package de.lmu.ifi.dbs.elki.datasource.filter.normalization;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
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 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.math.linearalgebra.LinearEquationSystem;
import de.lmu.ifi.dbs.elki.utilities.FormatUtil;
import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.ArrayLikeUtil;
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.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 minimum and maximum in each dimension.
*
* @author Elke Achtert
* @param <V> vector type
*
* @apiviz.uses NumberVector
*/
// TODO: extract superclass AbstractAttributeWiseNormalization
public class AttributeWiseMinMaxNormalization<V extends NumberVector<V, ?>> extends AbstractNormalization<V> {
/**
* Parameter for minimum.
*/
public static final OptionID MINIMA_ID = OptionID.getOrCreateOptionID("normalize.min", "a comma separated concatenation of the minimum values in each dimension that are mapped to 0. If no value is specified, the minimum value of the attribute range in this dimension will be taken.");
/**
* Parameter for maximum.
*/
public static final OptionID MAXIMA_ID = OptionID.getOrCreateOptionID("normalize.max", "a comma separated concatenation of the maximum values in each dimension that are mapped to 1. If no value is specified, the maximum value of the attribute range in this dimension will be taken.");
/**
* Stores the maximum in each dimension.
*/
private double[] maxima = new double[0];
/**
* Stores the minimum in each dimension.
*/
private double[] minima = new double[0];
/**
* Constructor.
*
* @param minima Minimum values
* @param maxima Maximum values
*/
public AttributeWiseMinMaxNormalization(double[] minima, double[] maxima) {
super();
this.minima = minima;
this.maxima = maxima;
}
@Override
protected boolean prepareStart(SimpleTypeInformation<V> in) {
return (minima.length == 0 || maxima.length == 0);
}
@Override
protected void prepareProcessInstance(V featureVector) {
// First object? Then initialize.
if(minima.length == 0 || maxima.length == 0) {
int dimensionality = featureVector.getDimensionality();
minima = new double[dimensionality];
maxima = new double[dimensionality];
for(int i = 0; i < dimensionality; i++) {
maxima[i] = -Double.MAX_VALUE;
minima[i] = Double.MAX_VALUE;
}
}
if(minima.length != featureVector.getDimensionality()) {
throw new IllegalArgumentException("FeatureVectors differ in length.");
}
for(int d = 1; d <= featureVector.getDimensionality(); d++) {
final double val = featureVector.doubleValue(d);
if(val > maxima[d - 1]) {
maxima[d - 1] = val;
}
if(val < minima[d - 1]) {
minima[d - 1] = val;
}
}
}
@Override
protected V filterSingleObject(V featureVector) {
double[] values = new double[featureVector.getDimensionality()];
for(int d = 1; d <= featureVector.getDimensionality(); d++) {
values[d - 1] = (featureVector.doubleValue(d) - minima[d - 1]) / factor(d);
}
return featureVector.newNumberVector(values);
}
@Override
public V restore(V featureVector) throws NonNumericFeaturesException {
if(featureVector.getDimensionality() == maxima.length && featureVector.getDimensionality() == minima.length) {
double[] values = new double[featureVector.getDimensionality()];
for(int d = 1; d <= featureVector.getDimensionality(); d++) {
values[d - 1] = (featureVector.doubleValue(d) * (factor(d)) + minima[d - 1]);
}
return featureVector.newNumberVector(values);
}
else {
throw new NonNumericFeaturesException("Attributes cannot be resized: current dimensionality: " + featureVector.getDimensionality() + " former dimensionality: " + maxima.length);
}
}
/**
* Returns a factor for normalization in a certain dimension.
* <p/>
* The provided factor is the maximum-minimum in the specified dimension, if
* these two values differ, otherwise it is the maximum if this value differs
* from 0, otherwise it is 1.
*
* @param dimension the dimension to get a factor for normalization
* @return a factor for normalization in a certain dimension
*/
private double factor(int dimension) {
return maxima[dimension - 1] != minima[dimension - 1] ? maxima[dimension - 1] - minima[dimension - 1] : maxima[dimension - 1] != 0 ? maxima[dimension - 1] : 1;
}
@Override
public LinearEquationSystem transform(LinearEquationSystem linearEquationSystem) {
double[][] coeff = linearEquationSystem.getCoefficents();
double[] rhs = linearEquationSystem.getRHS();
int[] row = linearEquationSystem.getRowPermutations();
int[] col = linearEquationSystem.getColumnPermutations();
// noinspection ForLoopReplaceableByForEach
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 += minima[c] * coeff[row[r]][col[c]] / factor(c + 1);
coeff[row[r]][col[c]] = coeff[row[r]][col[c]] / factor(c + 1);
}
rhs[row[r]] = rhs[row[r]] + sum;
}
}
LinearEquationSystem lq = new LinearEquationSystem(coeff, rhs, row, col);
return lq;
}
@Override
public String toString() {
StringBuffer result = new StringBuffer();
result.append("normalization class: ").append(getClass().getName());
result.append("\n");
result.append("normalization minima: ").append(FormatUtil.format(minima));
result.append("\n");
result.append("normalization maxima: ").append(FormatUtil.format(maxima));
return result.toString();
}
@Override
protected SimpleTypeInformation<? super V> getInputTypeRestriction() {
return TypeUtil.NUMBER_VECTOR_FIELD;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<V extends NumberVector<V, ?>> extends AbstractParameterizer {
/**
* Stores the maximum in each dimension.
*/
private double[] maxima = new double[0];
/**
* Stores the minimum in each dimension.
*/
private double[] minima = new double[0];
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
DoubleListParameter minimaP = new DoubleListParameter(MINIMA_ID, true);
if(config.grab(minimaP)) {
minima = ArrayLikeUtil.toPrimitiveDoubleArray(minimaP.getValue());
}
DoubleListParameter maximaP = new DoubleListParameter(MAXIMA_ID, true);
if(config.grab(maximaP)) {
maxima = ArrayLikeUtil.toPrimitiveDoubleArray(maximaP.getValue());
}
ArrayList<Parameter<?, ?>> global_1 = new ArrayList<Parameter<?, ?>>();
global_1.add(minimaP);
global_1.add(maximaP);
config.checkConstraint(new AllOrNoneMustBeSetGlobalConstraint(global_1));
ArrayList<ListParameter<?>> global = new ArrayList<ListParameter<?>>();
global.add(minimaP);
global.add(maximaP);
config.checkConstraint(new EqualSizeGlobalConstraint(global));
}
@Override
protected AttributeWiseMinMaxNormalization<V> makeInstance() {
return new AttributeWiseMinMaxNormalization<V>(minima, maxima);
}
}
}