weka.filters.unsupervised.attribute.Center java code examples

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}

/**
 * default constructor
 */
public PLSFilter() {
 super();
 // setup pre-processing
 m_Missing = new ReplaceMissingValues();
 m_Filter = new Center();
}

/**
 * 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) {
 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;
 }
}

/**
 * Signify that this batch of input to the filter is finished. 
 * If the filter requires all instances prior to filtering,
 * output() may now be called to retrieve the filtered instances.
 *
 * @return true             if there are instances pending output
 * @throws IllegalStateException     if no input structure has been defined
 */
public boolean batchFinished() {
 if (getInputFormat() == null)
  throw new IllegalStateException("No input instance format defined");
 if (m_Means == null) {
  Instances input = getInputFormat();
  m_Means = new double[input.numAttributes()];
  for (int i = 0; i < input.numAttributes(); i++) {
   if (input.attribute(i).isNumeric() &&
       (input.classIndex() != i)) {
    m_Means[i] = input.meanOrMode(i);
   }
  }
  // Convert pending input instances
  for (int i = 0; i < input.numInstances(); i++)
   convertInstance(input.instance(i));
 }
 // Free memory
 flushInput();
 m_NewBatch = true;
 return (numPendingOutput() != 0);
}

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

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);
  }
 }
}

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

 m_ClassMean = instances.meanOrMode(instances.classIndex());
 m_ClassStdDev = 1;
 m_Filter = new Center();
 ((Center) m_Filter).setIgnoreClass(true);
 break;
case PREPROCESSING_STANDARDIZE:

  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)) {
   vals[j] = (vals[j] - m_Means[j]);
push(inst, false); // No need to copy instance

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);
  }
 }
}

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

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

  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)) {
   vals[j] = (vals[j] - m_Means[j]);
push(inst, false); // No need to copy instance

/** Creates a default Center */
public Filter getFilter() {
 return new Center();
}

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

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

/**
 * Signify that this batch of input to the filter is finished. 
 * If the filter requires all instances prior to filtering,
 * output() may now be called to retrieve the filtered instances.
 *
 * @return true             if there are instances pending output
 * @throws IllegalStateException     if no input structure has been defined
 */
public boolean batchFinished() {
 if (getInputFormat() == null)
  throw new IllegalStateException("No input instance format defined");
 if (m_Means == null) {
  Instances input = getInputFormat();
  m_Means = new double[input.numAttributes()];
  for (int i = 0; i < input.numAttributes(); i++) {
   if (input.attribute(i).isNumeric() &&
       (input.classIndex() != i)) {
    m_Means[i] = input.meanOrMode(i);
   }
  }
  // Convert pending input instances
  for (int i = 0; i < input.numInstances(); i++)
   convertInstance(input.instance(i));
 }
 // Free memory
 flushInput();
 m_NewBatch = true;
 return (numPendingOutput() != 0);
}

/**
 * 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) {
 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 a default Center */
public Filter getFilter() {
 return new Center();
}

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

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

Javadoc

Centers all numeric attributes in the given dataset to have zero mean (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)

Most used methods

<init>
batchFinished
Signify that this batch of input to the filter is finished. If the filter requires all instances pri
bufferInput
convertInstance
Convert a single instance over. The converted instance is added to the end of the output queue.
flushInput
getInputFormat
input
Input an instance for filtering. Filter requires all training instances be read before producing out
numPendingOutput
output
push
resetQueue
runFilter

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How to useCenter in weka.filters.unsupervised.attribute

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weka.filters.unsupervised.attribute