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 $

      }  
      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 (instance.attribute(j).isNumeric() &&

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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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      int current = m_SelectedAttributes[i];
      vals[i + j] = 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());

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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(instance.dataset());
    push(inst);

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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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      }
    }
    
    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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      }
    }
    
    // create instance
    if (sparse)
      result = new SparseInstance(weight, values);
    else
      result = new Instance(weight, values);
    
    return result;
  }

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