standardize.java

来自「Weka」· Java 代码 · 共 361 行

/*
 *    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.
 */

/*
 *    Standardize.java
 *    Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.attribute;

import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Sourcable;
import weka.filters.UnsupervisedFilter;

/** 
 <!-- globalinfo-start -->
 * Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (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 ([e-mail]) 
 * @version $Revision: 1.11 $
 */
public class Standardize 
  extends PotentialClassIgnorer 
  implements UnsupervisedFilter, Sourcable {
  
  /** for serialization */
  static final long serialVersionUID = -6830769026855053281L;

  /** The means */
  private double [] m_Means;
  
  /** The variances */
  private double [] m_StdDevs;

  /**
   * Returns a string describing this filter
   *
   * @return a description of the filter suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {

    return "Standardizes all numeric attributes in the given dataset "
      + "to have zero mean and unit variance (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();

    // 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 = m_StdDevs = 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) 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;
    }
  }

  /**
   * 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
   * @exception Exception if an error occurs
   * @exception IllegalStateException if no input structure has been defined
   */
  public boolean batchFinished() throws Exception {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }
    if (m_Means == null) {
      Instances input = getInputFormat();
      m_Means = new double[input.numAttributes()];
      m_StdDevs = 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);
	  m_StdDevs[i] = Math.sqrt(input.variance(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
   * @exception Exception if an error occurs
   */
  private void convertInstance(Instance instance) throws Exception {
  
    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() &&
	    (!Instance.isMissingValue(vals[j])) &&
	    (getInputFormat().classIndex() != j)) {
	  
	  // Just subtract the mean if the standard deviation is zero
	  if (m_StdDevs[j] > 0) { 
	    value = (vals[j] - m_Means[j]) / m_StdDevs[j];
	  } else {
	    value = vals[j] - m_Means[j];
	  }
          if (Double.isNaN(value)) {
            throw new Exception("A NaN value was generated "
                                + "while standardizing attribute " 
                                + instance.attribute(j).name());
          }
	  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() &&
	    (!Instance.isMissingValue(vals[j])) &&
	    (getInputFormat().classIndex() != j)) {
	  
	  // Just subtract the mean if the standard deviation is zero
	  if (m_StdDevs[j] > 0) { 
	    vals[j] = (vals[j] - m_Means[j]) / m_StdDevs[j];
	  } else {
	    vals[j] = (vals[j] - m_Means[j]);
	  }
          if (Double.isNaN(vals[j])) {
            throw new Exception("A NaN value was generated "
                                + "while standardizing attribute " 
                                + instance.attribute(j).name());
          }
	}
      }	
      inst = new Instance(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("  /** the computed standard deviations */\n");
    result.append("  protected final static double[] STDEVS = new double[]{" + Utils.arrayToString(m_StdDevs) + "};\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("        if (STDEVS[n] > 0)\n");
    result.append("          result[n] = (((Double) i[n]) - MEANS[n]) / STDEVS[n];\n");
    result.append("        else\n");
    result.append("          result[n] = ((Double) i[n]) - MEANS[n];\n");
    result.append("      }\n");
    result.append("      else {\n");
    result.append("        result[n] = i[n];\n");
    result.append("      }\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();
  }

  /**
   * 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);
  }
}