/** * * @param correctLabel * The correct label * @param classifiedResult * The classified result * @return whether the instance was correct or not */ public boolean addInstance(String correctLabel, ClassifierResult classifiedResult) { boolean result = correctLabel.equals(classifiedResult.getLabel()); if (result) { correctlyClassified++; } else { incorrectlyClassified++; } confusionMatrix.addInstance(correctLabel, classifiedResult); if (classifiedResult.getLogLikelihood() != Double.MAX_VALUE) { summarizer.add(classifiedResult.getLogLikelihood()); hasLL = true; } return result; }
final ClassifierResult UNKNOWN = new ClassifierResult("unknown", 1.0); UNKNOWN.getLabel());
public void addInstance(String correctLabel, ClassifierResult classifiedResult) { samples++; incrementCount(correctLabel, classifiedResult.getLabel()); }
= new ClassifierResult(label, score); resultAnalyzer.addInstance(parts[0], result);
public void addInstance(String correctLabel, ClassifierResult classifiedResult) { samples++; incrementCount(correctLabel, classifiedResult.getLabel()); }
private static void analyzeResults(Map<Integer, String> labelMap, SequenceFileDirIterable<Text, VectorWritable> dirIterable, ResultAnalyzer analyzer) { for (Pair<Text, VectorWritable> pair : dirIterable) { int bestIdx = Integer.MIN_VALUE; double bestScore = Long.MIN_VALUE; for (Vector.Element element : pair.getSecond().get().all()) { if (element.get() > bestScore) { bestScore = element.get(); bestIdx = element.index(); } } if (bestIdx != Integer.MIN_VALUE) { ClassifierResult classifierResult = new ClassifierResult(labelMap.get(bestIdx), bestScore); analyzer.addInstance(pair.getFirst().toString(), classifierResult); } } } }
/** * * @param correctLabel * The correct label * @param classifiedResult * The classified result * @return whether the instance was correct or not */ public boolean addInstance(String correctLabel, ClassifierResult classifiedResult) { boolean result = correctLabel.equals(classifiedResult.getLabel()); if (result) { correctlyClassified++; } else { incorrectlyClassified++; } confusionMatrix.addInstance(correctLabel, classifiedResult); if (classifiedResult.getLogLikelihood() != Double.MAX_VALUE) { summarizer.add(classifiedResult.getLogLikelihood()); hasLL = true; } return result; }
public void addInstance(String correctLabel, ClassifierResult classifiedResult) { samples++; incrementCount(correctLabel, classifiedResult.getLabel()); }
private static void analyzeResults(Map<Integer, String> labelMap, SequenceFileDirIterable<Text, VectorWritable> dirIterable, ResultAnalyzer analyzer) { for (Pair<Text, VectorWritable> pair : dirIterable) { int bestIdx = Integer.MIN_VALUE; double bestScore = Long.MIN_VALUE; for (Vector.Element element : pair.getSecond().get().all()) { if (element.get() > bestScore) { bestScore = element.get(); bestIdx = element.index(); } } if (bestIdx != Integer.MIN_VALUE) { ClassifierResult classifierResult = new ClassifierResult(labelMap.get(bestIdx), bestScore); analyzer.addInstance(pair.getFirst().toString(), classifierResult); } } } }
/** * * @param correctLabel * The correct label * @param classifiedResult * The classified result * @return whether the instance was correct or not */ public boolean addInstance(String correctLabel, ClassifierResult classifiedResult) { boolean result = correctLabel.equals(classifiedResult.getLabel()); if (result) { correctlyClassified++; } else { incorrectlyClassified++; } confusionMatrix.addInstance(correctLabel, classifiedResult); if (classifiedResult.getLogLikelihood() != Double.MAX_VALUE) { summarizer.add(classifiedResult.getLogLikelihood()); hasLL = true; } return result; }
public void classifyDocument(SolrInputDocument doc) throws IOException { try { //<start id="mahout.bayes.classify"/> SolrInputField field = doc.getField(inputField); String[] tokens = tokenizeField(inputField, field); ClassifierResult result = ctx.classifyDocument(tokens, defaultCategory); if (result != null && result.getLabel() != NO_LABEL) { doc.addField(outputField, result.getLabel()); } //<end id="mahout.bayes.classify"/> } catch (InvalidDatastoreException e) { throw new IOException("Invalid Classifier Datastore", e); } }
private static void analyzeResults(Map<Integer, String> labelMap, SequenceFileDirIterable<Text, VectorWritable> dirIterable, ResultAnalyzer analyzer) { for (Pair<Text, VectorWritable> pair : dirIterable) { int bestIdx = Integer.MIN_VALUE; double bestScore = Long.MIN_VALUE; for (Vector.Element element : pair.getSecond().get().all()) { if (element.get() > bestScore) { bestScore = element.get(); bestIdx = element.index(); } } if (bestIdx != Integer.MIN_VALUE) { ClassifierResult classifierResult = new ClassifierResult(labelMap.get(bestIdx), bestScore); analyzer.addInstance(pair.getFirst().toString(), classifierResult); } } } }