XMeans.java
| Index Score | ||
|---|---|---|
 |
 |
weka.clusterers |
 |
 |
Weka |
View: Reasons, Metrics, Source Code
These are the metrics that contribute to the Enerjy Score for this file, ranked by impact. So the metrics listed at the top influence the score to a greater extent that the metrics listed at the bottom.
| Metric | Description | |
|---|---|---|
| DECL_COMMENTS | Comments in declarations |  |
| EXEC_COMMENTS | Comments in executable code | |
| LOOPS | Number of loops | |
| DOC_COMMENT | Number of javadoc comment lines | |
| COMMENTS | Comment lines | |
| LINE_COMMENT | Number of line comments | |
| SIZE | Size of the file in bytes | |
| LINES | Number of lines in the source file | |
| LOGICAL_LINES | Number of statements | |
| JAVA0034 | JAVA0034 Missing braces in if statement | |
| ELOC | Effective lines of code | |
| CYCLOMATIC | Cyclomatic complexity | |
| COMPARISONS | Number of comparison operators | |
| OPERATORS | Number of operators | |
| PROGRAM_LENGTH | Halstead program length | |
| LOC | Lines of code | |
| OPERANDS | Number of operands | |
| RETURNS | Number of return points from functions | |
| INTERFACE_COMPLEXITY | Interface complexity | |
| UNIQUE_OPERANDS | Number of unique operands | |
| BLOCKS | Number of blocks | |
| FUNCTIONS | Number of function declarations | |
| PROGRAM_VOCAB | Halstead program vocabulary | |
| EXITS | Procedure exits | |
| PARAMS | Number of formal parameter declarations | |
| WHITESPACE | Number of whitespace lines | |
| JAVA0177 | JAVA0177 Variable declaration missing initializer | |
| JAVA0083 | JAVA0083 Unnecessary instanceof test | |
| JAVA0035 | JAVA0035 Missing braces in for statement | |
| JAVA0007 | JAVA0007 Should not declare public field | |
| JAVA0076 | JAVA0076 Use of magic number | |
| JAVA0119 | JAVA0119 Control variable changed within body of for loop | |
| JAVA0081 | JAVA0081 Boolean literal in comparison | |
| UNIQUE_OPERATORS | Number of unique operators | |
| JAVA0266 | JAVA0266 Use of System.out | |
| JAVA0163 | JAVA0163 Empty statement | |
| JAVA0036 | JAVA0036 Missing braces in while statement | |
| JAVA0117 | JAVA0117 Missing javadoc: method 'method' | |
| JAVA0144 | JAVA0144 Line exceeds maximum M characters | |
| JAVA0082 | JAVA0082 Unnecessary widening cast | |
| PROGRAM_VOLUME | Halstead program volume | |
| JAVA0136 | JAVA0136 N methods defined in class (maximum: M) | |
| JAVA0174 | JAVA0174 Assigned local variable never used | |
| JAVA0160 | JAVA0160 Method does not throw specified exception | |
| JAVA0126 | JAVA0126 Method declares unchecked exception in throws | |
| JAVA0110 | JAVA0110 Incorrect javadoc: no @return tag | |
| JAVA0123 | JAVA0123 Use all three components of for loop | |
| JAVA0173 | JAVA0173 Unused method parameter | |
| JAVA0100 | JAVA0100 Class contains N non-final fields (maximum: M) | |
| JAVA0108 | JAVA0108 Incorrect javadoc: no @param tag for 'parameter' | |
| JAVA0130 | JAVA0130 Non-static method does not use instance fields | |
| NEST_DEPTH | Maximum nesting depth | |
/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* XMeans.java
* Copyright (C) 2000 University of Waikato, Hamilton, New Zealand
*
*/
package weka.clusterers;
import weka.core.AlgVector;
import weka.core.Capabilities;
import weka.core.DistanceFunction;
import weka.core.EuclideanDistance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.neighboursearch.KDTree;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileOutputStream;
import java.io.FileReader;
import java.io.PrintWriter;
import java.io.Reader;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
/**
<!-- globalinfo-start -->
* Cluster data using the X-means algorithm.<br/>
* <br/>
* X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.
* <p/>
<!-- globalinfo-end -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @inproceedings{Pelleg2000,
* author = {Dan Pelleg and Andrew W. Moore},
* booktitle = {Seventeenth International Conference on Machine Learning},
* pages = {727-734},
* publisher = {Morgan Kaufmann},
* title = {X-means: Extending K-means with Efficient Estimation of the Number of Clusters},
* year = {2000}
* }
* </pre>
* <p/>
<!-- technical-bibtex-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -I <num>
* maximum number of overall iterations
* (default 1).</pre>
*
* <pre> -M <num>
* maximum number of iterations in the kMeans loop in
* the Improve-Parameter part
* (default 1000).</pre>
*
* <pre> -J <num>
* maximum number of iterations in the kMeans loop
* for the splitted centroids in the Improve-Structure part
* (default 1000).</pre>
*
* <pre> -L <num>
* minimum number of clusters
* (default 2).</pre>
*
* <pre> -H <num>
* maximum number of clusters
* (default 4).</pre>
*
* <pre> -B <value>
* distance value for binary attributes
* (default 1.0).</pre>
*
* <pre> -use-kdtree
* Uses the KDTree internally
* (default no).</pre>
*
* <pre> -K <KDTree class specification>
* Full class name of KDTree class to use, followed
* by scheme options.
* eg: "weka.core.neighboursearch.kdtrees.KDTree -P"
* (default no KDTree class used).</pre>
*
* <pre> -C <value>
* cutoff factor, takes the given percentage of the splitted
* centroids if none of the children win
* (default 0.0).</pre>
*
* <pre> -D <distance function class specification>
* Full class name of Distance function class to use, followed
* by scheme options.
* (default weka.core.EuclideanDistance).</pre>
*
* <pre> -N <file name>
* file to read starting centers from (ARFF format).</pre>
*
* <pre> -O <file name>
* file to write centers to (ARFF format).</pre>
*
* <pre> -U <int>
* The debug level.
* (default 0)</pre>
*
* <pre> -Y <file name>
* The debug vectors file.</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 10)</pre>
*
<!-- options-end -->
*
* @author Gabi Schmidberger (gabi@cs.waikato.ac.nz)
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @author Malcolm Ware (mfw4@cs.waikato.ac.nz)
* @version $Revision: 1.24 $
* @see RandomizableClusterer
*/
public class XMeans
extends RandomizableClusterer
implements TechnicalInformationHandler {
/*
* major TODOS:
*
* make BIC-Score replaceable by other scores
*/
/** for serialization. */
private static final long serialVersionUID = -7941793078404132616L;
/** training instances. */
protected Instances m_Instances = null;
/** model information, should increase readability. */
protected Instances m_Model = null;
/** replace missing values in training instances. */
protected ReplaceMissingValues m_ReplaceMissingFilter;
/**
* Distance value between true and false of binary attributes and
* "same" and "different" of nominal attributes (default = 1.0).
*/
protected double m_BinValue = 1.0;
/** BIC-Score of the current model. */
protected double m_Bic = Double.MIN_VALUE;
/** Distortion. */
protected double[] m_Mle = null;
/** maximum overall iterations. */
protected int m_MaxIterations = 1;
/**
* maximum iterations to perform Kmeans part
* if negative, iterations are not checked.
*/
protected int m_MaxKMeans = 1000;
/** see above, but for kMeans of splitted clusters.
*/
protected int m_MaxKMeansForChildren = 1000;
/** The actual number of clusters. */
protected int m_NumClusters = 2;
/** min number of clusters to generate. */
protected int m_MinNumClusters = 2;
/** max number of clusters to generate. */
protected int m_MaxNumClusters = 4;
/** the distance function used. */
protected DistanceFunction m_DistanceF = new EuclideanDistance();
/** cluster centers. */
protected Instances m_ClusterCenters;
/** file name of the output file for the cluster centers. */
protected File m_InputCenterFile = new File(System.getProperty("user.dir"));
/* --> DebugVectors - USED FOR DEBUGGING */
/** input file for the random vectors --> USED FOR DEBUGGING. */
protected Reader m_DebugVectorsInput = null;
/** the index for the current debug vector. */
protected int m_DebugVectorsIndex = 0;
/** all the debug vectors. */
protected Instances m_DebugVectors = null;
/** file name of the input file for the random vectors. */
protected File m_DebugVectorsFile = new File(System.getProperty("user.dir"));
/** input file for the cluster centers. */
protected Reader m_CenterInput = null;
/** file name of the output file for the cluster centers. */
protected File m_OutputCenterFile = new File(System.getProperty("user.dir"));
/** output file for the cluster centers. */
protected PrintWriter m_CenterOutput = null;
/**
* temporary variable holding cluster assignments while iterating.
