Examples of weka.core.SparseInstance

weka.core.SparseInstance
Class for storing an instance as a sparse vector. A sparse instance only requires storage for those attribute values that are non-zero. Since the objective is to reduce storage requirements for datasets with large numbers of default values, this also includes nominal attributes -- the first nominal value (i.e. that which has index 0) will not require explicit storage, so rearrange your nominal attribute value orderings if necessary. Missing values will be stored explicitly. @author Eibe Frank @version $Revision: 5970 $

      int current = m_SelectedAttributes[i];
      vals[i] = instance.value(current);
    }
    Instance inst = null;
    if (instance instanceof SparseInstance) {
      inst = new SparseInstance(instance.weight(), vals);
    } else {
      inst = new Instance(instance.weight(), vals);
    }
    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat());

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  }
      }
    }
    Instance inst = null;
    if (instance instanceof SparseInstance) {
      inst = new SparseInstance(instance.weight(), vals);
    } else {
      inst = new Instance(instance.weight(), vals);
    }
    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat());

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      // Add instance to dataset
      double[] tempValues = new double[numValues];
      int[] tempIndices = new int[numValues];
      System.arraycopy(m_ValueBuffer, 0, tempValues, 0, numValues);
      System.arraycopy(m_IndicesBuffer, 0, tempIndices, 0, numValues);
      Instance inst = new SparseInstance(weight, tempValues, tempIndices, m_Data.numAttributes());
      inst.setDataset(m_Data);


      return inst;
    }

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      if (instances.classIndex() > -1)
  values[values.length - 1] = inst.value(instances.classIndex());


      // generate and add instance
      if (inst instanceof SparseInstance)
  newInst = new SparseInstance(instances.instance(i).weight(), values);
      else
  newInst = new Instance(instances.instance(i).weight(), values);
      result.add(newInst);
    }

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      }  
      double[] tempVals = new double[ind];
      int[] tempInd = new int[ind];
      System.arraycopy(newVals, 0, tempVals, 0, ind);
      System.arraycopy(newIndices, 0, tempInd, 0, ind);
      inst = new SparseInstance(instance.weight(), tempVals, tempInd,
                                instance.numAttributes());
    } 
    else {
      double[] vals = instance.toDoubleArray();
      for (int j = 0; j < getInputFormat().numAttributes(); j++) {

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      }  
      double[] tempVals = new double[ind];
      int[] tempInd = new int[ind];
      System.arraycopy(newVals, 0, tempVals, 0, ind);
      System.arraycopy(newIndices, 0, tempInd, 0, ind);
      inst = new SparseInstance(instance.weight(), tempVals, tempInd,
                                instance.numAttributes());
    }
    else {
      double[] vals = instance.toDoubleArray();
      for (int j = 0; j < getInputFormat().numAttributes(); j++) {

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      vals[j] = 1;
    }
  } 
  newIndices[j] = instance.index(j);
      }
      inst = new SparseInstance(instance.weight(), vals, newIndices, 
                                outputFormatPeek().numAttributes());
    } else {
      double[] vals = new double[outputFormatPeek().numAttributes()];
      for (int j = 0; j < getInputFormat().numAttributes(); j++) {
  Attribute att = getInputFormat().attribute(j);

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      }  
      double[] tempVals = new double[ind];
      int[] tempInd = new int[ind];
      System.arraycopy(newVals, 0, tempVals, 0, ind);
      System.arraycopy(newIndices, 0, tempInd, 0, ind);
      inst = new SparseInstance(instance.weight(), tempVals, tempInd,
                                instance.numAttributes());
    } else {
      double[] vals = instance.toDoubleArray();
      for (int j = 0; j < getInputFormat().numAttributes(); j++) {
        if (m_SelectCols.isInRange(j)) {

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      }
      else {
  data = sparse;
      }
      
      result.add(new SparseInstance(1, data));
    }


    try {
      // close the stream
      m_sourceReader.close();

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    data[data.length - 1] = result.classAttribute().indexOfValue("-1");
  else
    throw new IllegalStateException("Class is not binary!");
      }
      
      result.add(new SparseInstance(1, data));
    }


    try {
      // close the stream
      m_sourceReader.close();

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