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

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

112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
  @Test
  public void testUpdate() throws Exception {
    DataModel model = getDataModel();
    MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, Long.MAX_VALUE);
   
    RunningAverage average = storage.getDiff(1, 2);
    assertEquals(0.23333332935969034, average.getAverage(), EPSILON);
    assertEquals(3, average.getCount());
   
    storage.updateItemPref(1, 0.5f);
   
    average = storage.getDiff(1, 2);
    assertEquals(0.06666666666666668, average.getAverage(), EPSILON);
    assertEquals(3, average.getCount());
  }
View Full Code Here
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
  @Test
  public void testRemove() throws Exception {
    DataModel model = getDataModel();
    MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, Long.MAX_VALUE);
   
    RunningAverage average1 = storage.getDiff(0, 2);
    assertEquals(0.1, average1.getAverage(), EPSILON);
    assertEquals(3, average1.getCount());
   
    RunningAverage average2 = storage.getDiff(1, 2);
    assertEquals(0.23333332935969034, average2.getAverage(), EPSILON);
    assertEquals(3, average2.getCount());
   
    storage.removeItemPref(4, 2, 0.8f);
   
    average1 = storage.getDiff(0, 2);
    assertEquals(0.1, average1.getAverage(), EPSILON);
    assertEquals(2, average1.getCount());
   
    average2 = storage.getDiff(1, 2);
    assertEquals(0.1, average2.getAverage(), EPSILON);
    assertEquals(2, average2.getCount());
  }
View Full Code Here
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
    }
   
    @Override
    public double estimate(Long itemID) throws TasteException {
      DataModel dataModel = getDataModel();
      RunningAverage average = new FullRunningAverage();
      LongPrimitiveIterator it = cluster.iterator();
      while (it.hasNext()) {
        Float pref = dataModel.getPreferenceValue(it.nextLong(), itemID);
        if (pref != null) {
          average.addDatum(pref);
        }
      }
      return average.getAverage();
    }
View Full Code Here
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
    Factorization factorization = sgdFactorizer.factorize();

    log.info("Estimating validation preferences...");
    int prefsProcessed = 0;
    RunningAverage average = new FullRunningAverage();
    for (Pair<PreferenceArray,long[]> validationPair
        : new DataFileIterable(KDDCupDataModel.getValidationFile(dataFileDirectory))) {
      for (Preference validationPref : validationPair.getFirst()) {
        double estimate = estimatePreference(factorization, validationPref.getUserID(), validationPref.getItemID(),
            factorizablePreferences.getMinPreference(), factorizablePreferences.getMaxPreference());
        double error = validationPref.getValue() - estimate;
        average.addDatum(error * error);
        prefsProcessed++;
        if (prefsProcessed % 100000 == 0) {
          log.info("Computed {} estimations", prefsProcessed);
        }
      }
    }
    log.info("Computed {} estimations, done.", prefsProcessed);

    double rmse = Math.sqrt(average.getAverage());
    log.info("RMSE {}", rmse);

    log.info("Estimating test preferences...");
    OutputStream out = null;
    try {
View Full Code Here
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
    double totalPreference = 0.0;
    PreferenceArray prefs = getDataModel().getPreferencesFromUser(userID);
    RunningAverage[] averages = diffStorage.getDiffs(userID, itemID, prefs);
    int size = prefs.length();
    for (int i = 0; i < size; i++) {
      RunningAverage averageDiff = averages[i];
      if (averageDiff != null) {
        double averageDiffValue = averageDiff.getAverage();
        if (weighted) {
          double weight = averageDiff.getCount();
          if (stdDevWeighted) {
            double stdev = ((RunningAverageAndStdDev) averageDiff).getStandardDeviation();
            if (!Double.isNaN(stdev)) {
              weight /= 1.0 + stdev;
            }
            // If stdev is NaN, then it is because count is 1. Because we're weighting by count,
            // the weight is already relatively low. We effectively assume stdev is 0.0 here and
            // that is reasonable enough. Otherwise, dividing by NaN would yield a weight of NaN
            // and disqualify this pref entirely
            // (Thanks Daemmon)
          }
          totalPreference += weight * (prefs.getValue(i) + averageDiffValue);
          count += weight;
        } else {
          totalPreference += prefs.getValue(i) + averageDiffValue;
          count += 1.0;
        }
      }
    }
    if (count <= 0.0) {
      RunningAverage itemAverage = diffStorage.getAverageItemPref(itemID);
      return itemAverage == null ? Float.NaN : (float) itemAverage.getAverage();
    } else {
      return (float) (totalPreference / count);
    }
  }
View Full Code Here
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
      buildAverageDiffsLock.readLock().lock();
      level2Map = averageDiffs.get(itemID1);
    } finally {
      buildAverageDiffsLock.readLock().unlock();
    }
    RunningAverage average = null;
    if (level2Map != null) {
      average = level2Map.get(itemID2);
    }
    if (inverted) {
      return average == null ? null : average.inverse();
    } else {
      return average;
    }
  }
View Full Code Here
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
      int length = userPreferences.length();
      for (int i = 0; i < length; i++) {
        long itemIDB = userPreferences.getItemID(i);
        float bValue = userPreferences.getValue(i);
        if (itemIDA < itemIDB) {
          RunningAverage average = aMap.get(itemIDB);
          if (average == null) {
            average = buildRunningAverage();
            aMap.put(itemIDB, average);
          }
          average.addDatum(bValue - prefValue);
        } else {
          FastByIDMap<RunningAverage> bMap = averageDiffs.get(itemIDB);
          if (bMap == null) {
            bMap = new FastByIDMap<RunningAverage>();
            averageDiffs.put(itemIDB, bMap);
          }
          RunningAverage average = bMap.get(itemIDA);
          if (average == null) {
            average = buildRunningAverage();
            bMap.put(itemIDA, average);
          }
          average.addDatum(prefValue - bValue);
        }
      }