*/
protected int[] m_ClusterAssignments;
/** cutoff factor - percentage of splits done in Improve-Structure part
only relevant, if all children lost. */
protected double m_CutOffFactor = 0.5;
/** Index in ranges for LOW. */
public static int R_LOW = 0;
/** Index in ranges for HIGH. */
public static int R_HIGH = 1;
/** Index in ranges for WIDTH. */
public static int R_WIDTH = 2;
/**
* KDTrees class if KDTrees are used.
*/
protected KDTree m_KDTree = new KDTree();
/** whether to use the KDTree (the KDTree is only initialized to be
* configurable from the GUI). */
protected boolean m_UseKDTree = false;
/** counts iterations done in main loop. */
protected int m_IterationCount = 0;
/** counter to say how often kMeans was stopped by loop counter. */
protected int m_KMeansStopped = 0;
/** Number of splits prepared. */
protected int m_NumSplits = 0;
/** Number of splits accepted (including cutoff factor decisions). */
protected int m_NumSplitsDone = 0;
/** Number of splits accepted just because of cutoff factor. */
protected int m_NumSplitsStillDone = 0;
/**
* level of debug output, 0 is no output.
*/
protected int m_DebugLevel = 0;
/** print the centers. */
public static int D_PRINTCENTERS = 1;
/** follows the splitting of the centers. */
public static int D_FOLLOWSPLIT = 2;
/** have a closer look at converge children. */
public static int D_CONVCHCLOSER = 3;
/** check on random vectors. */
public static int D_RANDOMVECTOR = 4;
/** check on kdtree. */
public static int D_KDTREE = 5;
/** follow iterations. */
public static int D_ITERCOUNT = 6;
/** functions were maybe misused. */
public static int D_METH_MISUSE = 80;
/** for current debug. */
public static int D_CURR = 88;
/** general debugging. */
public static int D_GENERAL = 99;
/** Flag: I'm debugging. */
public boolean m_CurrDebugFlag = true;
/**
* the default constructor.
*/
public XMeans() {
super();
m_SeedDefault = 10;
setSeed(m_SeedDefault);
}
/**
* Returns a string describing this clusterer.
* @return a description of the evaluator suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Cluster data using the X-means algorithm.\n\n"
+ "X-Means is K-Means extended by an Improve-Structure part In this "
+ "part of the algorithm the centers are attempted to be split in "
+ "its region. The decision between the children of each center and "
+ "itself is done comparing the BIC-values of the two structures.\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.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Dan Pelleg and Andrew W. Moore");
result.setValue(Field.TITLE, "X-means: Extending K-means with Efficient Estimation of the Number of Clusters");
result.setValue(Field.BOOKTITLE, "Seventeenth International Conference on Machine Learning");
result.setValue(Field.YEAR, "2000");
result.setValue(Field.PAGES, "727-734");
result.setValue(Field.PUBLISHER, "Morgan Kaufmann");
return result;
}
/**
* Returns default capabilities of the clusterer.
*
* @return the capabilities of this clusterer
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
// attributes
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
return result;
}
/**
* Generates the X-Means clusterer.
*
* @param data set of instances serving as training data
* @throws Exception if the clusterer has not been
* generated successfully
*/
public void buildClusterer(Instances data) throws Exception {
// can clusterer handle the data?
getCapabilities().testWithFail(data);
m_NumSplits = 0;
m_NumSplitsDone = 0;
m_NumSplitsStillDone = 0;
// replace missing values
m_ReplaceMissingFilter = new ReplaceMissingValues();
m_ReplaceMissingFilter.setInputFormat(data);
m_Instances = Filter.useFilter(data, m_ReplaceMissingFilter);
// initialize random function
Random random0 = new Random(m_Seed);
// num of clusters to start with
m_NumClusters = m_MinNumClusters;
// set distance function to default
if (m_DistanceF == null) {
m_DistanceF = new EuclideanDistance();
}
m_DistanceF.setInstances(m_Instances);
checkInstances();
if (m_DebugVectorsFile.exists() && m_DebugVectorsFile.isFile())
initDebugVectorsInput();
// make list of indexes for m_Instances
int[] allInstList = new int[m_Instances.numInstances()];
for (int i = 0; i < m_Instances.numInstances(); i++) {
allInstList[i] = i;
}
// set model used (just for convenience)
m_Model = new Instances(m_Instances, 0);
// produce the starting centers
if (m_CenterInput != null) {
// read centers from file
m_ClusterCenters = new Instances(m_CenterInput);
m_NumClusters = m_ClusterCenters.numInstances();
}
else
// makes the first centers randomly
m_ClusterCenters = makeCentersRandomly(random0,
m_Instances, m_NumClusters);
PFD(D_FOLLOWSPLIT, "\n*** Starting centers ");
for (int k = 0; k < m_ClusterCenters.numInstances(); k++) {
PFD(D_FOLLOWSPLIT, "Center " + k + ": " + m_ClusterCenters.instance(k));
}
PrCentersFD(D_PRINTCENTERS);
boolean finished = false;
Instances children;
// builds up a KDTree
if (m_UseKDTree)
m_KDTree.setInstances(m_Instances);
// loop counter of main loop
m_IterationCount = 0;
/**
* "finished" does get true as soon as:
* 1. number of clusters gets >= m_MaxClusters,
* 2. in the last round, none of the centers have been split
*
* if number of clusters is already >= m_MaxClusters
* part 1 (= Improve-Params) is done at least once.
