ELKI的加权核心谓词实现——一个例子

Question

我最近试图通过修改CorePrecdicate来在ELKI中实现加权DBSCAN的一个示例(例如，使用MinPointCorePrecdictate作为基础进行构建(，我只是想知道是否有人可以批评在这种情况下这是否是正确的实现：

import de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.*;
import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd;
import de.lmu.ifi.dbs.elki.data.Cluster;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.KMeansModel;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.StaticArrayDatabase;
import de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDRange;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.datasource.ArrayAdapterDatabaseConnection;
import de.lmu.ifi.dbs.elki.datasource.DatabaseConnection;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.LoggingConfiguration;
import de.lmu.ifi.dbs.elki.datasource.DatabaseConnection;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomlyChosenInitialMeans;
//import de.lmu.ifi.dbs.elki.math.random.RandomFactory;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.algorithm.clustering.DBSCAN;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.RadialBasisFunctionKernelFunction;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.EpsilonNeighborPredicate;
import de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.MinPtsCorePredicate;
import de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
// Imports for generalized dbscan
import de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.CorePredicate;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.utilities.ELKIServiceLoader;

public class SampleELKI2 {
public static void main(String[] args) {
double[][] data = new double[1000][3]; // The third column refers to the weights
for (int i = 0; i < data.length; i++) {
for (int j = 0; j < data[i].length; j++) {
data[i][j] = Math.random();
//System.out.println(i + " and " + j + " and " + data[i][j]);
}
//System.out.println(i + " and " + data[i][0] + " " + data[i][1] + " " + data[i][2]);
}
// Adapter to load data from an existing array
DatabaseConnection dbc = new ArrayAdapterDatabaseConnection(data);
//  Create a database (which may contain multiple relations
Database db = new StaticArrayDatabase(dbc, null);
// Load the data into the database (do NOT forget to initialize)
db.initialize();
// Relation containing the number vectors
Relation<NumberVector> rel = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);
// We know that the ids must be a continuous range
DBIDRange ids = (DBIDRange) rel.getDBIDs();
SquaredEuclideanDistanceFunction dist = SquaredEuclideanDistanceFunction.STATIC;
// Default initialization, using global random:
// To fix the random seed, use: new RandomFactory(seed);
// Compute the neighbourhood and core predicates here for generalized gdbscan
// --------------------------------------------------------------------- //
EpsilonNeighborPredicate ENP = new EpsilonNeighborPredicate(0.3, dist); // Generic Neighbourhoodpredicate
WeightedCorePredicate WCP = new WeightedCorePredicate(330, db, 2); // WeightedCorePredicate with the db  (db) and column index variable containing the weights (2)
// The Implementation of the predicates in the GDBSCAN - predicates can be replaced for conditionals
Clustering<Model> result = new GeneralizedDBSCAN(ENP, WCP, false).run(db);
int i = 0;
for (Cluster<Model> clu : result.getAllClusters()) {
for (DBIDIter it = clu.getIDs().iter(); it.valid(); it.advance()) {
NumberVector v = rel.get(it);
final int offset = ids.getOffset(it);
}
++i;
}
}
}

新的WeightedCorePrecdicate看起来像这样，它来自于对ELKI源文件中的MinPtCorePrecidite类的轻微修改。

public class WeightedCorePredicate implements CorePredicate<DBIDs> {
/**
* Class logger.
*/
public static final Logging LOG = Logging.getLogger(MinPtsCorePredicate.class);
/**
* The minpts parameter.
*/
protected int minpts;
static Database db;
static int WeightColumn;
static int WeightSum;
/**
* Default constructor.
*
* @param minpts Minimum number of neighbors to be a core point.
*/
public WeightedCorePredicate(int minpts, Database db, int WeightColumn) {
super();
this.minpts = minpts;
this.db = db;
this.WeightColumn = WeightColumn;
}
@Override
public Instance instantiate(Database database) {
return new Instance(minpts, db, WeightColumn);
}
@Override
public boolean acceptsType(SimpleTypeInformation<? extends DBIDs> type) {
return TypeUtil.DBIDS.isAssignableFromType(type) //
|| TypeUtil.NEIGHBORLIST.isAssignableFromType(type);
}
/**
* Instance for a particular data set.
*
* @author Erich Schubert
*/
public static class Instance implements CorePredicate.Instance<DBIDs> {
/**
* The minpts parameter.
*/
protected int minpts;
protected Database db;
protected int WeightColumn;
protected double WeightSum;
/**
* Constructor for this predicate.
*
* @param minpts MinPts parameter
*/
public Instance(int minpts, Database db, int WeightColumn) {
super();
this.minpts = minpts;
this.db = db;
this.WeightColumn = WeightColumn;
}
@Override
public boolean isCorePoint(DBIDRef point, DBIDs neighbors) {
db.initialize(); // Initialize database 
Relation<NumberVector> rel = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD); // db relation to get to the datapoints 
WeightSum = 0; // Make sure to initialize the weights as 0 
// DBIDS contain the indices of the points - so just need a database relation to access the points at the index 
for (DBIDIter it = neighbors.iter(); it.valid(); it.advance()) {
//    System.out.print("The weights are " + rel.get(it).doubleValue(WeightColumn) + "n");
WeightSum += rel.get(it).doubleValue(WeightColumn); // Sum the weights 
}
return WeightSum >= minpts;
}
}

/**
* Parameterization class
*
* @author Erich Schubert
*/
public static class Parameterizer extends AbstractParameterizer {
/**
* Minpts value
*/
protected int minpts;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
// Get the minpts parameter
IntParameter minptsP = new IntParameter(DBSCAN.Parameterizer.MINPTS_ID) //
.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
if(config.grab(minptsP)) {
minpts = minptsP.intValue();
if(minpts <= 2) {
LOG.warning("DBSCAN with minPts <= 2 is equivalent to single-link clustering at a single height. Consider using larger values of minPts.");
}
}
}
@Override
protected WeightedCorePredicate makeInstance() {
return new WeightedCorePredicate(minpts, db, WeightColumn);
}
}
}

从本质上讲，我在WeightedCorePrecdicate中添加了输入，它引用了数据库，我可以使用索引从rel和this中挑选出数据库的元素。WeightColumn，选择权重与X/Y列一起列出的列。这源于这里的讨论：Elki GDBSCAN Java/Scala-如何修改DBSCAN的Elki实现中的CorePrecdicate和sample_weight选项。

如有任何反馈，我们将不胜感激。我不是Java背景，主要来自Python/Scala，所以我完全明白这不是最优雅的Java代码。

感谢

Answer 1

对于应该是局部变量或实例变量的东西，不要使用static变量！

他们在99.99%的情况下都是错误的

C.f.，为什么静态变量被认为是邪恶的？