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

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

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      long userID = validationPrefs.get(0).getUserID();
      estimateCallables.add(
          new PreferenceEstimateCallable(recommender, userID, validationPrefs, noEstimateCounter));
    }

    RunningAverageAndStdDev timing = new FullRunningAverageAndStdDev();
    execute(estimateCallables, noEstimateCounter, timing);

    double result = computeFinalEvaluation();
    log.info("Evaluation result: {}", result);
    return result;
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  private static double computeThreshold(PreferenceArray prefs) {
    if (prefs.length() < 2) {
      // Not enough data points -- return a threshold that allows everything
      return Double.NEGATIVE_INFINITY;
    }
    RunningAverageAndStdDev stdDev = new FullRunningAverageAndStdDev();
    int size = prefs.length();
    for (int i = 0; i < size; i++) {
      stdDev.addDatum(prefs.getValue(i));
    }
    return stdDev.getAverage() + stdDev.getStandardDeviation();
  }
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  private class MeanStdevRetriever implements Retriever<Long, RunningAverageAndStdDev> {

    @Override
    public RunningAverageAndStdDev get(Long userID) throws TasteException {
      RunningAverageAndStdDev running = new FullRunningAverageAndStdDev();
      PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
      int size = prefs.length();
      for (int i = 0; i < size; i++) {
        running.addDatum(prefs.getValue(i));
      }
      return running;
    }
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    DataModel dataModel = recommender.getDataModel();
    int numUsers = dataModel.getNumUsers();
    double sampleRate = 1000.0 / numUsers;
    LongPrimitiveIterator userSampler =
        SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel.getUserIDs(), sampleRate);
    RunningAverage recommendationTime = new FullRunningAverageAndStdDev();
    recommender.recommend(userSampler.next(), 10); // Warm up
    Collection<Callable<Object>> callables = new ArrayList<Callable<Object>>();
    while (userSampler.hasNext()) {
      callables.add(new LoadCallable(recommender, userSampler.next(), recommendationTime));
    }
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    return averageCount;
  }

  private RunningAverage buildRunningAverage() {
    if (stdDevWeighted) {
      return compactAverages ? new CompactRunningAverageAndStdDev() : new FullRunningAverageAndStdDev();
    } else {
      return compactAverages ? new CompactRunningAverage() : new FullRunningAverage();
    }
  }
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  private static double computeThreshold(PreferenceArray prefs) {
    if (prefs.length() < 2) {
      // Not enough data points -- return a threshold that allows everything
      return Double.NEGATIVE_INFINITY;
    }
    RunningAverageAndStdDev stdDev = new FullRunningAverageAndStdDev();
    int size = prefs.length();
    for (int i = 0; i < size; i++) {
      stdDev.addDatum(prefs.getValue(i));
    }
    return stdDev.getAverage() + stdDev.getStandardDeviation();
  }
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    }
    return averageCount;
  }
 
  private RunningAverage buildRunningAverage() {
    return stdDevWeighted ? new FullRunningAverageAndStdDev() : new FullRunningAverage();
  }
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  private static Collection<Callable<Void>> wrapWithStatsCallables(Collection<Callable<Void>> callables,
                                                                   AtomicInteger noEstimateCounter) {
    int size = callables.size();
    Collection<Callable<Void>> wrapped = new ArrayList<Callable<Void>>(size);
    int count = 0;
    RunningAverageAndStdDev timing = new FullRunningAverageAndStdDev();
    for (Callable<Void> callable : callables) {
      boolean logStats = count++ % 1000 == 0; // log every 100 or so iterations
      wrapped.add(new StatsCallable(callable, logStats, timing, noEstimateCounter));
    }
    return wrapped;
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    for (Map.Entry<Long,PreferenceArray> entry : testPrefs.entrySet()) {
      estimateCallables.add(
          new PreferenceEstimateCallable(recommender, entry.getKey(), entry.getValue(), noEstimateCounter));
    }
    log.info("Beginning evaluation of {} users", estimateCallables.size());
    RunningAverageAndStdDev timing = new FullRunningAverageAndStdDev();
    execute(estimateCallables, noEstimateCounter, timing);
    return computeFinalEvaluation();
  }
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  private static double computeThreshold(PreferenceArray prefs) {
    if (prefs.length() < 2) {
      // Not enough data points -- return a threshold that allows everything
      return Double.NEGATIVE_INFINITY;
    }
    RunningAverageAndStdDev stdDev = new FullRunningAverageAndStdDev();
    int size = prefs.length();
    for (int i = 0; i < size; i++) {
      stdDev.addDatum(prefs.getValue(i));
    }
    return stdDev.getAverage() + stdDev.getStandardDeviation();
  }
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