Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.AddID


      // not occur immediately after the last training event. Since the time
      // index is artificial, all we can do for future predictions is assume
      // that the n instances provided to the primeForecaster() method overlap
      // the last n instances of the training data and that future predictions
      // occur from the last known artificial time value + 1.
      m_artificialTimeMaker = new AddID();
      m_artificialTimeMaker.setAttributeName("ArtificialTimeIndex");
      m_artificialTimeMaker.setIDIndex("last");
      m_artificialTimeMaker.setInputFormat(result);
      result = Filter.useFilter(result, m_artificialTimeMaker);
      m_useArtificialTimeIndex = true;
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