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

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

148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
    FastByIDMap<RunningAverage> level1Map = averageDiffs.get(itemID1);
    if (level1Map == null) {
      level1Map = new FastByIDMap<RunningAverage>();
      averageDiffs.put(itemID1, level1Map);
    }
    RunningAverage average = level1Map.get(itemID2);
    if ((average == null) && (averageCount < maxEntries)) {
      average = new FullRunningAverage();
      level1Map.put(itemID2, average);
      averageCount++;
    }
    if (average != null) {
      average.addDatum(diff);
    }
   
    allRecommendableItemIDs.add(itemID1);
    allRecommendableItemIDs.add(itemID2);
   
View Full Code Here
172
173
174
175
176
177
178
179
180
181
182
183
    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
215
216
217
218
219
220
221
222
223
224
225
      buildAverageDiffsLock.readLock().lock();
      level2Map = averageDiffs.get(itemID1);
    } finally {
      buildAverageDiffsLock.readLock().unlock();
    }
    RunningAverage average = null;
    if (level2Map != null) {
      average = level2Map.get(itemID2);
    }
    if (inverted) {
      if (average == null) {
View Full Code Here
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
    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) {
            if (remove) {
              average.removeDatum(prefDelta);
            } else {
              average.changeDatum(-prefDelta);
            }
          } else if (itemID == entry2.getKey()) {
            if (remove) {
              average.removeDatum(-prefDelta);
            } else {
              average.changeDatum(prefDelta);
            }
          }
        }
      }
      // RunningAverage itemAverage = averageItemPref.get(itemID);
View Full Code Here
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
    info.append(MathHelper.nice(Ak));
    info.append('\n');

    log.info(info.toString());

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();
        if (!Double.isNaN(e.get())) {
          double pref = e.get();
          double estimate = u.viewRow(slice.index()).dot(m.viewRow(e.index()));
          double err = pref - estimate;
          avg.addDatum(err * err);
          log.info("Comparing preference of user [{}] towards item [{}], was [{}] estimate is [{}]",
                   new Object[] { slice.index(), e.index(), pref, estimate });
        }
      }
    }
    double rmse = Math.sqrt(avg.getAverage());
    log.info("RMSE: {}", rmse);

    assertTrue(rmse < 0.2);
  }
View Full Code Here
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
    info.append(MathHelper.nice(Ak));
    info.append('\n');

    log.info(info.toString());

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      for (Vector.Element e : slice.vector()) {
        if (!Double.isNaN(e.get())) {
          double pref = e.get();
          double estimate = u.viewRow(slice.index()).dot(m.viewRow(e.index()));
          double confidence = 1 + alpha * observations.getQuick(slice.index(), e.index());
          double err = confidence * (pref - estimate) * (pref - estimate);
          avg.addDatum(err);
          log.info("Comparing preference of user [{}] towards item [{}], was [{}] with confidence [{}] " +
                       "estimate is [{}]", new Object[]{slice.index(), e.index(), pref, confidence, estimate});
        }
      }
    }
    double rmse = Math.sqrt(avg.getAverage());
    log.info("RMSE: {}", rmse);

    assertTrue(rmse < 0.4);
  }
View Full Code Here
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
    return 0;
  }

  protected double computeRmse(Path errors) {
    RunningAverage average = new FullRunningAverage();
    for (Pair<DoubleWritable,NullWritable> entry :
      new SequenceFileDirIterable<DoubleWritable, NullWritable>(errors, PathType.LIST, PathFilters.logsCRCFilter(),
          getConf())) {
      DoubleWritable error = entry.getFirst();
      average.addDatum(error.get() * error.get());
    }

    return Math.sqrt(average.getAverage());
  }
View Full Code Here
290
291
292
293
294
295
296
297
298
299
300
301
302
303
  }

  static class AverageRatingMapper extends Mapper<IntWritable,VectorWritable,IntWritable,VectorWritable> {
    @Override
    protected void map(IntWritable r, VectorWritable v, Context ctx) throws IOException, InterruptedException {
      RunningAverage avg = new FullRunningAverage();
      Iterator<Vector.Element> elements = v.get().iterateNonZero();
      while (elements.hasNext()) {
        avg.addDatum(elements.next().get());
      }
      Vector vector = new RandomAccessSparseVector(Integer.MAX_VALUE, 1);
      vector.setQuick(r.get(), avg.getAverage());
      ctx.write(new IntWritable(0), new VectorWritable(vector));
    }
View Full Code Here
28
29
30
31
32
33
34
35
36
  @Test
  public void testGetDiff() throws Exception {
    DataModel model = getDataModel();
    MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, false, Long.MAX_VALUE);
    RunningAverage average = storage.getDiff(1, 2);
    assertEquals(0.23333333333333334, average.getAverage(), EPSILON);
    assertEquals(3, average.getCount());
  }
View Full Code Here
38
39
40
41
42
43
44
45
46
  @Test
  public void testUpdate() throws Exception {
    DataModel model = getDataModel();
    MemoryDiffStorage storage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, false, Long.MAX_VALUE);
    storage.updateItemPref(1, 0.5f, false);
    RunningAverage average = storage.getDiff(1, 2);
    assertEquals(0.06666666666666668, average.getAverage(), EPSILON);
    assertEquals(3, average.getCount());
  }
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.