*/
while (!finished &&
!stopIteration(m_IterationCount, m_MaxIterations)) {
/* ====================================================================
* 1. Improve-Params
* conventional K-means
*/
PFD(D_FOLLOWSPLIT, "\nBeginning of main loop - centers:");
PrCentersFD(D_FOLLOWSPLIT);
PFD(D_ITERCOUNT, "\n*** 1. Improve-Params " + m_IterationCount +
". time");
m_IterationCount++;
// prepare to converge
boolean converged = false;
// initialize assignments to -1
m_ClusterAssignments = initAssignments(m_Instances.numInstances());
// stores a list of indexes of instances belonging to each center
int[][] instOfCent = new int[m_ClusterCenters.numInstances()][];
// KMeans loop counter
int kMeansIteration = 0;
// converge in conventional K-means ----------------------------------
PFD(D_FOLLOWSPLIT, "\nConverge in K-Means:");
while (!converged &&
!stopKMeansIteration(kMeansIteration, m_MaxKMeans)) {
kMeansIteration++;
converged = true;
// assign instances to centers -------------------------------------
converged = assignToCenters(m_UseKDTree ? m_KDTree : null,
m_ClusterCenters,
instOfCent,
allInstList,
m_ClusterAssignments,
kMeansIteration);
PFD(D_FOLLOWSPLIT, "\nMain loop - Assign - centers:");
PrCentersFD(D_FOLLOWSPLIT);
// compute new centers = centers of mass of points
converged = recomputeCenters(m_ClusterCenters, // clusters
instOfCent, // their instances
m_Model); // model information
PFD(D_FOLLOWSPLIT, "\nMain loop - Recompute - centers:");
PrCentersFD(D_FOLLOWSPLIT);
}
PFD(D_FOLLOWSPLIT, "");
PFD(D_FOLLOWSPLIT, "End of Part: 1. Improve-Params - conventional K-means");
/** =====================================================================
* 2. Improve-Structur
*/
// BIC before split distortioning the centres
m_Mle = distortion(instOfCent, m_ClusterCenters);
m_Bic = calculateBIC(instOfCent, m_ClusterCenters, m_Mle);
PFD(D_FOLLOWSPLIT, "m_Bic " + m_Bic);
int currNumCent = m_ClusterCenters.numInstances();
Instances splitCenters = new Instances(m_ClusterCenters,
currNumCent * 2);
// store BIC values of parent and children
double[] pbic = new double [currNumCent];
double[] cbic = new double [currNumCent];
// split each center
for (int i = 0; i < currNumCent
// this could help to optimize the algorithm
// && currNumCent + numSplits <= m_MaxNumClusters
;
i++) {
PFD(D_FOLLOWSPLIT, "\nsplit center " + i +
" " + m_ClusterCenters.instance(i));
Instance currCenter = m_ClusterCenters.instance(i);
int[] currInstList = instOfCent[i];
int currNumInst = instOfCent[i].length;
// not enough instances; than continue with next
if (currNumInst <= 2) {
pbic[i] = Double.MAX_VALUE;
cbic[i] = 0.0;
// add center itself as dummy
splitCenters.add(currCenter);
splitCenters.add(currCenter);
continue;
}
// split centers ----------------------------------------------
double variance = m_Mle[i] / (double)currNumInst;
children = splitCenter(random0, currCenter, variance, m_Model);
// initialize assignments to -1
int[] oneCentAssignments = initAssignments(currNumInst);
int[][] instOfChCent = new int [2][]; // todo maybe split didn't work
// converge the children --------------------------------------
converged = false;
int kMeansForChildrenIteration = 0;
PFD(D_FOLLOWSPLIT, "\nConverge, K-Means for children: " + i);
while (!converged &&
!stopKMeansIteration(kMeansForChildrenIteration,
m_MaxKMeansForChildren)) {
kMeansForChildrenIteration++;
converged =
assignToCenters(children, instOfChCent,
currInstList, oneCentAssignments);
if (!converged) {
recomputeCentersFast(children, instOfChCent, m_Model);
}
}
// store new centers for later decision if they are taken
splitCenters.add(children.instance(0));
splitCenters.add(children.instance(1));
PFD(D_FOLLOWSPLIT, "\nconverged cildren ");
PFD(D_FOLLOWSPLIT, " " + children.instance(0));
PFD(D_FOLLOWSPLIT, " " + children.instance(1));
// compare parent and children model by their BIC-value
pbic[i] = calculateBIC(currInstList, currCenter, m_Mle[i], m_Model);
double[] chMLE = distortion(instOfChCent, children);
cbic[i] = calculateBIC(instOfChCent, children, chMLE);
} // end of loop over clusters
// decide which one to split and make new list of cluster centers
Instances newClusterCenters = null;
newClusterCenters = newCentersAfterSplit(pbic, cbic, m_CutOffFactor,
splitCenters);
/**
* Compare with before Improve-Structure
*/
int newNumClusters = newClusterCenters.numInstances();
if (newNumClusters != m_NumClusters) {
PFD(D_FOLLOWSPLIT, "Compare with non-split");
// initialize assignments to -1
int[] newClusterAssignments =
initAssignments(m_Instances.numInstances());
// stores a list of indexes of instances belonging to each center
int[][] newInstOfCent = new int[newClusterCenters.numInstances()][];
// assign instances to centers -------------------------------------
converged = assignToCenters(m_UseKDTree ? m_KDTree : null,
newClusterCenters,
newInstOfCent,
allInstList,
newClusterAssignments,
m_IterationCount);
double[] newMle = distortion(newInstOfCent, newClusterCenters);
double newBic = calculateBIC(newInstOfCent, newClusterCenters, newMle);
PFD(D_FOLLOWSPLIT, "newBic " + newBic);
if (newBic > m_Bic) {
PFD(D_FOLLOWSPLIT, "*** decide for new clusters");
m_Bic = newBic;
m_ClusterCenters = newClusterCenters;
m_ClusterAssignments = newClusterAssignments;
} else {
PFD(D_FOLLOWSPLIT, "*** keep old clusters");
}
}
newNumClusters = m_ClusterCenters.numInstances();
// decide if finished: max num cluster reached
// or last centers where not split at all
if ((newNumClusters >= m_MaxNumClusters)
|| (newNumClusters == m_NumClusters)) {
finished = true;
}
m_NumClusters = newNumClusters;
}
}
/**
* Checks for nominal attributes in the dataset.
* Class attribute is ignored.
* @param data the data to check
* @return false if no nominal attributes are present
*/
public boolean checkForNominalAttributes(Instances data) {
int i = 0;
while (i < data.numAttributes()) {
if ((i != data.classIndex()) && data.attribute(i++).isNominal()) {
return true;
}
}
return false;
}
/**
* Set array of int, used to store assignments, to -1.
* @param ass integer array used for storing assignments
* @return integer array used for storing assignments
*/
protected int[] initAssignments(int[] ass) {
for (int i = 0; i < ass.length; i++)
ass[i] = -1;
return ass;
}
/**
* Creates and initializes integer array, used to store assignments.
* @param numInstances length of array used for assignments
* @return integer array used for storing assignments
*/
protected int[] initAssignments(int numInstances) {
int[] ass = new int[numInstances];
for (int i = 0; i < numInstances; i++)
ass[i] = -1;
return ass;
}
/**
* Creates and initializes boolean array.
* @param len length of new array
* @return the new array
*/
boolean[] initBoolArray(int len) {
boolean[] boolArray = new boolean [len];
for (int i = 0; i < len; i++) {
boolArray[i] = false;
}
return boolArray;
}
/**
* Returns new center list.
*
* The following steps 1. and 2. both take care that the number of centers
* does not exceed maxCenters.
*
* 1. Compare BIC values of parent and children and takes the one as
* new centers which do win (= BIC-value is smaller).
*
* 2. If in 1. none of the children are chosen
* && and cutoff factor is > 0
* cutoff factor is taken as the percentage of "best" centers that are
* still taken.
* @param pbic array of parents BIC-values
* @param cbic array of childrens BIC-values
* @param cutoffFactor cutoff factor
* @param splitCenters all children
* @return the new centers
*/
protected Instances newCentersAfterSplit(double[] pbic,
double[] cbic,
double cutoffFactor,
Instances splitCenters) {
// store if split won
boolean splitPerCutoff = false;
boolean takeSomeAway = false;
boolean[] splitWon = initBoolArray(m_ClusterCenters.numInstances());
int numToSplit = 0;
Instances newCenters = null;
// how many would be split, because the children have a better bic value
for (int i = 0; i < cbic.length; i++) {
if (cbic[i] > pbic[i]) {
// decide for splitting ----------------------------------------
splitWon[i] = true; numToSplit++;
PFD(D_FOLLOWSPLIT, "Center " + i + " decide for children");
}
else {
// decide for parents and finished stays true -----------------
PFD(D_FOLLOWSPLIT, "Center " + i + " decide for parent");
}
}
// no splits yet so split per cutoff factor
if ((numToSplit == 0) && (cutoffFactor > 0)) {
splitPerCutoff = true;
// how many to split per cutoff factor
numToSplit = (int)
((double) m_ClusterCenters.numInstances() * m_CutOffFactor);
}
// prepare indexes of values in ascending order
double[] diff = new double [m_NumClusters];
for (int j = 0; j < diff.length; j++) {
diff[j] = pbic[j] - cbic[j];
}
int[] sortOrder = Utils.sort(diff);
// check if maxNumClusters would be exceeded
int possibleToSplit = m_MaxNumClusters - m_NumClusters;
if (possibleToSplit > numToSplit) {
// still enough possible, do the whole amount
possibleToSplit = numToSplit;
}
else
takeSomeAway = true;
// prepare for splitting the one that are supposed to be split
if (splitPerCutoff) {
for (int j = 0; (j < possibleToSplit) && (cbic[sortOrder[j]] > 0.0);
j++) {
splitWon[sortOrder[j]] = true;
}
m_NumSplitsStillDone += possibleToSplit;
}
else {
// take some splits away if max number of clusters would be exceeded
if (takeSomeAway) {
int count = 0;
int j = 0;
for (;j < splitWon.length && count < possibleToSplit; j++){
if (splitWon[sortOrder[j]] == true) count++;
}
while (j < splitWon.length) {
splitWon[sortOrder[j]] = false;
j++;
}
}
}
// finally split
if (possibleToSplit > 0)
newCenters = newCentersAfterSplit(splitWon, splitCenters);
else
newCenters = m_ClusterCenters;
return newCenters;
}
/**
* Returns new centers. Depending on splitWon: if true takes children, if
* false takes parent = current center.
