Java tutorial
/* * 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 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 General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. */ /* * EuclideanDistance.java * Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand * */ package weka.core; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.core.neighboursearch.PerformanceStats; /** <!-- globalinfo-start --> * Implementing Euclidean distance (or similarity) function.<br/> * <br/> * One object defines not one distance but the data model in which the distances between objects of that data model can be computed.<br/> * <br/> * Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.<br/> * <br/> * For more information, see:<br/> * <br/> * Wikipedia. Euclidean distance. URL http://en.wikipedia.org/wiki/Euclidean_distance. * <p/> <!-- globalinfo-end --> * <!-- technical-bibtex-start --> * BibTeX: * <pre> * @misc{missing_id, * author = {Wikipedia}, * title = {Euclidean distance}, * URL = {http://en.wikipedia.org/wiki/Euclidean_distance} * } * </pre> * <p/> <!-- technical-bibtex-end --> * <!-- options-start --> * Valid options are: <p/> * * <pre> -D * Turns off the normalization of attribute * values in distance calculation.</pre> * * <pre> -R <col1,col2-col4,...> * Specifies list of columns to used in the calculation of the * distance. 'first' and 'last' are valid indices. * (default: first-last)</pre> * * <pre> -V * Invert matching sense of column indices.</pre> * <!-- options-end --> * * @author Gabi Schmidberger (gabi@cs.waikato.ac.nz) * @author Ashraf M. Kibriya (amk14@cs.waikato.ac.nz) * @author FracPete (fracpete at waikato dot ac dot nz) * @version $Revision$ */ public class EuclideanDistance extends NormalizableDistance implements Cloneable, TechnicalInformationHandler { /** for serialization. */ private static final long serialVersionUID = 1068606253458807903L; /** * Constructs an Euclidean Distance object, Instances must be still set. */ public EuclideanDistance() { super(); } /** * Constructs an Euclidean Distance object and automatically initializes the * ranges. * * @param data the instances the distance function should work on */ public EuclideanDistance(Instances data) { super(data); } /** * Returns a string describing this object. * * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Implementing Euclidean distance (or similarity) function.\n\n" + "One object defines not one distance but the data model in which " + "the distances between objects of that data model can be computed.\n\n" + "Attention: For efficiency reasons the use of consistency checks " + "(like are the data models of the two instances exactly the same), " + "is low.\n\n" + "For more information, see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.MISC); result.setValue(Field.AUTHOR, "Wikipedia"); result.setValue(Field.TITLE, "Euclidean distance"); result.setValue(Field.URL, "http://en.wikipedia.org/wiki/Euclidean_distance"); return result; } /** * Calculates the distance between two instances. * * @param first the first instance * @param second the second instance * @return the distance between the two given instances */ public double distance(Instance first, Instance second) { return Math.sqrt(distance(first, second, Double.POSITIVE_INFINITY)); } /** * Calculates the distance (or similarity) between two instances. Need to * pass this returned distance later on to postprocess method to set it on * correct scale. <br/> * P.S.: Please don't mix the use of this function with * distance(Instance first, Instance second), as that already does post * processing. Please consider passing Double.POSITIVE_INFINITY as the cutOffValue to * this function and then later on do the post processing on all the * distances. * * @param first the first instance * @param second the second instance * @param stats the structure for storing performance statistics. * @return the distance between the two given instances or * Double.POSITIVE_INFINITY. */ public double distance(Instance first, Instance second, PerformanceStats stats) { //debug method pls remove after use return Math.sqrt(distance(first, second, Double.POSITIVE_INFINITY, stats)); } /** * Updates the current distance calculated so far with the new difference * between two attributes. The difference between the attributes was * calculated with the difference(int,double,double) method. * * @param currDist the current distance calculated so far * @param diff the difference between two new attributes * @return the update distance * @see #difference(int, double, double) */ protected double updateDistance(double currDist, double diff) { double result; result = currDist; result += diff * diff; return result; } /** * Does post processing of the distances (if necessary) returned by * distance(distance(Instance first, Instance second, double cutOffValue). It * is necessary to do so to get the correct distances if * distance(distance(Instance first, Instance second, double cutOffValue) is * used. This is because that function actually returns the squared distance * to avoid inaccuracies arising from floating point comparison. * * @param distances the distances to post-process */ public void postProcessDistances(double distances[]) { for (int i = 0; i < distances.length; i++) { distances[i] = Math.sqrt(distances[i]); } } /** * Returns the squared difference of two values of an attribute. * * @param index the attribute index * @param val1 the first value * @param val2 the second value * @return the squared difference */ public double sqDifference(int index, double val1, double val2) { double val = difference(index, val1, val2); return val * val; } /** * Returns value in the middle of the two parameter values. * * @param ranges the ranges to this dimension * @return the middle value */ public double getMiddle(double[] ranges) { double middle = ranges[R_MIN] + ranges[R_WIDTH] * 0.5; return middle; } /** * Returns the index of the closest point to the current instance. * Index is index in Instances object that is the second parameter. * * @param instance the instance to assign a cluster to * @param allPoints all points * @param pointList the list of points * @return the index of the closest point * @throws Exception if something goes wrong */ public int closestPoint(Instance instance, Instances allPoints, int[] pointList) throws Exception { double minDist = Integer.MAX_VALUE; int bestPoint = 0; for (int i = 0; i < pointList.length; i++) { double dist = distance(instance, allPoints.instance(pointList[i]), Double.POSITIVE_INFINITY); if (dist < minDist) { minDist = dist; bestPoint = i; } } return pointList[bestPoint]; } /** * Returns true if the value of the given dimension is smaller or equal the * value to be compared with. * * @param instance the instance where the value should be taken of * @param dim the dimension of the value * @param value the value to compare with * @return true if value of instance is smaller or equal value */ public boolean valueIsSmallerEqual(Instance instance, int dim, double value) { //This stays return instance.value(dim) <= value; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision$"); } }