Package org.apache.mahout.cf.taste.impl.common

Examples of org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator

113
114
115
116
117
118
119
120
121
122
123
124
125
    FastByIDMap<PreferenceArray> trainingUsers = new FastByIDMap<PreferenceArray>(
        1 + (int) (evaluationPercentage * numUsers));
    FastByIDMap<PreferenceArray> testUserPrefs = new FastByIDMap<PreferenceArray>(
        1 + (int) (evaluationPercentage * numUsers));
   
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      if (random.nextDouble() < evaluationPercentage) {
        processOneUser(trainingPercentage, trainingUsers, testUserPrefs, userID, dataModel);
      }
    }
   
View Full Code Here
36
37
38
39
40
41
42
43
44
45
46
47
48
49
 
  public static void runLoad(Recommender recommender) throws TasteException {
    DataModel dataModel = recommender.getDataModel();
    int numUsers = dataModel.getNumUsers();
    double sampleRate = 1000.0 / numUsers;
    LongPrimitiveIterator userSampler = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel
        .getUserIDs(), sampleRate);
    recommender.recommend(userSampler.next(), 10); // Warm up
    Collection<Callable<Void>> callables = new ArrayList<Callable<Void>>();
    while (userSampler.hasNext()) {
      callables.add(new LoadCallable(recommender, userSampler.next()));
    }
    AbstractDifferenceRecommenderEvaluator.execute(callables);
  }
View Full Code Here
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
    FastIDSet itemIDSet = new FastIDSet();
    for (Map.Entry<Long,FastIDSet> entry : preferenceFromUsers.entrySet()) {
      long userID = entry.getKey();
      FastIDSet itemIDs = entry.getValue();
      itemIDSet.addAll(itemIDs);
      LongPrimitiveIterator it = itemIDs.iterator();
      while (it.hasNext()) {
        long itemID = it.next();
        FastIDSet userIDs = preferenceForItems.get(itemID);
        if (userIDs == null) {
          userIDs = new FastIDSet(2);
          preferenceForItems.put(itemID, userIDs);
        }
        userIDs.add(userID);
      }
    }
   
    this.itemIDs = itemIDSet.toArray();
    itemIDSet = null; // Might help GC -- this is big
    Arrays.sort(itemIDs);
   
    this.userIDs = new long[userData.size()];
    int i = 0;
    LongPrimitiveIterator it = userData.keySetIterator();
    while (it.hasNext()) {
      userIDs[i++] = it.next();
    }
    Arrays.sort(userIDs);
   
  }
View Full Code Here
109
110
111
112
113
114
115
116
117
118
119
120
    this(toDataMap(dataModel));
  }
 
  private static FastByIDMap<FastIDSet> toDataMap(DataModel dataModel) throws TasteException {
    FastByIDMap<FastIDSet> data = new FastByIDMap<FastIDSet>(dataModel.getNumUsers());
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      data.put(userID, dataModel.getItemIDsFromUser(userID));
    }
    return data;
  }
View Full Code Here
154
155
156
157
158
159
160
161
162
163
164
165
166
    if (itemIDs == null) {
      throw new NoSuchUserException();
    }
    PreferenceArray prefArray = new BooleanUserPreferenceArray(itemIDs.size());
    int i = 0;
    LongPrimitiveIterator it = itemIDs.iterator();
    while (it.hasNext()) {
      prefArray.setUserID(i, userID);
      prefArray.setItemID(i, it.next());
      i++;
    }
    return prefArray;
  }
View Full Code Here
185
186
187
188
189
190
191
192
193
194
195
196
197
    if (userIDs == null) {
      throw new NoSuchItemException();
    }
    PreferenceArray prefArray = new BooleanItemPreferenceArray(userIDs.size());
    int i = 0;
    LongPrimitiveIterator it = userIDs.iterator();
    while (it.hasNext()) {
      prefArray.setUserID(i, it.next());
      prefArray.setItemID(i, itemID);
      i++;
    }
    return prefArray;
  }
View Full Code Here
110
111
112
113
114
115
116
117
118
119
120
121
122
  }
 
