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

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


    }
    this.dataModel = dataModel;
    this.weighted = weighting == Weighting.WEIGHTED;
    this.cachedNumItems = dataModel.getNumItems();
    this.cachedNumUsers = dataModel.getNumUsers();
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        cachedNumItems = dataModel.getNumItems();
        cachedNumUsers = dataModel.getNumUsers();
        return null;
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      throw new IllegalArgumentException("logBase is NaN or <= 1.0");
    }
    this.dataModel = dataModel;
    this.logBase = logBase;
    this.iufFactors = new AtomicReference<FastByIDMap<Double>>(new FastByIDMap<Double>());
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        recompute();
        return null;
      }
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      throw new IllegalArgumentException("samplingRate must be in (0,1]");
    }
    this.userSimilarity = userSimilarity;
    this.dataModel = dataModel;
    this.samplingRate = samplingRate;
    this.refreshHelper = new RefreshHelper(null);
    this.refreshHelper.addDependency(this.dataModel);
    this.refreshHelper.addDependency(this.userSimilarity);
  }
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    this.getRecommendableItemsSQL = getRecommendableItemsSQL;
    this.deleteDiffsSQL = deleteDiffsSQL;
    this.createDiffsSQL = createDiffsSQL;
    this.diffsExistSQL = diffsExistSQL;
    this.minDiffCount = minDiffCount;
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        buildAverageDiffs();
        return null;
      }
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    int numUsers = recommender.getDataModel().getNumUsers();
    this.recommendationCache = new Cache<Long,Recommendations>(new RecommendationRetriever(this.recommender),
        numUsers);
    this.estimatedPrefCache = new Cache<LongPair,Float>(new EstimatedPrefRetriever(this.recommender),
        numUsers);
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() {
        clear();
        return null;
      }
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    this.clusterSimilarity = clusterSimilarity;
    this.numClusters = numClusters;
    this.clusteringThreshold = Double.NaN;
    this.clusteringByThreshold = false;
    this.buildClustersLock = new ReentrantLock();
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        buildClusters();
        return null;
      }
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    this.clusterSimilarity = clusterSimilarity;
    this.numClusters = Integer.MIN_VALUE;
    this.clusteringThreshold = clusteringThreshold;
    this.clusteringByThreshold = true;
    this.buildClustersLock = new ReentrantLock();
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        buildClusters();
        return null;
      }
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    this.maxEntries = maxEntries;
    this.averageDiffs = new FastByIDMap<FastByIDMap<RunningAverage>>();
    this.averageItemPref = new FastByIDMap<RunningAverage>();
    this.buildAverageDiffsLock = new ReentrantReadWriteLock();
    this.allRecommendableItemIDs = new FastIDSet(dataModel.getNumItems());
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        buildAverageDiffs();
        return null;
      }
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    super(dataModel);
    this.itemAverages = new FastByIDMap<RunningAverage>();
    this.userAverages = new FastByIDMap<RunningAverage>();
    this.overallAveragePrefValue = new FullRunningAverage();
    this.buildAveragesLock = new ReentrantReadWriteLock();
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        buildAverageDiffs();
        return null;
      }
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    if (neighborhood == null) {
      throw new IllegalArgumentException("neighborhood is null");
    }
    this.neighborhood = neighborhood;
    this.similarity = similarity;
    this.refreshHelper = new RefreshHelper(null);
    refreshHelper.addDependency(dataModel);
    refreshHelper.addDependency(similarity);
    refreshHelper.addDependency(neighborhood);
  }
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