content: /*
   *    This program is free software; you can redistribute it and/or modify
   *    it under the terms of the GNU General Public License as published by
   *    the Free Software Foundation; either version 2 of the License, or
   *    (at your option) any later version.
   *
   *    This program is distributed in the hope that it will be useful,
   *    but WITHOUT ANY WARRANTY; without even the implied warranty of
   *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *    GNU General Public License for more details.
   *
   *    You should have received a copy of the GNU General Public License
   *    along with this program; if not, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   * Center.java
   * Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.filters.unsupervised.attribute;
  
  import weka.core.Capabilities;
  import weka.core.Instance; 
  import weka.core.DenseInstance;
  import weka.core.Instances;
  import weka.core.RevisionUtils;
  import weka.core.SparseInstance;
  import weka.core.Utils;
  import weka.core.Capabilities.Capability;
  import weka.filters.Sourcable;
  import weka.filters.UnsupervisedFilter;
  
  /** 
   <!-- globalinfo-start -->
   * Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -unset-class-temporarily
   *  Unsets the class index temporarily before the filter is
   *  applied to the data.
   *  (default: no)</pre>
   * 
   <!-- options-end -->
   * 
   * @author Eibe Frank (eibe@cs.waikato.ac.nz) 
   * @author FracPete (fracpete at waikato dot ac dot nz) 
   * @version $Revision: 5987 $
   */
  public class Center 
    extends PotentialClassIgnorer 
    implements UnsupervisedFilter, Sourcable {
  
    /** for serialization */
    private static final long serialVersionUID = -9101338448900581023L;
    
    /** The means */
    private double[] m_Means;
  
    /**
     * Returns a string describing this filter
     *
     * @return 		a description of the filter suitable for
     * 			displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
  
      return "Centers all numeric attributes in the given dataset "
        + "to have zero mean (apart from the class attribute, if set).";
    }
  
    /** 
     * Returns the Capabilities of this filter.
     *
     * @return            the capabilities of this object
     * @see               Capabilities
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
  
      // attributes
      result.enableAllAttributes();
      result.enable(Capability.MISSING_VALUES);
      
      // class
      result.enableAllClasses();
      result.enable(Capability.MISSING_CLASS_VALUES);
      result.enable(Capability.NO_CLASS);
      
      return result;
    }
  
    /**
     * Sets the format of the input instances.
     *
     * @param instanceInfo 	an Instances object containing the input 
     * 				instance structure (any instances contained 
     * 				in the object are ignored - only the structure 
     * 				is required).
     * @return true 		if the outputFormat may be collected immediately
     * @throws Exception 		if the input format can't be set successfully
     */
    public boolean setInputFormat(Instances instanceInfo) throws Exception {
      super.setInputFormat(instanceInfo);
      setOutputFormat(instanceInfo);
      m_Means = null;
      return true;
    }
  
    /**
     * 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);
    }
  
    /**
     * Convert a single instance over. The converted instance is 
     * added to the end of the output queue.
     *
     * @param instance 	the instance to convert
     */
    private void convertInstance(Instance instance) {
      Instance inst = null;
      
      if (instance instanceof SparseInstance) {
        double[] newVals = new double[instance.numAttributes()];
        int[] newIndices = new int[instance.numAttributes()];
        double[] vals = instance.toDoubleArray();
        int ind = 0;
        for (int j = 0; j < instance.numAttributes(); j++) {
  	double value;
  	if (instance.attribute(j).isNumeric() &&
  	    (!Utils.isMissingValue(vals[j])) &&
  	    (getInputFormat().classIndex() != j)) {
  	  
  	  value = vals[j] - m_Means[j];
  	  if (value != 0.0) {
  	    newVals[ind] = value;
  	    newIndices[ind] = j;
  	    ind++;
  	  }
  	} else {
  	  value = vals[j];
  	  if (value != 0.0) {
  	    newVals[ind] = value;
  	    newIndices[ind] = j;
  	    ind++;
  	  }
  	}
        }	
        double[] tempVals = new double[ind];
        int[] tempInd = new int[ind];
        System.arraycopy(newVals, 0, tempVals, 0, ind);
        System.arraycopy(newIndices, 0, tempInd, 0, ind);
        inst = new SparseInstance(instance.weight(), tempVals, tempInd,
                                  instance.numAttributes());
      } 
      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]);
  	}
        }	
        inst = new DenseInstance(instance.weight(), vals);
      }
      
      inst.setDataset(instance.dataset());
      
      push(inst);
    }
    
    /**
     * Returns a string that describes the filter as source. The
     * filter will be contained in a class with the given name (there may
     * be auxiliary classes),
     * and will contain two methods with these signatures:
     * <pre><code>
     * // converts one row
     * public static Object[] filter(Object[] i);
     * // converts a full dataset (first dimension is row index)
     * public static Object[][] filter(Object[][] i);
     * </code></pre>
     * where the array <code>i</code> contains elements that are either
     * Double, String, with missing values represented as null. The generated
     * code is public domain and comes with no warranty.
     *
     * @param className   the name that should be given to the source class.
     * @param data	the dataset used for initializing the filter
     * @return            the object source described by a string
     * @throws Exception  if the source can't be computed
     */
    public String toSource(String className, Instances data) throws Exception {
      StringBuffer        result;
      boolean[]		process;
      int			i;
      
      result = new StringBuffer();
      
      // determine what attributes were processed
      process = new boolean[data.numAttributes()];
      for (i = 0; i < data.numAttributes(); i++) {
        process[i] = (data.attribute(i).isNumeric() && (i != data.classIndex()));
      }
      
      result.append("class " + className + " {\n");
      result.append("\n");
      result.append("  /** lists which attributes will be processed */\n");
      result.append("  protected final static boolean[] PROCESS = new boolean[]{" + Utils.arrayToString(process) + "};\n");
      result.append("\n");
      result.append("  /** the computed means */\n");
      result.append("  protected final static double[] MEANS = new double[]{" + Utils.arrayToString(m_Means) + "};\n");
      result.append("\n");
      result.append("  /**\n");
      result.append("   * filters a single row\n");
      result.append("   * \n");
      result.append("   * @param i the row to process\n");
      result.append("   * @return the processed row\n");
      result.append("   */\n");
      result.append("  public static Object[] filter(Object[] i) {\n");
      result.append("    Object[] result;\n");
      result.append("\n");
      result.append("    result = new Object[i.length];\n");
      result.append("    for (int n = 0; n < i.length; n++) {\n");
      result.append("      if (PROCESS[n] && (i[n] != null))\n");
      result.append("        result[n] = ((Double) i[n]) - MEANS[n];\n");
      result.append("      else\n");
      result.append("        result[n] = i[n];\n");
      result.append("    }\n");
      result.append("\n");
      result.append("    return result;\n");
      result.append("  }\n");
      result.append("\n");
      result.append("  /**\n");
      result.append("   * filters multiple rows\n");
      result.append("   * \n");
      result.append("   * @param i the rows to process\n");
      result.append("   * @return the processed rows\n");
      result.append("   */\n");
      result.append("  public static Object[][] filter(Object[][] i) {\n");
      result.append("    Object[][] result;\n");
      result.append("\n");
      result.append("    result = new Object[i.length][];\n");
      result.append("    for (int n = 0; n < i.length; n++) {\n");
      result.append("      result[n] = filter(i[n]);\n");
      result.append("    }\n");
      result.append("\n");
      result.append("    return result;\n");
      result.append("  }\n");
      result.append("}\n");
      
      return result.toString();
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5987 $");
    }
  
    /**
     * 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);
    }
  }
  