUri.getAsURI())); } } } catch (IOException e) { listener.updateStatus(" - could not read FITS xml"); } catch (SAXException e) { listener.updateStatus(" - invalid FITS xml found"); } catch (ParserConfigurationException e) { listener.updateStatus(" - invalid FITS xml found"); } } else { listener.updateStatus(" - no FITS xml found"); } return results; }
From source file:org.devathon.contest2016.npc.NPCController.java
public void updateEquipment() { for (ArmorCategory category : ArmorCategory.values()) { List<Pair<ItemStack, Double>> weighted = itemStacks.stream() .map(itemStack -> Pair.of(itemStack, getGenericDefense(category, itemStack.getType()))) .filter(pair -> pair.getRight() > 0).collect(Collectors.toList()); weighted.sort((a, b) -> -Double.compare(a.getRight(), b.getRight())); if (weighted.size() > 0) { category.applyTo(getBukkitEntity(), weighted.get(0).getLeft()); }/*from ww w . j a va2 s . co m*/ } List<Pair<ItemStack, Double>> weighted = itemStacks.stream() .map(itemStack -> Pair.of(itemStack, getGenericAttackDamage(itemStack.getType()))) .filter(pair -> pair.getRight() > 0).collect(Collectors.toList()); weighted.sort((a, b) -> -Double.compare(a.getRight(), b.getRight())); if (weighted.size() > 0) { getBukkitEntity().getEquipment().setItemInMainHand(weighted.get(0).getLeft()); } }
From source file:org.orekit.time.TimeComponents.java
/** {@inheritDoc} */ public int compareTo(final TimeComponents other) { return Double.compare(getSecondsInDay(), other.getSecondsInDay()); }
From source file:de.dfki.madm.anomalydetection.operator.statistical_based.RobustPCAOperator.java
@Override public void doWork() throws OperatorException { // check whether all attributes are numerical ExampleSet exampleSet = exampleSetInput.getData(ExampleSet.class); Tools.onlyNonMissingValues(exampleSet, "PCA"); Tools.onlyNumericalAttributes(exampleSet, "PCA"); // Get normal probability. double normProb = getParameterAsDouble(PARAMETER_OUTLIER_PROBABILITY); int olInst = exampleSet.size() - (int) Math.floor(exampleSet.size() * normProb); log("Ignoring " + olInst + " anomalyous instances for robustness."); // The robust estimate is based on removing top outliers first based on Mahalanobis distance (MD). // Since MD is the same as the outlier score when using all PCs, the PCA is done twice: // First with all examples, second with top-outliers removed (robust) // First PCA for outlier removal // create covariance matrix Matrix covarianceMatrix = CovarianceMatrix.getCovarianceMatrix(exampleSet); // EigenVector and EigenValues of the covariance matrix EigenvalueDecomposition eigenvalueDecomposition = covarianceMatrix.eig(); // create and deliver results double[] eigenvalues = eigenvalueDecomposition.getRealEigenvalues(); Matrix eigenvectorMatrix = eigenvalueDecomposition.getV(); double[][] eigenvectors = eigenvectorMatrix.getArray(); PCAModel model = new PCAModel(exampleSet, eigenvalues, eigenvectors); // Perform transformation ExampleSet res = model.apply((ExampleSet) exampleSet.clone()); // Compute simple list with MDs and sort according to MD. List<double[]> l = new LinkedList<double[]>(); double eIdx = 0; for (Example example : res) { double md = 0.0; int aNr = 0; for (Attribute attr : example.getAttributes()) { double pcscore = example.getValue(attr); md += (pcscore * pcscore) / model.getEigenvalue(aNr); aNr++;/*from w w w . jav a2s .c om*/ } double[] x = { md, eIdx }; l.add(x); eIdx++; } Collections.sort(l, new Comparator<double[]>() { public int compare(double[] first, double[] second) { return Double.compare(second[0], first[0]); } }); // Out of the list, create array with outlier-indexes and array (mapping) with good instances. Iterator<double[]> iter = l.iterator(); int[] olMapping = new int[olInst]; for (int i = 0; i < olInst; i++) { olMapping[i] = (int) ((double[]) iter.next())[1]; } Arrays.sort(olMapping); int[] mapping = new int[exampleSet.size() - olInst]; int olc = 0; int ctr = 0; for (int i = 0; i < exampleSet.size(); i++) { if (olc == olInst) { // Add last elements after last outlier mapping[ctr++] = i; continue; } if (olMapping[olc] != i) { mapping[ctr++] = i; } else { olc++; } } ExampleSet robustExampleSet = new MappedExampleSet(exampleSet, mapping); // creates a new example set without the top outliers. // --- // Second PCA (robust) covarianceMatrix = CovarianceMatrix.getCovarianceMatrix(robustExampleSet); eigenvalueDecomposition = covarianceMatrix.eig(); // create and deliver results eigenvalues = eigenvalueDecomposition.getRealEigenvalues(); eigenvectorMatrix = eigenvalueDecomposition.getV(); eigenvectors = eigenvectorMatrix.getArray(); // Apply on original set model = new PCAModel(exampleSet, eigenvalues, eigenvectors); // Perform transformation res = model.apply((ExampleSet) exampleSet.clone()); // Sort eigenvalues Arrays.sort(eigenvalues); ArrayUtils.reverse(eigenvalues); // if necessary reduce nbr of dimensions ... int reductionType = getParameterAsInt(PARAMETER_REDUCTION_TYPE); List<Integer> pcList = new ArrayList<Integer>(); if (reductionType == PCS_ALL) { for (int i = 0; i < exampleSet.getAttributes().size(); i++) { pcList.add(i); } } if (reductionType == PCS_TOP || reductionType == PCS_BOTH) { //top switch (getParameterAsInt(PARAMETER_TOP_METHODS)) { case PCS_TOP_FIX: for (int i = 0; i < getParameterAsInt(PARAMETER_NUMBER_OF_COMPONENTS_TOP); i++) { pcList.add(i); } break; case PCS_TOP_VAR: double var = getParameterAsDouble(PARAMETER_VARIANCE_THRESHOLD); boolean last = false; for (int i = 0; i < exampleSet.getAttributes().size(); i++) { if (model.getCumulativeVariance(i) < var) { pcList.add(i); } else if (!last) { // we need to add another PC to meet the minimum requirement. last = true; pcList.add(i); } } break; } } if (reductionType == PCS_LOWER || reductionType == PCS_BOTH) { //lower switch (getParameterAsInt(PARAMETER_LOW_METHODS)) { case PCS_LOW_FIX: for (int i = exampleSet.getAttributes().size() - getParameterAsInt(PARAMETER_NUMBER_OF_COMPONENTS_LOW); i < exampleSet.getAttributes() .size(); i++) { pcList.add(i); } break; case PCS_LOW_VAL: double val = getParameterAsDouble(PARAMETER_VALUE_THRESHOLD); for (int i = 0; i < eigenvalues.length; i++) { if (eigenvalues[i] <= val) { if (pcList.size() == 0) { pcList.add(i); } else if (pcList.get(pcList.size() - 1).intValue() < i) { pcList.add(i); } } } break; } } int[] opcs = ArrayUtils.toPrimitive(pcList.toArray(new Integer[pcList.size()])); if (opcs.length == 0) { throw new UserError(this, "Parameters thresholds are selected such that they did not match any principal component. Lower variance or increase eigenvalue threshold."); } if (opcs.length == exampleSet.getAttributes().size()) { log("Using all PCs for score."); } else { log("Using following PCs for score: " + Arrays.toString(opcs)); } // Normalize by Chi-Dist with d degrees of freedom double scoreNormalizer = 1.0; ChiSquaredDistributionImpl chi = new ChiSquaredDistributionImpl(opcs.length); try { scoreNormalizer = chi.inverseCumulativeProbability(normProb); } catch (MathException e) { System.err.println(e); } log("Normalizing score with chi cumulative propability: " + scoreNormalizer); // compute scores Attribute scoreAttr = AttributeFactory.createAttribute("outlier", Ontology.REAL); exampleSet.getExampleTable().addAttribute(scoreAttr); exampleSet.getAttributes().setOutlier(scoreAttr); for (int exNr = 0; exNr < exampleSet.size(); exNr++) { Example orig = exampleSet.getExample(exNr); Example pc = res.getExample(exNr); double oscore = 0.0; int aNr = 0; ctr = 0; for (Attribute attr : pc.getAttributes()) { if (ctr < opcs.length && opcs[ctr] != aNr) { // we skip this dimension aNr++; continue; } double pcscore = pc.getValue(attr); oscore += (pcscore * pcscore) / model.getEigenvalue(aNr); aNr++; ctr++; } orig.setValue(scoreAttr, oscore / scoreNormalizer); } exampleSetOutput.deliver(exampleSet); }
From source file:com.vsthost.rnd.commons.math.ext.linear.DMatrixUtils.java
/** * Get the order of the specified elements in descending or ascending order. * * @param values A vector of integer values. * @param indices The indices which will be considered for ordering. * @param descending Flag indicating if we go descending or not. * @return A vector of indices sorted in the provided order. *//* www . java2 s. c om*/ public static int[] getOrder(int[] values, int[] indices, boolean descending) { // Create an index series: Integer[] opIndices = ArrayUtils.toObject(indices); // Sort indices: Arrays.sort(opIndices, new Comparator<Integer>() { @Override public int compare(Integer o1, Integer o2) { if (descending) { return Double.compare(values[o2], values[o1]); } else { return Double.compare(values[o1], values[o2]); } } }); return ArrayUtils.toPrimitive(opIndices); }