*
* @param splitWon
* array of boolean to indicate to take split or not
* @param splitCenters
* list of splitted centers
* @return the new centers
*/
protected Instances newCentersAfterSplit(boolean[] splitWon,
Instances splitCenters) {
Instances newCenters = new Instances(splitCenters, 0);
int sIndex = 0;
for (int i = 0; i < splitWon.length; i++) {
if (splitWon[i]) {
m_NumSplitsDone++;
newCenters.add(splitCenters.instance(sIndex++));
newCenters.add(splitCenters.instance(sIndex++));
} else {
sIndex++;
sIndex++;
newCenters.add(m_ClusterCenters.instance(i));
}
}
return newCenters;
}
/**
* Controls that counter does not exceed max iteration value. Special function
* for kmeans iterations.
*
* @param iterationCount
* current value of counter
* @param max
* maximum value for counter
* @return true if iteration should be stopped
*/
protected boolean stopKMeansIteration(int iterationCount, int max) {
boolean stopIterate = false;
if (max >= 0)
stopIterate = (iterationCount >= max);
if (stopIterate)
m_KMeansStopped++;
return stopIterate;
}
/**
* Checks if iterationCount has to be checked and if yes
* (this means max is > 0) compares it with max.
*
* @param iterationCount the current iteration count
* @param max the maximum number of iterations
* @return true if maximum has been reached
*/
protected boolean stopIteration(int iterationCount, int max) {
boolean stopIterate = false;
if (max >= 0)
stopIterate = (iterationCount >= max);
return stopIterate;
}
/**
* Recompute the new centers. New cluster center is center of mass of its
* instances. Returns true if cluster stays the same.
* @param centers the input and output centers
* @param instOfCent the instances to the centers
* @param model data model information
* @return true if converged.
*/
protected boolean recomputeCenters(Instances centers,
int[][] instOfCent,
Instances model) {
boolean converged = true;
for (int i = 0; i < centers.numInstances(); i++) {
double val;
for (int j = 0; j < model.numAttributes(); j++) {
val = meanOrMode(m_Instances, instOfCent[i], j);
for (int k = 0; k < instOfCent[i].length; k++)
if (converged && m_ClusterCenters.instance(i).value(j) != val)
converged = false;
if (!converged)
m_ClusterCenters.instance(i).setValue(j, val);
}
}
return converged;
}
/**
* Recompute the new centers - 2nd version
* Same as recomputeCenters, but does not check if center stays the same.
*
* @param centers the input center and output centers
* @param instOfCentIndexes the indexes of the instances to the centers
* @param model data model information
*/
protected void recomputeCentersFast(Instances centers,
int[][] instOfCentIndexes,
Instances model
) {
for (int i = 0; i < centers.numInstances(); i++) {
double val;
for (int j = 0; j < model.numAttributes(); j++) {
val = meanOrMode(m_Instances, instOfCentIndexes[i], j);
centers.instance(i).setValue(j, val);
}
}
}
/**
* Computes Mean Or Mode of one attribute on a subset of m_Instances.
* The subset is defined by an index list.
* @param instances all instances
* @param instList the indexes of the instances the mean is computed from
* @param attIndex the index of the attribute
* @return mean value
*/
protected double meanOrMode(Instances instances,
int[] instList,
int attIndex) {
double result, found;
int[] counts;
int numInst = instList.length;
if (instances.attribute(attIndex).isNumeric()) {
result = found = 0;
for (int j = 0; j < numInst; j++) {
Instance currInst = instances.instance(instList[j]);
if (!currInst.isMissing(attIndex)) {
found += currInst.weight();
result += currInst.weight() *
currInst.value(attIndex);
}
}
if (Utils.eq(found, 0)) {
return 0;
} else {
return result / found;
}
} else if (instances.attribute(attIndex).isNominal()) {
counts = new int[instances.attribute(attIndex).numValues()];
for (int j = 0; j < numInst; j++) {
Instance currInst = instances.instance(instList[j]);
if (!currInst.isMissing(attIndex)) {
counts[(int) currInst.value(attIndex)] += currInst.weight();
}
}
return (double)Utils.maxIndex(counts);
} else {
return 0;
}
}
/**
* Assigns instances to centers.
*
* @param tree KDTree on all instances
* @param centers all the input centers
* @param instOfCent the instances to each center
* @param allInstList list of all instances
* @param assignments assignments of instances to centers
* @param iterationCount the number of iteration
* @return true if converged
* @throws Exception is something goes wrong
*/
protected boolean assignToCenters(KDTree tree,
Instances centers,
int[][] instOfCent,
int[] allInstList,
int[] assignments,
int iterationCount) throws Exception {
boolean converged = true;
if (tree != null) {
// using KDTree structure for assigning
converged = assignToCenters(tree,
centers,
instOfCent,
assignments,
iterationCount);
} else {
converged = assignToCenters(centers,
instOfCent,
allInstList,
assignments);
}
return converged;
}
/**
* Assign instances to centers using KDtree.
* First part of conventionell K-Means, returns true if new assignment
* is the same as the last one.
*
* @param kdtree KDTree on all instances
* @param centers all the input centers
* @param instOfCent the instances to each center
* @param assignments assignments of instances to centers
* @param iterationCount the number of iteration
* @return true if converged
* @throws Exception in case instances are not assigned to cluster
*/
protected boolean assignToCenters(KDTree kdtree,
Instances centers,
int[][] instOfCent,
int[] assignments,
int iterationCount) throws Exception {
int numCent = centers.numInstances();
int numInst = m_Instances.numInstances();
int[] oldAssignments = new int[numInst];
// WARNING: assignments is "input/output-parameter"
// should not be null
if (assignments == null) {
assignments = new int[numInst];
for (int i = 0; i < numInst; i++) {
assignments[0] = -1;
}
}
// WARNING: instOfCent is "input/output-parameter"
// should not be null
if (instOfCent == null) {
instOfCent = new int [numCent][];
}
// save old assignments
for (int i = 0; i < assignments.length; i++) {
oldAssignments[i] = assignments[i];
}
// use tree to get new assignments
kdtree.centerInstances(centers, assignments,
Math.pow(.8, iterationCount));
boolean converged = true;
// compare with previous assignment
for (int i = 0; converged && (i < assignments.length); i++) {
converged = (oldAssignments[i] == assignments[i]);
if (assignments[i] == -1)
throw new Exception("Instance " + i +
" has not been assigned to cluster.");
}
if (!converged) {
int[] numInstOfCent = new int[numCent];
for (int i = 0; i < numCent; i++)
numInstOfCent[i] = 0;
// count num of assignments per center
for (int i = 0; i < numInst; i++)
numInstOfCent[assignments[i]]++;
// prepare instancelists per center
for (int i = 0; i < numCent; i++){
instOfCent[i] = new int[numInstOfCent[i]];
}
// write instance lists per center
for (int i = 0; i < numCent; i++) {
int index = -1;
for (int j = 0; j < numInstOfCent[i]; j++) {
index = nextAssignedOne(i, index, assignments);
instOfCent[i][j] = index;
}
}
}
return converged;
}
/**
* Assign instances to centers.
* Part of conventionell K-Means, returns true if new assignment
* is the same as the last one.
*
* @param centers all the input centers
* @param instOfCent the instances to each center
* @param allInstList list of all indexes
* @param assignments assignments of instances to centers
* @return true if converged
* @throws Exception if something goes wrong
*/
protected boolean assignToCenters(Instances centers,
int[][] instOfCent,
int[] allInstList,
int[] assignments)
throws Exception {
// todo: undecided situations
boolean converged = true; // true if new assignment is the same
// as the old one
int numInst = allInstList.length;
int numCent = centers.numInstances();
int[] numInstOfCent = new int [numCent];
for (int i = 0; i < numCent; i++) numInstOfCent[i] = 0;
// WARNING: assignments is "input/output-parameter"
// should not be null
if (assignments == null) {
assignments = new int[numInst];
for (int i = 0; i < numInst; i++) {
assignments[i] = -1;
}
}
// WARNING: instOfCent is "input/output-parameter"
// should not be null
if (instOfCent == null) {
instOfCent = new int [numCent][];
}
// set assignments
for (int i = 0; i < numInst; i++) {
Instance inst = m_Instances.instance(allInstList[i]);
int newC = clusterProcessedInstance(inst, centers);
if (converged && newC != assignments[i]) {
converged = false;
}
numInstOfCent[newC]++;
if (!converged)
assignments[i] = newC;
}
// the following is only done
// if assignments are not the same, because too much effort
if (!converged) {
PFD(D_FOLLOWSPLIT, "assignToCenters -> it has NOT converged");
for (int i = 0; i < numCent; i++) {
instOfCent[i] = new int [numInstOfCent[i]];
}
for (int i = 0; i < numCent; i++) {
int index = -1;
for (int j = 0; j < numInstOfCent[i]; j++) {
index = nextAssignedOne(i, index, assignments);
instOfCent[i][j] = allInstList[index];
}
}
}
else
PFD(D_FOLLOWSPLIT, "assignToCenters -> it has converged");
return converged;
}
/**
* Searches along the assignment array for the next entry of the center
* in question.
