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

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


    this.numClusters = numClusters;
    this.clusteringThreshold = Double.NaN;
    this.clusteringByThreshold = false;
    this.samplingRate = samplingRate;
    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.numClusters = Integer.MIN_VALUE;
    this.clusteringThreshold = clusteringThreshold;
    this.clusteringByThreshold = true;
    this.samplingRate = samplingRate;
    this.buildClustersLock = new ReentrantLock();
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        buildClusters();
        return null;
      }
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  public ItemAverageRecommender(DataModel dataModel) {
    super(dataModel);
    this.itemAverages = new FastByIDMap<RunningAverage>();
    this.buildAveragesLock = new ReentrantReadWriteLock();
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        buildAverageDiffs();
        return null;
      }
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    super(dataModel);
    if (similarity == null) {
      throw new IllegalArgumentException("similarity is null");
    }
    this.similarity = similarity;
    this.refreshHelper = new RefreshHelper(null);
    refreshHelper.addDependency(dataModel);
    refreshHelper.addDependency(similarity);
  }
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    emSvd = new ExpectationMaximizationSVD(numUsers, numItems, numFeatures, defaultValue);
    cachedPreferences = new ArrayList<Preference>(numUsers);
    recachePreferences();
   
    refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        recachePreferences();
        // TODO: train again
        return null;
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    Preconditions.checkArgument(dataModel != null, "dataModel is null");
    Preconditions.checkArgument(logBase > 1.0, "logBase should be > 1.0");
    this.dataModel = dataModel;
    this.logBase = logBase;
    this.iufFactors = new FastByIDMap<Double>();
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        recompute();
        return null;
      }
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    this.clusterSimilarity = clusterSimilarity;
    this.numClusters = numClusters;
    this.clusteringThreshold = Double.NaN;
    this.clusteringByThreshold = false;
    this.samplingRate = samplingRate;
    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.samplingRate = samplingRate;
    this.refreshHelper = new RefreshHelper(new Callable<Object>() {
      @Override
      public Object call() throws TasteException {
        buildClusters();
        return null;
      }
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  public ItemAverageRecommender(DataModel dataModel) throws TasteException {
    super(dataModel);
    this.itemAverages = new FastByIDMap<RunningAverage>();
    this.buildAveragesLock = new ReentrantReadWriteLock();
    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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