Examples of weka.filters.unsupervised.attribute.RemoveUseless

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.RemoveUseless

weka.filters.unsupervised.attribute.RemoveUseless
This filter removes attributes that do not vary at all or that vary too much. All constant attributes are deleted automatically, along with any that exceed the maximum percentage of variance parameter. The maximum variance test is only applied to nominal attributes.
Valid options are:
```
 -M <max variance %> Maximum variance percentage allowed (default 99)
```
@author Richard Kirkby (rkirkby@cs.waikato.ac.nz) @version $Revision: 5987 $

    m_ReplaceMissingValues = new ReplaceMissingValues();
    m_ReplaceMissingValues.setInputFormat(train);
    train = Filter.useFilter(train, m_ReplaceMissingValues);


    // Remove useless attributes
    m_AttFilter = new RemoveUseless();
    m_AttFilter.setInputFormat(train);
    train = Filter.useFilter(train, m_AttFilter);
  
    // Transform attributes
    m_NominalToBinary = new NominalToBinary();

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    }
    else {
      random = new Random(m_Seed);
    }


    m_RemoveUseless = new RemoveUseless();
    m_RemoveUseless.setInputFormat(data);
    data = Filter.useFilter(data, m_RemoveUseless);


    m_Normalize = new Normalize();
    m_Normalize.setInputFormat(data);

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    m_nominalToBinary = new NominalToBinary();
    m_nominalToBinary.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_nominalToBinary);


    m_removeUseless = new RemoveUseless();
    m_removeUseless.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_removeUseless);
    
    m_instances.randomize(new Random(1));

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    m_ReplaceMissingValues = new ReplaceMissingValues();
    m_ReplaceMissingValues.setInputFormat(train);
    train = Filter.useFilter(train, m_ReplaceMissingValues);


    // Remove useless attributes
    m_AttFilter = new RemoveUseless();
    m_AttFilter.setInputFormat(train);
    train = Filter.useFilter(train, m_AttFilter);
  
    // Transform attributes
    m_NominalToBinary = new NominalToBinary();

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    m_nominalToBinary = new NominalToBinary();
    m_nominalToBinary.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_nominalToBinary);


    m_removeUseless = new RemoveUseless();
    m_removeUseless.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_removeUseless);
    
    m_instances.randomize(new Random(1));

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    public void removeUselessFilter(String variance) throws Exception {
        if (_logger.isDebugEnabled()) {
            _logger.debug("Applying remove useless filter");
        }
        // Might employ filtered classifier for production
        RemoveUseless ru = new RemoveUseless();
        String[] options = new String[2];
        options[0] = "-M";
        options[1] = variance;
        ru.setOptions(options);
        ru.setInputFormat(_instances);
        _instances = Filter.useFilter(_instances, ru);
    }

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    m_ReplaceMissingValues = new ReplaceMissingValues();
    m_ReplaceMissingValues.setInputFormat(train);
    train = Filter.useFilter(train, m_ReplaceMissingValues);


    // Remove useless attributes
    m_AttFilter = new RemoveUseless();
    m_AttFilter.setInputFormat(train);
    train = Filter.useFilter(train, m_AttFilter);
  
    // Transform attributes
    m_NominalToBinary = new NominalToBinary();

View Full Code Here


    m_nominalToBinary = new NominalToBinary();
    m_nominalToBinary.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_nominalToBinary);


    m_removeUseless = new RemoveUseless();
    m_removeUseless.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_removeUseless);
    
    m_instances.randomize(new Random(1));

View Full Code Here

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