Examples of weka.core.DenseInstance

weka.core.DenseInstance
te empty instance with three attribute values
Instance inst = new DenseInstance(3);

// Set instance's values for the attributes "length", "weight", and "position"
inst.setValue(length, 5.3);
inst.setValue(weight, 300);
inst.setValue(position, "first");

// Set instance's dataset to be the dataset "race"
inst.setDataset(race);

// Print the instance
System.out.println("The instance: " + inst);
...

All methods that change an instance's attribute values are safe, ie. a change of an instance's attribute values does not affect any other instances. All methods that change an instance's attribute values clone the attribute value vector before it is changed. If your application heavily modifies instance values, it may be faster to create a new instance from scratch. @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5987 $

    Instance example;
    int numAtts = getNumAttributes();
    if (getClassFlag()) 
      numAtts++;


    example = new DenseInstance(numAtts);
    example.setDataset(format);
        
    for (int i = 0; i < getNumAttributes(); i++)
      example.setValue(i, randomG.nextGaussian() * stdDev + center[i]);

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    }


    if (sparse)
      result = new SparseInstance(weight, vals);
    else
      result = new DenseInstance(weight, vals);
    result.setDataset(data);
      
    return result;
  }

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    //convert to coordinates in the training instance space.
  double x = convertFromPanelX(mouseX);
  double y = convertFromPanelY(mouseY);
  
  //build the training instance
  Instance newInstance = new DenseInstance(m_trainingData.numAttributes());
  for (int i = 0; i < newInstance.numAttributes(); i++) {
    if (i == classAttIndex) {
      newInstance.setValue(i,classValue);
    }
    else if (i == m_xAttribute)
      newInstance.setValue(i,x);
    else if (i == m_yAttribute)
      newInstance.setValue(i,y);
    else newInstance.setMissing(i);
  }
  
  //add it to our data set.
  addTrainingInstance(newInstance);
  }

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        m_dataGenerator.buildGenerator(m_trainingData);


        // generate samples
        m_weightingAttsValues = new double [m_attsToWeightOn.length];
        m_vals = new double[m_trainingData.numAttributes()];
        m_predInst = new DenseInstance(1.0, m_vals);
        m_predInst.setDataset(m_trainingData);


  
        m_size = 1 << 4;  // Current sample region size

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    }
    
    if (inst instanceof SparseInstance) {
      return new SparseInstance(inst.weight(), newVals);
    } else {
      return new DenseInstance(inst.weight(), newVals);
    }      
  }

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    if (!m_transBackToOriginal) {
      if (instance instanceof SparseInstance) {
      return new SparseInstance(instance.weight(), newVals);
      } else {
        return new DenseInstance(instance.weight(), newVals);
      }      
    } else {
      if (instance instanceof SparseInstance) {
        return convertInstanceToOriginal(new SparseInstance(instance.weight(), 
                                                            newVals));
      } else {
        return convertInstanceToOriginal(new DenseInstance(instance.weight(),
                                                      newVals));
      }
    }
  }

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    int num = -1;
    Instance example = null;
    int numAtts = m_NumAttributes;
    if (getClassFlag()) numAtts++;


    example = new DenseInstance(numAtts);
    example.setDataset(format);
    boolean[] attributes = cl.getAttributes();
    double[] minValue = cl.getMinValue();
    double[] maxValue = cl.getMaxValue();
    double value;

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    Instance example = null;
    int numAtts = m_NumAttributes;
    if (getClassFlag()) numAtts++;


    example = new DenseInstance(numAtts);
    example.setDataset(format);
    boolean[] attributes = cl.getAttributes();
    double[] minValue = cl.getMinValue();
    double[] maxValue = cl.getMaxValue();
    double[] diff = new double[minValue.length];

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    Instance example = null;
    int numAtts = m_NumAttributes;
    if (getClassFlag()) numAtts++;


    example = new DenseInstance(numAtts);
    example.setDataset(format);
    boolean[] attributes = cl.getAttributes();
    double[] minValue = cl.getMinValue();
    double[] maxValue = cl.getMaxValue();
    int[] minInt = new int[minValue.length];

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    boolean makeInteger = cl.isInteger();
    Instance example = null;
    int numAtts = m_NumAttributes;
    if (getClassFlag()) numAtts++;


    example = new DenseInstance(numAtts);
    example.setDataset(format);
    boolean[] attributes = cl.getAttributes();
    double[] meanValue = cl.getMeanValue();
    double[] stddevValue = cl.getStddevValue();

View Full Code Here

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