    } finally {
      buildAverageDiffsLock.writeLock().unlock();
View Full Code Here
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
    try {
      buildAverageDiffsLock.readLock().lock();
      for (Map.Entry<Long,FastByIDMap<RunningAverage>> entry : averageDiffs.entrySet()) {
        boolean matchesItemID1 = itemID == entry.getKey();
        for (Map.Entry<Long,RunningAverage> entry2 : entry.getValue().entrySet()) {
          RunningAverage average = entry2.getValue();
          if (matchesItemID1) {
            average.changeDatum(-prefDelta);
          } else if (itemID == entry2.getKey()) {
            average.changeDatum(prefDelta);
          }
        }
      }
      RunningAverage itemAverage = averageItemPref.get(itemID);
      if (itemAverage != null) {
        itemAverage.changeDatum(prefDelta);
      }
    } finally {
      buildAverageDiffsLock.readLock().unlock();
    }
  }
View Full Code Here
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
        float bValue = userPreferences.getValue(i);

        if (itemIDA < itemIDB) {

          if (aMap != null) {
            RunningAverage average = aMap.get(itemIDB);
            if (average != null) {
              if (average.getCount() <= 1) {
                aMap.remove(itemIDB);
              } else {
                average.removeDatum(bValue - prefValue);
              }
            }
          }

        } else  if (itemIDA > itemIDB) {

          FastByIDMap<RunningAverage> bMap = averageDiffs.get(itemIDB);
          if (bMap != null) {
            RunningAverage average = bMap.get(itemIDA);
            if (average != null) {
              if (average.getCount() <= 1) {
                aMap.remove(itemIDA);
              } else {
                average.removeDatum(prefValue - bValue);
              }
            }
          }

        }
View Full Code Here
318
319
320
321
322
323
324
325
326
327
328
329
    Iterator<Map.Entry<Long,FastByIDMap<RunningAverage>>> it1 = averageDiffs.entrySet().iterator();
    while (it1.hasNext()) {
      FastByIDMap<RunningAverage> map = it1.next().getValue();
      Iterator<Map.Entry<Long,RunningAverage>> it2 = map.entrySet().iterator();
      while (it2.hasNext()) {
        RunningAverage average = it2.next().getValue();
        if (average.getCount() <= 1) {
          it2.remove();
        }
      }
      if (map.isEmpty()) {
        it1.remove();
View Full Code Here
TOP

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

  • org.apache.mahout.cf.taste.example.kddcup.track1.svd.ParallelArraysSGDFactorizer
  • org.apache.mahout.cf.taste.example.kddcup.track1.svd.Track1SVDRunner
  • org.apache.mahout.cf.taste.hadoop.als.FactorizationEvaluator
  • org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJob$AverageRatingMapper
  • org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJob$InitializeMReducer
  • org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJobTest
  • org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator
  • org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator
  • org.apache.mahout.cf.taste.impl.eval.LoadEvaluator
  • org.apache.mahout.cf.taste.impl.eval.RMSRecommenderEvaluator
    • 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.