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

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

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    super.setPreference(userID, itemID, value);
    try {
      buildAveragesLock.writeLock().lock();
      RunningAverage itemAverage = itemAverages.get(itemID);
      if (itemAverage == null) {
        RunningAverage newItemAverage = new FullRunningAverage();
        newItemAverage.addDatum(prefDelta);
        itemAverages.put(itemID, newItemAverage);
      } else {
        itemAverage.changeDatum(prefDelta);
      }
      RunningAverage userAverage = userAverages.get(userID);
      if (userAverage == null) {
        RunningAverage newUserAveragae = new FullRunningAverage();
        newUserAveragae.addDatum(prefDelta);
        userAverages.put(userID, newUserAveragae);
      } else {
        userAverage.changeDatum(prefDelta);
      }
      overallAveragePrefValue.changeDatum(prefDelta);
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  private RunningAverage buildRunningAverage() {
    if (stdDevWeighted) {
      return compactAverages ? new CompactRunningAverageAndStdDev() : new FullRunningAverageAndStdDev();
    } else {
      return compactAverages ? new CompactRunningAverage() : new FullRunningAverage();
    }
  }
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      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);
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    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);
  }
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    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);
  }
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    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());
  }
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  }

  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));
    }
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      this.rescorer = rescorer;
    }
   
    @Override
    public double estimate(Long itemID) throws TasteException {
      RunningAverage average = new FullRunningAverage();
      double[] similarities = similarity.itemSimilarities(itemID, toItemIDs);
      for (int i = 0; i < toItemIDs.length; i++) {
        long toItemID = toItemIDs[i];
        LongPair pair = new LongPair(toItemID, itemID);
        if ((rescorer != null) && rescorer.isFiltered(pair)) {
          continue;
        }
        double estimate = similarities[i];
        if (rescorer != null) {
          estimate = rescorer.rescore(pair, estimate);
        }
        average.addDatum(estimate);
      }
      return average.getAverage();
    }
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  private final class PrefRetriever implements Retriever<Long,Float> {
   
    @Override
    public Float get(Long key) throws TasteException {
      RunningAverage average = new FullRunningAverage();
      PreferenceArray prefs = dataModel.getPreferencesFromUser(key);
      int size = prefs.length();
      if (size == 0) {
        return ZERO;
      }
      for (int i = 0; i < size; i++) {
        average.addDatum(prefs.getValue(i));
      }
      return (float) average.getAverage();
    }
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    }
   
    @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.next(), itemID);
        if (pref != null) {
          average.addDatum(pref);
        }
      }
      return average.getAverage();
    }
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