Package org.apache.mahout.classifier.df.data

Examples of org.apache.mahout.classifier.df.data.Instance

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                                                                     InterruptedException {
      if (firstId == null) {
        firstId = new LongWritable(key.get());
      }
     
      Instance instance = converter.convert(value.toString());
     
      context.write(firstId, new IntWritable((int) dataset.getLabel(instance)));
    }
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  private boolean isIdentical(Data data) {
    if (data.isEmpty()) {
      return true;
    }

    Instance instance = data.get(0);
    for (int attr = 0; attr < selected.length; attr++) {
      if (selected[attr]) {
        continue;
      }

      for (int index = 1; index < data.size(); index++) {
        if (data.get(index).get(attr) != instance.get(attr)) {
          return false;
        }
      }
    }
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        first = false;
      }

      String line = value.toString();
      if (!line.isEmpty()) {
        Instance instance = converter.convert(line);
        double prediction = forest.classify(dataset, rng, instance);
        lkey.set(dataset.getLabel(instance));
        lvalue.set(Double.toString(prediction));
        context.write(lkey, lvalue);
      }
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      if (line.isEmpty()) {
        continue; // skip empty lines
      }
     
      // write the tuple in files[tuple.label]
      Instance instance = converter.convert(line);
      int label = (int) dataset.getLabel(instance);
      files[currents[label]].writeBytes(line);
      files[currents[label]].writeChar('\n');
     
      // update currents
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    Dataset dataset = data.getDataset();

    // compute frequencies
    for (int index = 0; index < data.size(); index++) {
      Instance instance = data.get(index);
      counts[ArrayUtils.indexOf(values, instance.get(attr))][(int) dataset.getLabel(instance)]++;
      countAll[(int) dataset.getLabel(instance)]++;
    }

    int size = data.size();
    double hy = entropy(countAll, size); // H(Y)
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  void computeFrequencies(Data data, int attr, double[] values) {
    Dataset dataset = data.getDataset();

    for (int index = 0; index < data.size(); index++) {
      Instance instance = data.get(index);
      counts[ArrayUtils.indexOf(values, instance.get(attr))][(int) dataset.getLabel(instance)]++;
      countAll[(int) dataset.getLabel(instance)]++;
    }
  }
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                                                                     InterruptedException {
      if (firstId == null) {
        firstId = new LongWritable(key.get());
      }
     
      Instance instance = converter.convert(value.toString());
     
      context.write(firstId, new IntWritable((int) dataset.getLabel(instance)));
    }
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  private boolean isIdentical(Data data) {
    if (data.isEmpty()) {
      return true;
    }

    Instance instance = data.get(0);
    for (int attr = 0; attr < selected.length; attr++) {
      if (selected[attr]) {
        continue;
      }

      for (int index = 1; index < data.size(); index++) {
        if (data.get(index).get(attr) != instance.get(attr)) {
          return false;
        }
      }
    }
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        first = false;
      }

      String line = value.toString();
      if (!line.isEmpty()) {
        Instance instance = converter.convert(line);
        double prediction = forest.classify(dataset, rng, instance);
        lkey.set(dataset.getLabel(instance));
        lvalue.set(Double.toString(prediction));
        context.write(lkey, lvalue);
      }
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  private boolean isIdentical(Data data) {
    if (data.isEmpty()) {
      return true;
    }

    Instance instance = data.get(0);
    for (int attr = 0; attr < selected.length; attr++) {
      if (selected[attr]) {
        continue;
      }

      for (int index = 1; index < data.size(); index++) {
        if (data.get(index).get(attr) != instance.get(attr)) {
          return false;
        }
      }
    }
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Related Classes of org.apache.mahout.classifier.df.data.Instance

  • org.apache.mahout.classifier.df.builder.DecisionTreeBuilder
  • org.apache.mahout.classifier.df.builder.DefaultTreeBuilder
  • org.apache.mahout.classifier.df.mapreduce.Classifier$CMapper
  • org.apache.mahout.classifier.df.mapreduce.TestForest
  • org.apache.mahout.classifier.df.split.OptIgSplit
  • org.apache.mahout.classifier.df.split.RegressionSplit
  • org.apache.mahout.classifier.df.tools.FrequenciesJob$FrequenciesMapper
  • org.apache.mahout.classifier.df.tools.UDistrib
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