Examples of weka.filters.unsupervised.attribute.MultiInstanceToPropositional

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.MultiInstanceToPropositional

weka.filters.unsupervised.attribute.MultiInstanceToPropositional
Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.
Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.
Valid options are:
```
 -A <num> The type of weight setting for each prop. instance: 0.weight = original single bag weight /Total number of prop. instance in the corresponding bag; 1.weight = 1.0; 2.weight = 1.0/Total number of prop. instance in the  corresponding bag;  3. weight = Total number of prop. instance / (Total number  of bags * Total number of prop. instance in the  corresponding bag).  (default:0)
```
@author Lin Dong (ld21@cs.waikato.ac.nz) @version $Revision: 5543 $ @see PropositionalToMultiInstance

    else 
      m_Filter = null;




    Instances transformedInsts;
    Filter convertToProp = new MultiInstanceToPropositional();
    Filter convertToMI = new PropositionalToMultiInstance();


    //transform the data into single-instance format
    if (m_minimax){ 
      /* using SimpleMI class minimax transform method. 
         this method transforms the multi-instance dataset into minmax feature space (single-instance) */
      SimpleMI transMinimax = new SimpleMI();
      transMinimax.setTransformMethod(
          new SelectedTag(
            SimpleMI.TRANSFORMMETHOD_MINIMAX, SimpleMI.TAGS_TRANSFORMMETHOD));
      transformedInsts = transMinimax.transform(insts);
    }
    else { 
      convertToProp.setInputFormat(insts);
      transformedInsts=Filter.useFilter(insts, convertToProp);
    }


    if (m_Missing != null) {
      m_Missing.setInputFormat(transformedInsts);

View Full Code Here

    //convert instance into instances
    Instances insts = new Instances(inst.dataset(), 0);
    insts.add(inst);


    //transform the data into single-instance format
    Filter convertToProp = new MultiInstanceToPropositional();
    Filter convertToMI = new PropositionalToMultiInstance();


    if (m_minimax){ // using minimax feature space
      SimpleMI transMinimax = new SimpleMI();
      transMinimax.setTransformMethod(
          new SelectedTag(
            SimpleMI.TRANSFORMMETHOD_MINIMAX, SimpleMI.TAGS_TRANSFORMMETHOD));
      insts = transMinimax.transform (insts);
    }
    else{
      convertToProp.setInputFormat(insts);
      insts=Filter.useFilter( insts, convertToProp);
    }


    // Filter instances 
    if (m_Missing!=null)

View Full Code Here

    else 
      m_Filter = null;




    Instances transformedInsts;
    Filter convertToProp = new MultiInstanceToPropositional();
    Filter convertToMI = new PropositionalToMultiInstance();


    //transform the data into single-instance format
    if (m_minimax){ 
      /* using SimpleMI class minimax transform method. 
         this method transforms the multi-instance dataset into minmax feature space (single-instance) */
      SimpleMI transMinimax = new SimpleMI();
      transMinimax.setTransformMethod(
          new SelectedTag(
            SimpleMI.TRANSFORMMETHOD_MINIMAX, SimpleMI.TAGS_TRANSFORMMETHOD));
      transformedInsts = transMinimax.transform(insts);
    }
    else { 
      convertToProp.setInputFormat(insts);
      transformedInsts=Filter.useFilter(insts, convertToProp);
    }


    if (m_Missing != null) {
      m_Missing.setInputFormat(transformedInsts);

View Full Code Here

    //convert instance into instances
    Instances insts = new Instances(inst.dataset(), 0);
    insts.add(inst);


    //transform the data into single-instance format
    Filter convertToProp = new MultiInstanceToPropositional();
    Filter convertToMI = new PropositionalToMultiInstance();


    if (m_minimax){ // using minimax feature space
      SimpleMI transMinimax = new SimpleMI();
      transMinimax.setTransformMethod(
          new SelectedTag(
            SimpleMI.TRANSFORMMETHOD_MINIMAX, SimpleMI.TAGS_TRANSFORMMETHOD));
      insts = transMinimax.transform (insts);
    }
    else{
      convertToProp.setInputFormat(insts);
      insts=Filter.useFilter( insts, convertToProp);
    }


    // Filter instances 
    if (m_Missing!=null)

View Full Code Here

TOP

Related Classes of weka.filters.unsupervised.attribute.MultiInstanceToPropositional

weka.classifiers.mi.MISMO

weka.core.Attribute

weka.core.Capabilities

weka.core.Instance

weka.core.Instances

weka.core.Option

weka.core.RelationalLocator

weka.core.SelectedTag

weka.core.StringLocator

java.util.Vector

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.