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Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java')
| -rw-r--r-- | src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java | 242 |
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diff --git a/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java b/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java new file mode 100644 index 00000000..4cf3c606 --- /dev/null +++ b/src/de/lmu/ifi/dbs/elki/datasource/filter/normalization/AttributeWiseMinMaxNormalization.java @@ -0,0 +1,242 @@ +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()]; + if(minima.length != featureVector.getDimensionality()) { + throw new IllegalArgumentException("FeatureVectors and given Minima/Maxima differ in length."); + } + 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); + } + } +}
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