* @param cent index of the center
* @param lastIndex index to start searching
* @param assignments assignments
* @return index of the instance the center cent is assigned to
*/
protected int nextAssignedOne(int cent, int lastIndex,
int[] assignments) {
int len = assignments.length;
int index = lastIndex + 1;
while (index < len) {
if (assignments[index] == cent) {
return (index);
}
index++;
}
return (-1);
}
/**
* Split centers in their region. Generates random vector of
* length = variance and
* adds and substractsx to cluster vector to get two new clusters.
*
* @param random random function
* @param center the center that is split here
* @param variance variance of the cluster
* @param model data model valid
* @return a pair of new centers
* @throws Exception something in AlgVector goes wrong
*/
protected Instances splitCenter(Random random,
Instance center,
double variance,
Instances model) throws Exception {
m_NumSplits++;
AlgVector r = null;
Instances children = new Instances(model, 2);
if (m_DebugVectorsFile.exists() && m_DebugVectorsFile.isFile()) {
Instance nextVector = getNextDebugVectorsInstance(model);
PFD(D_RANDOMVECTOR, "Random Vector from File " + nextVector);
r = new AlgVector(nextVector);
}
else {
// random vector of length = variance
r = new AlgVector(model, random);
}
r.changeLength(Math.pow(variance, 0.5));
PFD(D_RANDOMVECTOR, "random vector *variance "+ r);
// add random vector to center
AlgVector c = new AlgVector(center);
AlgVector c2 = (AlgVector) c.clone();
c = c.add(r);
Instance newCenter = c.getAsInstance(model, random);
children.add(newCenter);
PFD(D_FOLLOWSPLIT, "first child "+ newCenter);
// substract random vector to center
c2 = c2.substract(r);
newCenter = c2.getAsInstance(model, random);
children.add(newCenter);
PFD(D_FOLLOWSPLIT, "second child "+ newCenter);
return children;
}
/**
* Split centers in their region.
* (*Alternative version of splitCenter()*)
*
* @param random the random number generator
* @param instances of the region
* @param model the model for the centers
* (should be the same as that of instances)
* @return a pair of new centers
*/
protected Instances splitCenters(Random random,
Instances instances,
Instances model) {
Instances children = new Instances(model, 2);
int instIndex = Math.abs(random.nextInt()) % instances.numInstances();
children.add(instances.instance(instIndex));
int instIndex2 = instIndex;
int count = 0;
while ((instIndex2 == instIndex) && count < 10) {
count++;
instIndex2 = Math.abs(random.nextInt()) % instances.numInstances();
}
children.add(instances.instance(instIndex2));
return children;
}
/**
* Generates new centers randomly. Used for starting centers.
*
* @param random0 random number generator
* @param model data model of the instances
* @param numClusters number of clusters
* @return new centers
*/
protected Instances makeCentersRandomly(Random random0,
Instances model,
int numClusters) {
Instances clusterCenters = new Instances(model, numClusters);
m_NumClusters = numClusters;
// makes the new centers randomly
for (int i = 0; i < numClusters; i++) {
int instIndex = Math.abs(random0.nextInt()) % m_Instances.numInstances();
clusterCenters.add(m_Instances.instance(instIndex));
}
return clusterCenters;
}
/**
* Returns the BIC-value for the given center and instances.
* @param instList The indices of the instances that belong to the center
* @param center the center.
* @param mle maximum likelihood
* @param model the data model
* @return the BIC value
*/
protected double calculateBIC(int[] instList, Instance center,
double mle, Instances model) {
int[][] w1 = new int[1][instList.length];
for (int i = 0; i < instList.length; i++) {
w1[0][i] = instList[i];
}
double[] m = {mle};
Instances w2 = new Instances(model, 1);
w2.add(center);
return calculateBIC(w1, w2, m);
}
/**
* Calculates the BIC for the given set of centers and instances.
* @param instOfCent The instances that belong to their respective centers
* @param centers the centers
* @param mle maximum likelihood
* @return The BIC for the input.
*/
protected double calculateBIC(int[][] instOfCent, Instances centers,
double[] mle) {
double loglike = 0.0;
int numInstTotal = 0;
int numCenters = centers.numInstances();
int numDimensions = centers.numAttributes();
int numParameters = (numCenters - 1) + //probabilities
numCenters * numDimensions + //means
numCenters; // variance params
for (int i = 0; i < centers.numInstances(); i++) {
loglike += logLikelihoodEstimate(instOfCent[i].length, centers.instance(i),
mle[i], centers.numInstances() * 2);
numInstTotal += instOfCent[i].length;
}
/* diff
thats how we did it
loglike -= ((centers.numAttributes() + 1.0) * centers.numInstances() * 1)
* Math.log(count);
*/
loglike -= numInstTotal * Math.log(numInstTotal);
//System.out.println ("numInstTotal " + numInstTotal +
// "calculateBIC res " + loglike);
loglike -= (numParameters / 2.0) * Math.log(numInstTotal);
//System.out.println ("numParam " +
// + numParameters +
// " calculateBIC res " + loglike);
return loglike;
}
/**
* Calculates the log-likelihood of the data for the given model, taken
* at the maximum likelihood point.
*
* @param numInst number of instances that belong to the center
* @param center the center
* @param distortion distortion
* @param numCent number of centers
* @return the likelihood estimate
*/
protected double logLikelihoodEstimate(int numInst,
Instance center,
double distortion,
int numCent) {
// R(n) num of instances of the center -> numInst
// K num of centers -> not used
//
//todo take the diff comments away
double loglike = 0;
/* if is new */
if (numInst > 1) {
/* diff variance is new */
//
// distortion = Sum over instances x of the center(x-center)
// different to paper; sum should be squared
//
// (Sum of distances to center) / R(n) - 1.0
// different to paper; should be R(n)-K
double variance = distortion / (numInst - 1.0);
//
// -R(n)/2 * log(pi*2)
//
double p1 = - (numInst / 2.0) * Math.log(Math.PI * 2.0);
/* diff
thats how we had it
double p2 = -((ni * center.numAttributes()) / 2) * distortion;
*/
//
// -(R(n)*M)/2 * log(variance)
//
double p2 = - (numInst * center.numAttributes()) / 2 * Math.log(variance);
/* diff
thats how we had it, the difference is a bug in x-means
double p3 = - (numInst - numCent) / 2;
*/
//
// -(R(n)-1)/2
//
double p3 = - (numInst - 1.0) / 2.0;
//
// R(n)*log(R(n))
//
double p4 = numInst * Math.log(numInst);
/* diff x-means doesn't have this part
double p5 = - numInst * Math.log(numInstTotal);
*/
/*
loglike = -(ni / 2) * Math.log(Math.PI * 2)
- (ni * center.numAttributes()) / 2.0) * logdistortion
- (ni - k) / 2.0
+ ni * Math.log(ni)
- ni * Math.log(r);
*/
loglike = p1 + p2 + p3 + p4; // diff + p5;
//the log(r) is something that can be reused.
//as is the log(2 PI), these could provide extra speed up later on.
//since distortion is so expensive to compute, I only do that once.
}
return loglike;
}
/**
* Calculates the maximum likelihood estimate for the variance.
* @param instOfCent indices of instances to each center
* @param centers the centers
* @return the list of distortions distortion.
*/
protected double[] distortion(int[][] instOfCent, Instances centers) {
double[] distortion = new double[centers.numInstances()];
for (int i = 0; i < centers.numInstances(); i++) {
distortion[i] = 0.0;
for (int j = 0; j < instOfCent[i].length; j++) {
distortion[i] += m_DistanceF.distance(m_Instances
.instance(instOfCent[i][j]), centers.instance(i));
}
}
/*
* diff not done in x-means res *= 1.0 / (count - centers.numInstances());
*/
return distortion;
}
/**
* Clusters an instance.
