path: root/src/de/lmu/ifi/dbs/elki/datasource/filter/AttributeWiseVarianceNormalization.java

package de.lmu.ifi.dbs.elki.datasource.filter;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures

Copyright (C) 2011
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 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 Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/

import java.util.ArrayList;
import java.util.List;

import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.math.linearalgebra.LinearEquationSystem;
import de.lmu.ifi.dbs.elki.utilities.FormatUtil;
import de.lmu.ifi.dbs.elki.utilities.Util;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.WrongParameterValueException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.AllOrNoneMustBeSetGlobalConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.EqualSizeGlobalConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleListParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ListParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Parameter;

/**
 * Class to perform and undo a normalization on real vectors with respect to
 * given mean and standard deviation in each dimension.
 * 
 * @author Erich Schubert
 * @param <V> vector type
 */
// TODO: extract superclass AbstractAttributeWiseNormalization
public class AttributeWiseVarianceNormalization<V extends NumberVector<V, ?>> extends AbstractNormalization<V> {
  /**
   * Class logger.
   */
  public static final Logging logger = Logging.getLogger(AttributeWiseVarianceNormalization.class);

  /**
   * Parameter for means.
   */
  public static final OptionID MEAN_ID = OptionID.getOrCreateOptionID("normalize.mean", "a comma separated concatenation of the mean values in each dimension that are mapped to 0. If no value is specified, the mean value of the attribute range in this dimension will be taken.");

  /**
   * Parameter for stddevs.
   */
  public static final OptionID STDDEV_ID = OptionID.getOrCreateOptionID("normalize.stddev", "a comma separated concatenation of the standard deviations in each dimension that are scaled to 1. If no value is specified, the standard deviation of the attribute range in this dimension will be taken.");

  /**
   * Stores the mean in each dimension.
   */
  private double[] mean = new double[0];

  /**
   * Stores the standard deviation in each dimension.
   */
  private double[] stddev = new double[0];

  /**
   * Temporary storage used during initialization.
   */
  MeanVariance[] mvs = null;

  /**
   * Constructor.
   * 
   * @param mean Mean value
   * @param stddev Standard deviation
   */
  public AttributeWiseVarianceNormalization(double[] mean, double[] stddev) {
    super();
    this.mean = mean;
    this.stddev = stddev;
  }

  @Override
  protected boolean prepareStart(@SuppressWarnings("unused") SimpleTypeInformation<V> in) {
    return (mean.length == 0 || stddev.length == 0);
  }

  @Override
  protected void prepareProcessInstance(V featureVector) {
    // First object? Then init. (We didn't have a dimensionality before!)
    if(mvs == null) {
      int dimensionality = featureVector.getDimensionality();
      mvs = MeanVariance.newArray(dimensionality);
    }
    for(int d = 1; d <= featureVector.getDimensionality(); d++) {
      mvs[d - 1].put(featureVector.doubleValue(d));
    }
  }

  @Override
  protected void prepareComplete() {
    StringBuffer buf = logger.isVerbose() ? new StringBuffer() : null;
    final int dimensionality = mvs.length;
    mean = new double[dimensionality];
    stddev = new double[dimensionality];
    if(buf != null) {
      buf.append("Normalization parameters: ");
    }
    for(int d = 0; d < dimensionality; d++) {
      mean[d] = mvs[d].getMean();
      stddev[d] = mvs[d].getSampleStddev();
      if(stddev[d] == 0 || Double.isNaN(stddev[d])) {
        stddev[d] = 1.0;
      }
      if(buf != null) {
        buf.append(" m: ").append(mean[d]).append(" v: ").append(stddev[d]);
      }
    }
    mvs = null;
    if(buf != null) {
      logger.debugFine(buf.toString());
    }
  }

  @Override
  protected V filterSingleObject(V featureVector) {
    double[] values = new double[featureVector.getDimensionality()];
    for(int d = 1; d <= featureVector.getDimensionality(); d++) {
      values[d - 1] = normalize(d - 1, featureVector.doubleValue(d));
    }
    return featureVector.newInstance(values);
  }