  private synchronized void recompute() throws TasteException {
    Counters itemPreferenceCounts = new Counters();
    int numUsers = 0;
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      PreferenceArray prefs = dataModel.getPreferencesFromUser(it.nextLong());
      int size = prefs.length();
      for (int i = 0; i < size; i++) {
        itemPreferenceCounts.increment(prefs.getItemID(i));
      }
      numUsers++;
View Full Code Here
103
104
105
106
107
108
109
110
111
112
    DataModel dataModel = getDataModel();
    UserSimilarity userSimilarityImpl = getUserSimilarity();
   
    TopItems.Estimator<Long> estimator = new Estimator(userSimilarityImpl, userID, minSimilarity);
   
    LongPrimitiveIterator userIDs = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel.getUserIDs(),
      getSamplingRate());
   
    return TopItems.getTopUsers(n, userIDs, null, estimator);
  }
View Full Code Here
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
  @Override
  public long[] getUserNeighborhood(long userID) throws TasteException {
   
    DataModel dataModel = getDataModel();
    FastIDSet neighborhood = new FastIDSet();
    LongPrimitiveIterator usersIterable = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel
        .getUserIDs(), getSamplingRate());
    UserSimilarity userSimilarityImpl = getUserSimilarity();
   
    while (usersIterable.hasNext()) {
      long otherUserID = usersIterable.next();
      if (userID != otherUserID) {
        double theSimilarity = userSimilarityImpl.userSimilarity(userID, otherUserID);
        if (!Double.isNaN(theSimilarity) && (theSimilarity >= threshold)) {
          neighborhood.add(otherUserID);
        }
View Full Code Here
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
   
    int numItems = dataModel.getNumItems();
    RunningAverage precision = new FullRunningAverage();
    RunningAverage recall = new FullRunningAverage();
    RunningAverage fallOut = new FullRunningAverage();
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      if (random.nextDouble() < evaluationPercentage) {
        long start = System.currentTimeMillis();
        FastIDSet relevantItemIDs = new FastIDSet(at);
        PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
        int size = prefs.length();
        if (size < 2 * at) {
          // Really not enough prefs to meaningfully evaluate this user
          continue;
        }
       
        // List some most-preferred items that would count as (most) "relevant" results
        double theRelevanceThreshold = Double.isNaN(relevanceThreshold) ?
            computeThreshold(prefs) : relevanceThreshold;
        prefs.sortByValueReversed();
        for (int i = 0; (i < size) && (relevantItemIDs.size() < at); i++) {
          if (prefs.getValue(i) >= theRelevanceThreshold) {
            relevantItemIDs.add(prefs.getItemID(i));
          }
        }
        int numRelevantItems = relevantItemIDs.size();
        if (numRelevantItems > 0) {
          FastByIDMap<PreferenceArray> trainingUsers = new FastByIDMap<PreferenceArray>(dataModel
              .getNumUsers());
          LongPrimitiveIterator it2 = dataModel.getUserIDs();
          while (it2.hasNext()) {
            processOtherUser(userID, relevantItemIDs, trainingUsers, it2
                .nextLong(), dataModel);
          }
         
          DataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingUsers)
              : dataModelBuilder.buildDataModel(trainingUsers);
View Full Code Here
TOP

Related Classes of org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator

  • mia.recommender.ch02.ItemBaseRecommender
  • mia.recommender.ch02.UserBaseRecommender
  • net.myrrix.client.CLI
  • net.myrrix.client.translating.TranslatingClientRecommender
  • net.myrrix.common.collection.FastIDSetTest
  • net.myrrix.common.math.MatrixUtils
  • net.myrrix.common.random.RandomUtils
  • net.myrrix.online.factorizer.als.AlternatingLeastSquares
  • net.myrrix.online.generation.GenerationLoader
  • net.myrrix.online.generation.GenerationSerializer
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.