Examples of org.apache.mahout.classifier.bayes.common.BayesFeatureMapper.map()

org.apache.mahout.classifier.bayes.common.BayesFeatureMapper.map()

We need to count the number of times we've seen a term with a given label and we need to output that. But this Mapper does more than just outputing the count. It first does weight normalisation. Secondly, it outputs for each unique word in a document value 1 for summing up as the Term Document Frequency. Which later is used to calculate the Idf Thirdly, it outputs for each label the number of times a document was seen(Also used in Idf Calculation) @param key The label @param value the features (all unique) associated w/ this label @param output The OutputCollector to write the results to @param reporter Not used @throws IOException

    DefaultStringifier<Integer> intStringifier = new DefaultStringifier<Integer>(conf, Integer.class);
    conf.set("bayes.gramSize", intStringifier.toString(3));
    mapper.configure(conf);


    DummyOutputCollector<Text, DoubleWritable> output = new DummyOutputCollector<Text, DoubleWritable>();
    mapper.map(new Text("foo"), new Text("big brown shoe"), output, null);
    Map<String, List<DoubleWritable>> outMap = output.getData();
    System.out.println("Map: " + outMap);
    assertNotNull("outMap is null and it shouldn't be", outMap);
    //TODO: How about not such a lame test here?
    for (Map.Entry<String, List<DoubleWritable>> entry : outMap.entrySet()) {

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