import org.apache.mahout.cf.taste.impl.model.GenericPreference; //导入依赖的package包/类
@Test
public void testBuildKNNRecommender() {
    GenericRecommenderBuilder rb = new GenericRecommenderBuilder();
    FastByIDMap<PreferenceArray> userData = new FastByIDMap<PreferenceArray>();
    userData.put(1, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1, 1, 1),
            new GenericPreference(1, 2, 1), new GenericPreference(1, 3, 1))));
    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
            new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));
    DataModel dm = new GenericDataModel(userData);

    Recommender rec = null;
    String recommenderType = "org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender";
    String similarityType = "org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity";
    try {
        rec = rb.buildRecommender(dm, recommenderType, similarityType);
    } catch (RecommenderException e) {
        e.printStackTrace();
    }
    assertTrue(rec instanceof GenericUserBasedRecommender);
}
 

import org.apache.mahout.cf.taste.impl.model.GenericPreference; //导入依赖的package包/类
private FastByIDMap<Collection<Preference>> extractRawStudentPreferenceData(CloseableIterator<StudentSubjectPreference> iterator) {
    FastByIDMap<Collection<Preference>> rawData = new FastByIDMap<>();

    iterator.forEachRemaining(it -> {
        Collection<Preference> studentPreferences;
        if (rawData.containsKey(it.getStudentId())) {
            studentPreferences = rawData.get(it.getStudentId());
        } else {
            studentPreferences = new ArrayList<>();
            rawData.put(it.getStudentId(), studentPreferences);
        }

        studentPreferences.add(new GenericPreference(it.getStudentId(), it.getSubjectId(), it.getPreferenceValue().floatValue()));
    });

    return rawData;
}
 

import org.apache.mahout.cf.taste.impl.model.GenericPreference; //导入依赖的package包/类
/**
 * Constructs the wrapper using the provided model.
 *
 * @param model the model to be used to create the wrapped model
 */
public DataModelWrapper(final net.recommenders.rival.core.TemporalDataModelIF<Long, Long> model) {
    FastByIDMap<Collection<Preference>> data = new FastByIDMap<Collection<Preference>>();
    FastByIDMap<FastByIDMap<Long>> timestampData = new FastByIDMap<FastByIDMap<Long>>();
    for (Long u : model.getUsers()) {
        List<Preference> prefs = new ArrayList<Preference>();
        FastByIDMap<Long> userTimestamps = new FastByIDMap<Long>();
        timestampData.put(u, userTimestamps);
        for (Long i : model.getUserItems(u)) {
            Iterable<Long> timestamps = model.getUserItemTimestamps(u, i);
            long t = -1;
            if (timestamps != null) {
                for (Long tt : timestamps) {
                    t = tt;
                    break;
                }
            }
            userTimestamps.put(i, t);
            prefs.add(new GenericPreference(u, i, model.getUserItemPreference(u, i).floatValue()));
        }
        data.put(u, prefs);
    }

    FastByIDMap<PreferenceArray> userData = GenericDataModel.toDataMap(data, true);
    wrapper = new GenericDataModel(userData, timestampData);
}
 

import org.apache.mahout.cf.taste.impl.model.GenericPreference; //导入依赖的package包/类
@Test
public void testBuildDefaultRecommender() {

    RecommenderBuilder rb = new GenericRecommenderBuilder();
    FastByIDMap<PreferenceArray> userData = new FastByIDMap<PreferenceArray>();
    userData.put(1, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1, 1, 1),
            new GenericPreference(1, 2, 1), new GenericPreference(1, 3, 1))));
    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
            new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));
    DataModel dm = new GenericDataModel(userData);

    Recommender rec = null;
    try {
        rec = rb.buildRecommender(dm);
    } catch (TasteException e) {
        e.printStackTrace();
    }
    assertTrue(rec instanceof RandomRecommender);
}
 

import org.apache.mahout.cf.taste.impl.model.GenericPreference; //导入依赖的package包/类
public static DataModel getPrefDataByFileFast() throws Exception {		
	String data = EEG_INTEREST_LEVEL_DATASET_FILE;
	FastByIDMap<PreferenceArray> preferences = new FastByIDMap<PreferenceArray>();
	BufferedReader br = new BufferedReader(new FileReader(data));

	List<Preference> list = new ArrayList<Preference>();
	String line = br.readLine();
	long uid_init = Long.parseLong(line.split(",")[0]);
	while (line != null) {
		String[] arr = line.split(",");
		long uid = Long.parseLong(arr[0]);
		if (uid_init == uid) {
			list.add(new GenericPreference(uid, Long.parseLong(arr[1]),
					Float.parseFloat(arr[2])));
		} else {
			preferences.put(uid_init, new GenericUserPreferenceArray(list));
			list = new ArrayList<Preference>();
			list.add(new GenericPreference(uid, Long.parseLong(arr[1]),
					Float.parseFloat(arr[2])));
			uid_init = uid;
		}
		line = br.readLine();

		if (line == null) {
			preferences.put(uid_init, new GenericUserPreferenceArray(list));
		}
	}
	br.close();
	return new GenericDataModel(preferences);
}
 

import org.apache.mahout.cf.taste.impl.model.GenericPreference; //导入依赖的package包/类
@Override
public void addPreference(Long u, Long i, Double d) {
    if (model != null) {
        throw new IllegalArgumentException("DataModel already generated. It is not possible to add more information.");
    }
    Collection<Preference> prefs = null;
    if (!data.containsKey(u)) {
        prefs = new ArrayList<Preference>();
        data.put(u, prefs);
    } else {
        prefs = data.get(u);
    }
    prefs.add(new GenericPreference(u, i, d.floatValue()));
}
 

import org.apache.mahout.cf.taste.impl.model.GenericPreference; //导入依赖的package包/类
@Override
public Object mapRow(ResultSet rs, int rowNum) throws SQLException {
    return new GenericPreference(rs.getLong(1), rs.getLong(2), rs.getFloat(3));
}