*
* @param instance
* the instance to assign a cluster to.
* @param centers
* the centers to cluster the instance to.
* @return a cluster index.
*/
protected int clusterProcessedInstance(Instance instance, Instances centers) {
double minDist = Integer.MAX_VALUE;
int bestCluster = 0;
for (int i = 0; i < centers.numInstances(); i++) {
double dist = m_DistanceF.distance(instance, centers.instance(i));
if (dist < minDist) {
minDist = dist;
bestCluster = i;
}
}
;
return bestCluster;
}
/**
* Clusters an instance that has been through the filters.
*
* @param instance
* the instance to assign a cluster to
* @return a cluster number
*/
protected int clusterProcessedInstance(Instance instance) {
double minDist = Integer.MAX_VALUE;
int bestCluster = 0;
for (int i = 0; i < m_NumClusters; i++) {
double dist = m_DistanceF
.distance(instance, m_ClusterCenters.instance(i));
if (dist < minDist) {
minDist = dist;
bestCluster = i;
}
}
return bestCluster;
}
/**
* Classifies a given instance.
*
* @param instance the instance to be assigned to a cluster
* @return the number of the assigned cluster as an integer
* if the class is enumerated, otherwise the predicted value
* @throws Exception if instance could not be classified
* successfully
*/
public int clusterInstance(Instance instance) throws Exception {
m_ReplaceMissingFilter.input(instance);
Instance inst = m_ReplaceMissingFilter.output();
return clusterProcessedInstance(inst);
}
/**
* Returns the number of clusters.
*
* @return the number of clusters generated for a training dataset.
*/
public int numberOfClusters() {
return m_NumClusters;
}
/**
* Returns an enumeration describing the available options.
* @return an enumeration of all the available options
**/
public Enumeration listOptions() {
Vector result = new Vector();
result.addElement(new Option(
"\tmaximum number of overall iterations\n"
+ "\t(default 1).",
"I", 1, "-I <num>"));
result.addElement(new Option(
"\tmaximum number of iterations in the kMeans loop in\n"
+ "\tthe Improve-Parameter part \n"
+ "\t(default 1000).",
"M", 1, "-M <num>"));
result.addElement(new Option(
"\tmaximum number of iterations in the kMeans loop\n"
+ "\tfor the splitted centroids in the Improve-Structure part \n"
+ "\t(default 1000).",
"J", 1, "-J <num>"));
result.addElement(new Option(
"\tminimum number of clusters\n"
+ "\t(default 2).",
"L", 1, "-L <num>"));
result.addElement(new Option(
"\tmaximum number of clusters\n"
+ "\t(default 4).",
"H", 1, "-H <num>"));
result.addElement(new Option(
"\tdistance value for binary attributes\n"
+ "\t(default 1.0).",
"B", 1, "-B <value>"));
result.addElement(new Option(
"\tUses the KDTree internally\n"
+ "\t(default no).",
"use-kdtree", 0, "-use-kdtree"));
result.addElement(new Option(
"\tFull class name of KDTree class to use, followed\n"
+ "\tby scheme options.\n"
+ "\teg: \"weka.core.neighboursearch.kdtrees.KDTree -P\"\n"
+ "\t(default no KDTree class used).",
"K", 1, "-K <KDTree class specification>"));
result.addElement(new Option(
"\tcutoff factor, takes the given percentage of the splitted \n"
+ "\tcentroids if none of the children win\n"
+ "\t(default 0.0).",
"C", 1, "-C <value>"));
result.addElement(new Option(
"\tFull class name of Distance function class to use, followed\n"
+ "\tby scheme options.\n" +
"\t(default weka.core.EuclideanDistance).",
"D", 1, "-D <distance function class specification>"));
result.addElement(new Option(
"\tfile to read starting centers from (ARFF format).",
"N", 1, "-N <file name>"));
result.addElement(new Option(
"\tfile to write centers to (ARFF format).",
"O", 1, "-O <file name>"));
result.addElement(new Option(
"\tThe debug level.\n"
+ "\t(default 0)",
"U", 1, "-U <int>"));
result.addElement(new Option(
"\tThe debug vectors file.",
"Y", 1, "-Y <file name>"));
Enumeration en = super.listOptions();
while (en.hasMoreElements())
result.addElement(en.nextElement());
return result.elements();
}
/**
* Returns the tip text for this property.
* @return tip text for this property
*/
public String minNumClustersTipText() {
return "set minimum number of clusters";
}
/**
* Sets the minimum number of clusters to generate.
*
* @param n the minimum number of clusters to generate
*/
public void setMinNumClusters(int n) {
if (n <= m_MaxNumClusters) {
m_MinNumClusters = n;
}
}
/**
* Gets the minimum number of clusters to generate.
* @return the minimum number of clusters to generate
*/
public int getMinNumClusters() {
return m_MinNumClusters;
}
/**
* Returns the tip text for this property.
* @return tip text for this property
*/
public String maxNumClustersTipText() {
return "set maximum number of clusters";
}
/**
* Sets the maximum number of clusters to generate.
* @param n the maximum number of clusters to generate
*/
public void setMaxNumClusters(int n) {
if (n >= m_MinNumClusters) {
m_MaxNumClusters = n;
}
}
/**
* Gets the maximum number of clusters to generate.
* @return the maximum number of clusters to generate
*/
public int getMaxNumClusters() {
return m_MaxNumClusters;
}
/**
* Returns the tip text for this property.
* @return tip text for this property
*/
public String maxIterationsTipText() {
return "the maximum number of iterations to perform";
}
/**
* Sets the maximum number of iterations to perform.
* @param i the number of iterations
* @throws Exception if i is less than 1
*/
public void setMaxIterations(int i) throws Exception {
if (i < 0)
throw new Exception("Only positive values for iteration number" +
" allowed (Option I).");
m_MaxIterations = i;
}
/**
* Gets the maximum number of iterations.
* @return the number of iterations
*/
public int getMaxIterations() {
return m_MaxIterations;
}
/**
* Returns the tip text for this property.
* @return tip text for this property
*/
public String maxKMeansTipText() {
return "the maximum number of iterations to perform in KMeans";
}
/**
* Set the maximum number of iterations to perform in KMeans.
* @param i the number of iterations
*/
public void setMaxKMeans(int i) {
m_MaxKMeans = i;
m_MaxKMeansForChildren = i;
}
/**
* Gets the maximum number of iterations in KMeans.
* @return the number of iterations
*/
public int getMaxKMeans() {
return m_MaxKMeans;
}
/**
* Returns the tip text for this property.
* @return tip text for this property
*/
public String maxKMeansForChildrenTipText() {
return "the maximum number of iterations KMeans that is performed on the child centers";
}
/**
* Sets the maximum number of iterations KMeans that is performed
* on the child centers.
* @param i the number of iterations
*/
public void setMaxKMeansForChildren(int i) {
m_MaxKMeansForChildren = i;
}
/**
* Gets the maximum number of iterations in KMeans.
* @return the number of iterations
*/
public int getMaxKMeansForChildren() {
return m_MaxKMeansForChildren;
}
/**
* Returns the tip text for this property.
* @return tip text for this property
*/
public String cutOffFactorTipText() {
return "the cut-off factor to use";
}
/**
* Sets a new cutoff factor.
* @param i the new cutoff factor
*/
public void setCutOffFactor(double i) {
m_CutOffFactor = i;
}
/**
* Gets the cutoff factor.
* @return the cutoff factor
*/
public double getCutOffFactor() {
return m_CutOffFactor;
}
/**
* Returns the tip text for this property.
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String binValueTipText() {
return "Set the value that represents true in the new attributes.";
}
/**
* Gets value that represents true in a new numeric attribute.
* (False is always represented by 0.0.)
* @return the value that represents true in a new numeric attribute
*/
public double getBinValue() {
return m_BinValue;
}
/**
* Sets the distance value between true and false of binary attributes.
* and "same" and "different" of nominal attributes
* @param value the distance
*/
public void setBinValue(double value) {
m_BinValue = value;
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String distanceFTipText() {
return "The distance function to use.";
}
/**
* gets the "binary" distance value.
