How to use

weka.filters.unsupervised.attribute.Standardize

constructor

Instances train = ...   // from somewhere
Instances test = ...    // from somewhere
Standardize filter = new Standardize();
filter.setInputFormat(train);  // initializing the filter once with training set
Instances newTrain = Filter.useFilter(train, filter);  // configures the Filter based on train instances and returns filtered instances
Instances newTest = Filter.useFilter(test, filter);    // create new test se

 Instances train = ...   // from somewhere
Instances test = ...    // from somewhere
Standardize filter = new Standardize();
filter.setInputFormat(train);  // initializing the filter once with training set
Instances newTrain = Filter.useFilter(train, filter);  // configures the Filter based on train instances and returns filtered instances
Instances newTest = Filter.useFilter(test, filter);    // create new test set

 /**
  * Main method for testing this class.
  *
  * @param argv should contain arguments to the filter: 
  * use -h for help
  */
 public static void main(String [] argv) {
  runFilter(new Standardize(), argv);
 }
}

/** Creates an example Standardize */
public Filter getFilter() {
 Standardize f = new Standardize();
 return f;
}

 /**
  * Main method for testing this class.
  *
  * @param argv should contain arguments to the filter: 
  * use -h for help
  */
 public static void main(String [] argv) {
  runFilter(new Standardize(), argv);
 }
}

/** Creates an example Standardize */
public Filter getFilter() {
 Standardize f = new Standardize();
 return f;
}

 m_Filter = new Standardize();
} else if (m_filterType == FILTER_NORMALIZE) {
 m_Filter = new Normalize();

protected void fillCovariance() throws Exception {
 // just center the data or standardize it?
 if (m_center) {
  m_centerFilter = new Center();
  m_centerFilter.setInputFormat(m_TrainInstances);
  m_TrainInstances = Filter.useFilter(m_TrainInstances, m_centerFilter);
 } else {
  m_standardizeFilter = new Standardize();
  m_standardizeFilter.setInputFormat(m_TrainInstances);
  m_TrainInstances = Filter.useFilter(m_TrainInstances, m_standardizeFilter);
 }
 // now compute the covariance matrix
 m_Correlation = new UpperSymmDenseMatrix(m_NumAttribs);
 for (int i = 0; i < m_NumAttribs; i++) {
  for (int j = i; j < m_NumAttribs; j++) {
   double cov = 0;
   for (Instance inst: m_TrainInstances) {
    cov += inst.value(i) * inst.value(j);
   }
   cov /= m_TrainInstances.numInstances() - 1;
   m_Correlation.set(i, j, cov);
  }
 }
}

protected void fillCovariance() throws Exception {
 // just center the data or standardize it?
 if (m_center) {
  m_centerFilter = new Center();
  m_centerFilter.setInputFormat(m_TrainInstances);
  m_TrainInstances = Filter.useFilter(m_TrainInstances, m_centerFilter);
 } else {
  m_standardizeFilter = new Standardize();
  m_standardizeFilter.setInputFormat(m_TrainInstances);
  m_TrainInstances = Filter.useFilter(m_TrainInstances, m_standardizeFilter);
 }
 // now compute the covariance matrix
 m_Correlation = new UpperSymmDenseMatrix(m_NumAttribs);
 for (int i = 0; i < m_NumAttribs; i++) {
  for (int j = i; j < m_NumAttribs; j++) {
   double cov = 0;
   for (Instance inst: m_TrainInstances) {
    cov += inst.value(i) * inst.value(j);
   }
   cov /= m_TrainInstances.numInstances() - 1;
   m_Correlation.set(i, j, cov);
  }
 }
}

 m_trainInstances = Filter.useFilter(m_trainInstances, m_centerFilter);
} else {
 m_standardizeFilter = new Standardize();
 m_standardizeFilter.setInputFormat(m_trainInstances);
 m_trainInstances = Filter.useFilter(m_trainInstances, m_standardizeFilter);

 m_Filter = new Standardize();
} else if (m_filterType == FILTER_NORMALIZE) {
 m_Filter = new Normalize();

 m_trainInstances = Filter.useFilter(m_trainInstances, m_centerFilter);
} else {
 m_standardizeFilter = new Standardize();
 m_standardizeFilter.setInputFormat(m_trainInstances);
 m_trainInstances = Filter.useFilter(m_trainInstances, m_standardizeFilter);

filter = new Standardize();
filter.setOptions(new String[]{"-unset-class-temporarily"});
filter.setInputFormat(data);

m_ClassStdDev = StrictMath.sqrt(instances.variance(instances
 .classIndex()));
m_Filter = new Standardize();
((Standardize) m_Filter).setIgnoreClass(true);
break;

m_Filter = new Standardize();
((Standardize)m_Filter).setIgnoreClass(true);
m_Filter.setInputFormat(instances);

m_Filter = new Standardize();
((Standardize)m_Filter).setIgnoreClass(true);
m_Filter.setInputFormat(instances);

m_Filter = new Standardize();
m_Filter.setInputFormat(insts);
insts = Filter.useFilter(insts, m_Filter);

m_Filter = new Standardize();
m_Filter.setInputFormat(insts);
insts = Filter.useFilter(insts, m_Filter);

m_Filter = new Standardize();
((Standardize) m_Filter).setIgnoreClass(true);
m_Filter.setInputFormat(insts);

m_Filter = new Standardize();
((Standardize) m_Filter).setIgnoreClass(true);
m_Filter.setInputFormat(insts);