Package org.apache.mahout.math

Examples of org.apache.mahout.math.Vector.maxValueIndex()

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      Pair<Text, VectorWritable> next = iter.next();
      String ng = next.getFirst().toString();

      int actual = asfDictionary.intern(ng);
      Vector result = classifier.classifyFull(next.getSecond().get());
      int cat = result.maxValueIndex();
      double score = result.maxValue();
      double ll = classifier.logLikelihood(actual, next.getSecond().get());
      ClassifierResult cr = new ClassifierResult(asfDictionary.values().get(cat), score, ll);
      ra.addInstance(asfDictionary.values().get(actual), cr);
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    assertEquals(0, t);
    // should have 9 values set
    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(3.1, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(8.0, v.norm(1), 0);
    assertEquals(1.0, v.maxValue(), 0);

    v.assign(0);
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    // should have 9 values set
    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(5.3, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(10.339850002884626, v.norm(1), 1.0e-6);
    assertEquals(1.5849625007211563, v.maxValue(), 1.0e-6);

    v.assign(0);
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    // should have 9 values set
    assertEquals(9.0, v.norm(0), 0);
    // all should be = 1 except for the 3.1
    assertEquals(5.3, v.maxValue(), 0);
    v.set(v.maxValueIndex(), 0);
    assertEquals(8.0, v.norm(0), 0);
    assertEquals(10.339850002884626, v.norm(1), 1.0e-6);
    assertEquals(1.5849625007211563, v.maxValue(), 1.0e-6);
  }
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      if (k == trackingKey % models.size()) {
        Vector v = model.classifyFull(instance);
        double score = Math.max(v.get(actual), MIN_SCORE);
        logLikelihood += (Math.log(score) - logLikelihood) / Math.min(record, windowSize);

        int correct = v.maxValueIndex() == actual ? 1 : 0;
        percentCorrect += (correct - percentCorrect) / Math.min(record, windowSize);
        if (numCategories() == 2) {
          auc.addSample(actual, groupKey, v.get(1));
        }
      } else {
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      List<WeightedPropertyVectorWritable> wpvList = collector.getValue(k);
      assertTrue("empty cluster!", !wpvList.isEmpty());
      if (wpvList.get(0).getVector().get(0) <= 2.0) {
        for (WeightedPropertyVectorWritable wv : wpvList) {
          Vector v = wv.getVector();
          int idx = v.maxValueIndex();
          assertTrue("bad cluster!", v.get(idx) <= 2.0);
        }
        assertEquals("Wrong size cluster", 4, wpvList.size());
      } else {
        for (WeightedPropertyVectorWritable wv : wpvList) {
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        }
      }
      Vector pdfPerCluster = clusterClassifier.classify(vector);
      if (shouldClassify(pdfPerCluster)) {
        if (emitMostLikely) {
          int maxValueIndex = pdfPerCluster.maxValueIndex();
          write(new VectorWritable(vector), context, maxValueIndex, 1.0);
        } else {
          writeAllAboveThreshold(new VectorWritable(vector), context, pdfPerCluster);
        }
      }
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      throws IOException, InterruptedException {
    if (!clusterModels.isEmpty()) {
      Vector pdfPerCluster = clusterClassifier.classify(vw.get());
      if (shouldClassify(pdfPerCluster)) {
        if (emitMostLikely) {
          int maxValueIndex = pdfPerCluster.maxValueIndex();
          write(vw, context, maxValueIndex);
        } else {
          writeAllAboveThreshold(vw, context, pdfPerCluster);
        }
      }
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  @Override
  public Vector classify(Vector instance) {
    Vector result = classifyNoLink(instance);
    // Find the max value's index.
    int max = result.maxValueIndex();
    result.assign(0);
    result.setQuick(max, 1.0);
    return result.viewPart(1, result.size() - 1);
  }
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      List<WeightedVectorWritable> wvList = collector.getValue(k);
      assertTrue("empty cluster!", wvList.size() != 0);
      if (wvList.get(0).getVector().get(0) <= 2.0) {
        for (WeightedVectorWritable wv : wvList) {
          Vector v = wv.getVector();
          int idx = v.maxValueIndex();
          assertTrue("bad cluster!", v.get(idx) <= 2.0);
        }
        assertEquals("Wrong size cluster", 4, wvList.size());
        gotLowClust= true;
      } else {
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