* @param distanceF the distance function with all options set
*/
public void setDistanceF(DistanceFunction distanceF) {
m_DistanceF = distanceF;
}
/**
* Gets the distance function.
* @return the distance function
*/
public DistanceFunction getDistanceF() {
return m_DistanceF;
}
/**
* Gets the distance function specification string, which contains the
* class name of the distance function class and any options to it.
*
* @return the distance function specification string
*/
protected String getDistanceFSpec() {
DistanceFunction d = getDistanceF();
if (d instanceof OptionHandler) {
return d.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler) d).getOptions());
}
return d.getClass().getName();
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String debugVectorsFileTipText() {
return "The file containing the debug vectors (only for debugging!).";
}
/**
* Sets the file that has the random vectors stored.
* Only used for debugging reasons.
* @param value the file to read the random vectors from
*/
public void setDebugVectorsFile(File value) {
m_DebugVectorsFile = value;
}
/**
* Gets the file name for a file that has the random vectors stored.
* Only used for debugging purposes.
* @return the file to read the vectors from
*/
public File getDebugVectorsFile() {
return m_DebugVectorsFile;
}
/**
* Initialises the debug vector input.
* @throws Exception if there is error
* opening the debug input file.
*/
public void initDebugVectorsInput() throws Exception {
m_DebugVectorsInput =
new BufferedReader(new FileReader(m_DebugVectorsFile));
m_DebugVectors = new Instances(m_DebugVectorsInput);
m_DebugVectorsIndex = 0;
}
/**
* Read an instance from debug vectors file.
* @param model the data model for the instance.
* @throws Exception if there are no debug vector
* in m_DebugVectors.
* @return the next debug vector.
*/
public Instance getNextDebugVectorsInstance(Instances model)
throws Exception {
if (m_DebugVectorsIndex >= m_DebugVectors.numInstances())
throw new Exception("no more prefabricated Vectors");
Instance nex = m_DebugVectors.instance(m_DebugVectorsIndex);
nex.setDataset(model);
m_DebugVectorsIndex++;
return nex;
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String inputCenterFileTipText() {
return "The file to read the list of centers from.";
}
/**
* Sets the file to read the list of centers from.
*
* @param value the file to read centers from
*/
public void setInputCenterFile(File value) {
m_InputCenterFile = value;
}
/**
* Gets the file to read the list of centers from.
*
* @return the file to read the centers from
*/
public File getInputCenterFile() {
return m_InputCenterFile;
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String outputCenterFileTipText() {
return "The file to write the list of centers to.";
}
/**
* Sets file to write the list of centers to.
*
* @param value file to write centers to
*/
public void setOutputCenterFile(File value) {
m_OutputCenterFile = value;
}
/**
* Gets the file to write the list of centers to.
*
* @return filename of the file to write centers to
*/
public File getOutputCenterFile() {
return m_OutputCenterFile;
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String KDTreeTipText() {
return "The KDTree to use.";
}
/**
* Sets the KDTree class.
* @param k a KDTree object with all options set
*/
public void setKDTree(KDTree k) {
m_KDTree = k;
}
/**
* Gets the KDTree class.
*
* @return the configured KDTree
*/
public KDTree getKDTree() {
return m_KDTree;
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String useKDTreeTipText() {
return "Whether to use the KDTree.";
}
/**
* Sets whether to use the KDTree or not.
*
* @param value if true the KDTree is used
*/
public void setUseKDTree(boolean value) {
m_UseKDTree = value;
}
/**
* Gets whether the KDTree is used or not.
*
* @return true if KDTrees are used
*/
public boolean getUseKDTree() {
return m_UseKDTree;
}
/**
* Gets the KDTree specification string, which contains the class name of
* the KDTree class and any options to the KDTree.
*
* @return the KDTree string.
*/
protected String getKDTreeSpec() {
KDTree c = getKDTree();
if (c instanceof OptionHandler) {
return c.getClass().getName() + " "
+ Utils.joinOptions(((OptionHandler)c).getOptions());
}
return c.getClass().getName();
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String debugLevelTipText() {
return "The debug level to use.";
}
/**
* Sets the debug level.
* debug level = 0, means no output
* @param d debuglevel
*/
public void setDebugLevel(int d) {
m_DebugLevel = d;
}
/**
* Gets the debug level.
* @return debug level
*/
public int getDebugLevel() {
return m_DebugLevel;
}
/**
* Checks the instances.
* No checks in this KDTree but it calls the check of the distance function.
*/
protected void checkInstances () {
// m_DistanceF.checkInstances();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -I <num>
* maximum number of overall iterations
* (default 1).</pre>
*
* <pre> -M <num>
* maximum number of iterations in the kMeans loop in
* the Improve-Parameter part
* (default 1000).</pre>
*
* <pre> -J <num>
* maximum number of iterations in the kMeans loop
* for the splitted centroids in the Improve-Structure part
* (default 1000).</pre>
*
* <pre> -L <num>
* minimum number of clusters
* (default 2).</pre>
*
* <pre> -H <num>
* maximum number of clusters
* (default 4).</pre>
*
* <pre> -B <value>
* distance value for binary attributes
* (default 1.0).</pre>
*
* <pre> -use-kdtree
* Uses the KDTree internally
* (default no).</pre>
*
* <pre> -K <KDTree class specification>
* Full class name of KDTree class to use, followed
* by scheme options.
* eg: "weka.core.neighboursearch.kdtrees.KDTree -P"
* (default no KDTree class used).</pre>
*
* <pre> -C <value>
* cutoff factor, takes the given percentage of the splitted
* centroids if none of the children win
* (default 0.0).</pre>
*
* <pre> -D <distance function class specification>
* Full class name of Distance function class to use, followed
* by scheme options.
* (default weka.core.EuclideanDistance).</pre>
*
* <pre> -N <file name>
* file to read starting centers from (ARFF format).</pre>
*
* <pre> -O <file name>
* file to write centers to (ARFF format).</pre>
*
* <pre> -U <int>
* The debug level.
* (default 0)</pre>
*
* <pre> -Y <file name>
* The debug vectors file.</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 10)</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options)
throws Exception {
String optionString;
String funcString;
optionString = Utils.getOption('I', options);
if (optionString.length() != 0)
setMaxIterations(Integer.parseInt(optionString));
else
setMaxIterations(1);
optionString = Utils.getOption('M', options);
if (optionString.length() != 0)
setMaxKMeans(Integer.parseInt(optionString));
else
setMaxKMeans(1000);
optionString = Utils.getOption('J', options);
if (optionString.length() != 0)
setMaxKMeansForChildren(Integer.parseInt(optionString));
else
setMaxKMeansForChildren(1000);
optionString = Utils.getOption('L', options);
if (optionString.length() != 0)
setMinNumClusters(Integer.parseInt(optionString));
else
setMinNumClusters(2);
optionString = Utils.getOption('H', options);
if (optionString.length() != 0)
setMaxNumClusters(Integer.parseInt(optionString));
else
setMaxNumClusters(4);
optionString = Utils.getOption('B', options);
if (optionString.length() != 0)
setBinValue(Double.parseDouble(optionString));
else
setBinValue(1.0);
setUseKDTree(Utils.getFlag("use-kdtree", options));
if (getUseKDTree()) {
funcString = Utils.getOption('K', options);
if (funcString.length() != 0) {
String[] funcSpec = Utils.splitOptions(funcString);
if (funcSpec.length == 0) {
throw new Exception("Invalid function specification string");
}
String funcName = funcSpec[0];
funcSpec[0] = "";
setKDTree((KDTree) Utils.forName(KDTree.class, funcName, funcSpec));
}
else {
setKDTree(new KDTree());
}
}
else {
setKDTree(new KDTree());
}
optionString = Utils.getOption('C', options);
if (optionString.length() != 0)
setCutOffFactor(Double.parseDouble(optionString));
else
setCutOffFactor(0.0);
funcString = Utils.getOption('D', options);
if (funcString.length() != 0) {
String[] funcSpec = Utils.splitOptions(funcString);
if (funcSpec.length == 0) {
throw new Exception("Invalid function specification string");
}
String funcName = funcSpec[0];
funcSpec[0] = "";
setDistanceF((DistanceFunction) Utils.forName(DistanceFunction.class,
funcName, funcSpec));
}
else {
setDistanceF(new EuclideanDistance());
}
optionString = Utils.getOption('N', options);
if (optionString.length() != 0) {
setInputCenterFile(new File(optionString));
m_CenterInput =
new BufferedReader(new FileReader(optionString));
}
else {
setInputCenterFile(new File(System.getProperty("user.dir")));
m_CenterInput = null;
}
optionString = Utils.getOption('O', options);
if (optionString.length() != 0) {
setOutputCenterFile(new File(optionString));
m_CenterOutput = new PrintWriter(new FileOutputStream(optionString));
}
else {
setOutputCenterFile(new File(System.getProperty("user.dir")));
m_CenterOutput = null;
}
optionString = Utils.getOption('U', options);
int debugLevel = 0;
if (optionString.length() != 0) {
try {
debugLevel = Integer.parseInt(optionString);
} catch (NumberFormatException e) {
throw new Exception(optionString +
"is an illegal value for option -U");
}
}
setDebugLevel(debugLevel);
optionString = Utils.getOption('Y', options);
if (optionString.length() != 0) {
setDebugVectorsFile(new File(optionString));
}
else {
setDebugVectorsFile(new File(System.getProperty("user.dir")));
m_DebugVectorsInput = null;
m_DebugVectors = null;
}
super.setOptions(options);
}
/**
* Gets the current settings of SimpleKMeans.
