Examples of weka.filters.unsupervised.attribute.Standardize

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.Standardize

weka.filters.unsupervised.attribute.Standardize
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
Valid options are:
```
 -unset-class-temporarily Unsets the class index temporarily before the filter is applied to the data. (default: no)
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5547 $

    else {
      m_NominalToBinary = null;
    }


    if (m_filterType == FILTER_STANDARDIZE) {
      m_Filter = new Standardize();
      m_Filter.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Filter); 
    } else if (m_filterType == FILTER_NORMALIZE) {
      m_Filter = new Normalize();
      m_Filter.setInputFormat(insts);

View Full Code Here

    }
    double y1 = instances.instance(index).classValue();
    
    // apply filters
    if (m_filterType == FILTER_STANDARDIZE) {
      m_Filter = new Standardize();
      ((Standardize)m_Filter).setIgnoreClass(true);
      m_Filter.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_Filter);      
    } else if (m_filterType == FILTER_NORMALIZE) {
      m_Filter = new Normalize();

View Full Code Here

    ((Center) m_Filter).setIgnoreClass(true);
          break;
  case PREPROCESSING_STANDARDIZE:
    m_ClassMean   = instances.meanOrMode(instances.classIndex());
    m_ClassStdDev = StrictMath.sqrt(instances.variance(instances.classIndex()));
    m_Filter      = new Standardize();
    ((Standardize) m_Filter).setIgnoreClass(true);
          break;
  default:
      m_ClassMean   = 0;
    m_ClassStdDev = 1;

View Full Code Here

    else {
      m_NominalToBinary = null;
    }


    if (m_filterType == FILTER_STANDARDIZE) 
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;

View Full Code Here

    m_ConvertToProp.setInputFormat(train);
    train = Filter.useFilter( train, m_ConvertToProp);
    train.deleteAttributeAt(0); //remove the bagIndex attribute;


    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;

View Full Code Here

        }
      }
    }
    
    // now standardize the input data
    m_standardizeFilter = new Standardize();
    m_standardizeFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, m_standardizeFilter);
  }

View Full Code Here

      }
    }


    /* filter the training data */
    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;

View Full Code Here


    }


    /* filter the training data */
    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;

View Full Code Here

    // convert the training dataset into single-instance dataset
    m_ConvertToSI.setInputFormat(train);  
    train = Filter.useFilter( train, m_ConvertToSI);


    if (m_filterType == FILTER_STANDARDIZE) 
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;

View Full Code Here

    }




    /* filter the training data */
    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;

View Full Code Here

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