How to use

Standardize

in

weka.filters.unsupervised.attribute

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

/**
 * Input an instance for filtering. Filter requires all
 * training instances be read before producing output.
 *
 * @param instance the input instance
 * @return true if the filtered instance may now be
 * collected with output().
 * @throws IllegalStateException if no input format has been set.
 */
public boolean input(Instance instance) throws Exception {
 if (getInputFormat() == null) {
  throw new IllegalStateException("No input instance format defined");
 }
 if (m_NewBatch) {
  resetQueue();
  m_NewBatch = false;
 }
 if (m_Means == null) {
  bufferInput(instance);
  return false;
 } else {
  convertInstance(instance);
  return true;
 }
}

 /**
  * 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);
 }
}

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

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_standardizeFilter.input(tempInst);
 m_standardizeFilter.batchFinished();
 tempInst = m_standardizeFilter.output();
} else {
 m_centerFilter.input(tempInst);

if (getInputFormat() == null) {
 throw new IllegalStateException("No input instance format defined");
 Instances input = getInputFormat();
 m_Means = new double[input.numAttributes()];
 m_StdDevs = new double[input.numAttributes()];
  convertInstance(input.instance(i));
flushInput();
return (numPendingOutput() != 0);

  if (instance.attribute(j).isNumeric() &&
      (!Utils.isMissingValue(vals[j])) &&
      (getInputFormat().classIndex() != j)) {
} else {
 double[] vals = instance.toDoubleArray();
 for (int j = 0; j < getInputFormat().numAttributes(); j++) {
  if (instance.attribute(j).isNumeric() &&
      (!Utils.isMissingValue(vals[j])) &&
      (getInputFormat().classIndex() != j)) {
push(inst, false); // No need to copy

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

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);
  }
 }
}

 /**
  * 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);
 }
}

 m_standardizeFilter.input(tempInst);
 m_standardizeFilter.batchFinished();
 tempInst = m_standardizeFilter.output();
} else {
 m_centerFilter.input(tempInst);

if (getInputFormat() == null) {
 throw new IllegalStateException("No input instance format defined");
 Instances input = getInputFormat();
 m_Means = new double[input.numAttributes()];
 m_StdDevs = new double[input.numAttributes()];
  convertInstance(input.instance(i));
flushInput();
return (numPendingOutput() != 0);

  if (instance.attribute(j).isNumeric() &&
      (!Utils.isMissingValue(vals[j])) &&
      (getInputFormat().classIndex() != j)) {
} else {
 double[] vals = instance.toDoubleArray();
 for (int j = 0; j < getInputFormat().numAttributes(); j++) {
  if (instance.attribute(j).isNumeric() &&
      (!Utils.isMissingValue(vals[j])) &&
      (getInputFormat().classIndex() != j)) {
push(inst, false); // No need to copy

 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

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

 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_standardizeFilter.input(tempInst);
 m_standardizeFilter.batchFinished();
 tempInst = m_standardizeFilter.output();
} else {
 m_centerFilter.input(tempInst);

/**
 * Input an instance for filtering. Filter requires all
 * training instances be read before producing output.
 *
 * @param instance the input instance
 * @return true if the filtered instance may now be
 * collected with output().
 * @throws IllegalStateException if no input format has been set.
 */
public boolean input(Instance instance) throws Exception {
 if (getInputFormat() == null) {
  throw new IllegalStateException("No input instance format defined");
 }
 if (m_NewBatch) {
  resetQueue();
  m_NewBatch = false;
 }
 if (m_Means == null) {
  bufferInput(instance);
  return false;
 } else {
  convertInstance(instance);
  return true;
 }
}

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