  @Override
  public V restore(V featureVector) throws NonNumericFeaturesException {
    if(featureVector.getDimensionality() == mean.length) {
      double[] values = new double[featureVector.getDimensionality()];
      for(int d = 1; d <= featureVector.getDimensionality(); d++) {
        values[d - 1] = restore(d - 1, featureVector.doubleValue(d));
      }
      return featureVector.newInstance(values);
    }
    else {
      throw new NonNumericFeaturesException("Attributes cannot be resized: current dimensionality: " + featureVector.getDimensionality() + " former dimensionality: " + mean.length);
    }
  }

  private double normalize(int d, double val) {
    return (val - mean[d]) / stddev[d];
  }

  private double restore(int d, double val) {
    return (val * stddev[d]) + mean[d];
  }

  @Override
  public LinearEquationSystem transform(LinearEquationSystem linearEquationSystem) {
    double[][] coeff = linearEquationSystem.getCoefficents();
    double[] rhs = linearEquationSystem.getRHS();
    int[] row = linearEquationSystem.getRowPermutations();
    int[] col = linearEquationSystem.getColumnPermutations();

    for(int i = 0; i < coeff.length; i++) {
      for(int r = 0; r < coeff.length; r++) {
        double sum = 0.0;
        for(int c = 0; c < coeff[0].length; c++) {
          sum += mean[c] * coeff[row[r]][col[c]] / stddev[c];
          coeff[row[r]][col[c]] = coeff[row[r]][col[c]] / stddev[c];
        }
        rhs[row[r]] = rhs[row[r]] + sum;
      }
    }

    LinearEquationSystem lq = new LinearEquationSystem(coeff, rhs, row, col);
    return lq;
  }

  @Override
  protected SimpleTypeInformation<? super V> getInputTypeRestriction() {
    return TypeUtil.NUMBER_VECTOR_FIELD;
  }

  @Override
  public String toString() {
    StringBuffer result = new StringBuffer();
    result.append("normalization class: ").append(getClass().getName());
    result.append("\n");
    result.append("normalization means: ").append(FormatUtil.format(mean));
    result.append("\n");
    result.append("normalization stddevs: ").append(FormatUtil.format(stddev));

    return result.toString();
  }

  /**
   * Parameterization class.
   * 
   * @author Erich Schubert
   * 
   * @apiviz.exclude
   */
  public static class Parameterizer<V extends NumberVector<V, ?>> extends AbstractParameterizer {
    /**
     * Stores the mean in each dimension.
     */
    private double[] mean = new double[0];

    /**
     * Stores the standard deviation in each dimension.
     */
    private double[] stddev = new double[0];

    @Override
    protected void makeOptions(Parameterization config) {
      super.makeOptions(config);
      DoubleListParameter meanP = new DoubleListParameter(MEAN_ID, true);
      DoubleListParameter stddevP = new DoubleListParameter(STDDEV_ID, true);
      config.grab(meanP);
      config.grab(stddevP);
      // Note: grab first, then use isDefined, to ensure the stddev is grabbed.
      if(meanP.isDefined() && stddevP.isDefined()) {
        List<Double> mean_list = meanP.getValue();
        List<Double> stddev_list = stddevP.getValue();

        mean = Util.unbox(mean_list.toArray(new Double[mean_list.size()]));
        stddev = Util.unbox(stddev_list.toArray(new Double[stddev_list.size()]));

        for(double d : stddev) {
          if(d == 0) {
            config.reportError(new WrongParameterValueException("Standard deviations must not be 0."));
          }
        }
      }

      ArrayList<Parameter<?, ?>> global_1 = new ArrayList<Parameter<?, ?>>();
      global_1.add(meanP);
      global_1.add(stddevP);
      config.checkConstraint(new AllOrNoneMustBeSetGlobalConstraint(global_1));

      ArrayList<ListParameter<?>> global = new ArrayList<ListParameter<?>>();
      global.add(meanP);
      global.add(stddevP);
      config.checkConstraint(new EqualSizeGlobalConstraint(global));
    }

    @Override
    protected AttributeWiseVarianceNormalization<V> makeInstance() {
      return new AttributeWiseVarianceNormalization<V>(mean, stddev);
    }
  }
}