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
int i;
Vector result;
String[] options;
result = new Vector();
result.add("-I");
result.add("" + getMaxIterations());
result.add("-M");
result.add("" + getMaxKMeans());
result.add("-J");
result.add("" + getMaxKMeansForChildren());
result.add("-L");
result.add("" + getMinNumClusters());
result.add("-H");
result.add("" + getMaxNumClusters());
result.add("-B");
result.add("" + getBinValue());
if (getUseKDTree()) {
result.add("-use-kdtree");
result.add("-K");
result.add("" + getKDTreeSpec());
}
result.add("-C");
result.add("" + getCutOffFactor());
if (getDistanceF() != null) {
result.add("-D");
result.add("" + getDistanceFSpec());
}
if (getInputCenterFile().exists() && getInputCenterFile().isFile()) {
result.add("-N");
result.add("" + getInputCenterFile());
}
if (getOutputCenterFile().exists() && getOutputCenterFile().isFile()) {
result.add("-O");
result.add("" + getOutputCenterFile());
}
int dL = getDebugLevel();
if (dL > 0) {
result.add("-U");
result.add("" + getDebugLevel());
}
if (getDebugVectorsFile().exists() && getDebugVectorsFile().isFile()) {
result.add("-Y");
result.add("" + getDebugVectorsFile());
}
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Return a string describing this clusterer.
* @return a description of the clusterer as a string
*/
public String toString() {
StringBuffer temp = new StringBuffer();
temp.append("\nXMeans\n======\n");
temp.append("Requested iterations : " + m_MaxIterations + "\n");
temp.append("Iterations performed : " + m_IterationCount+ "\n");
if (m_KMeansStopped > 0) {
temp.append("kMeans did not converge\n");
temp.append(" but was stopped by max-loops "
+ m_KMeansStopped + " times (max kMeans-iter)\n");
}
temp.append("Splits prepared : " + m_NumSplits + "\n");
temp.append("Splits performed : " + m_NumSplitsDone + "\n");
temp.append("Cutoff factor : " + m_CutOffFactor + "\n");
double perc;
if (m_NumSplitsDone > 0)
perc = (((double)m_NumSplitsStillDone)/((double) m_NumSplitsDone))
* 100.0;
else
perc = 0.0;
temp.append("Percentage of splits accepted \n" +
"by cutoff factor : "
+ Utils.doubleToString(perc,2) + " %\n");
temp.append("------\n");
temp.append("Cutoff factor : " + m_CutOffFactor + "\n");
temp.append("------\n");
temp.append("\nCluster centers : " + m_NumClusters + " centers\n");
for (int i = 0; i < m_NumClusters; i++) {
temp.append("\nCluster "+i+"\n ");
for (int j = 0; j < m_ClusterCenters.numAttributes(); j++) {
if (m_ClusterCenters.attribute(j).isNominal()) {
temp.append(" "+m_ClusterCenters.attribute(j).
value((int)m_ClusterCenters.instance(i).value(j)));
} else {
temp.append(" "+m_ClusterCenters.instance(i).value(j));
}
}
}
if (m_Mle != null)
temp.append("\n\nDistortion: " +
Utils.doubleToString(Utils.sum(m_Mle),6) + "\n");
temp.append("BIC-Value : " + Utils.doubleToString(m_Bic,6) + "\n");
return temp.toString();
}
/**
* Print centers for debug.
* @param debugLevel level that gives according messages
*/
protected void PrCentersFD(int debugLevel) {
if (debugLevel == m_DebugLevel) {
for (int i = 0; i < m_ClusterCenters.numInstances(); i++) {
System.out.println(m_ClusterCenters.instance(i));
}
}
}
/**
* Tests on debug status.
* @param debugLevel level that gives according messages
* @return true if debug level is set
*/
protected boolean TFD(int debugLevel) {
return (debugLevel == m_DebugLevel);
}
/**
* Does debug printouts.
* @param debugLevel level that gives according messages
* @param output string that is printed
*/
protected void PFD(int debugLevel, String output) {
if (debugLevel == m_DebugLevel)
System.out.println(output);
}
/**
* Does debug printouts.
* @param output string that is printed
*/
protected void PFD_CURR(String output) {
if (m_CurrDebugFlag)
System.out.println(output);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.24 $");
}
/**
* Main method for testing this class.
* @param argv should contain options
*/
public static void main(String[] argv) {
runClusterer(new XMeans(), argv);
}
}
The table below shows all metrics for XMeans.java.
| Metric | Value | Description | |
|---|---|---|---|
| BLOCKS | 202.00 | Number of blocks | |
| BLOCK_COMMENT | 58.00 | Number of block comment lines | |
| COMMENTS | 933.00 | Comment lines | |
| COMMENT_DENSITY | 0.94 | Comment density | |
| COMPARISONS | 173.00 | Number of comparison operators | |
| CYCLOMATIC | 237.00 | Cyclomatic complexity | |
| DECL_COMMENTS | 139.00 | Comments in declarations | |
| DOC_COMMENT | 770.00 | Number of javadoc comment lines | |
| ELOC | 988.00 | Effective lines of code | |
| EXEC_COMMENTS | 89.00 | Comments in executable code | |
| EXITS | 134.00 | Procedure exits | |
| FUNCTIONS | 88.00 | Number of function declarations | |
| HALSTEAD_DIFFICULTY | 112.91 | Halstead difficulty | |
| HALSTEAD_EFFORT | 0.00 | Halstead effort | |
| INTERFACE_COMPLEXITY | 235.00 | Interface complexity | |
| JAVA0001 | 0.00 | JAVA0001 Package name does not contain only lower case letters | |
| JAVA0002 | 1.00 | JAVA0002 Package name does not begin with a top level domain name or country code | |
| JAVA0003 | 0.00 | JAVA0003 Minimize use of on-demand (.*) imports | |
| JAVA0004 | 0.00 | JAVA0004 Unnecessary import from java.lang | |
| JAVA0005 | 1.00 | JAVA0005 Imports not in specified order | |
| JAVA0006 | 0.00 | JAVA0006 Empty finally block | |
| JAVA0007 | 13.00 | JAVA0007 Should not declare public field | |
| JAVA0008 | 0.00 | JAVA0008 Empty catch block | |
| JAVA0009 | 0.00 | JAVA0009 Protected member in final class | |
| JAVA0010 | 0.00 | JAVA0010 Non-instantiable class does not contain a non-private static member | |
| JAVA0011 | 0.00 | JAVA0011 Abstract class does not contain an abstract method | |
| JAVA0012 | 0.00 | JAVA0012 Non-constructor method with same name as declaring class | |
| JAVA0013 | 0.00 | JAVA0013 Non-blank final field is not static | |
| JAVA0014 | 0.00 | JAVA0014 Class with only static members has non-private constructor | |
| JAVA0015 | 0.00 | JAVA0015 Package class contains public nested type | |
| JAVA0016 | 0.00 | JAVA0016 Abstract class contains public constructor | |
| JAVA0017 | 0.00 | JAVA0017 Class name does not have required form | |
| JAVA0018 | 0.00 | JAVA0018 Method name does not have required form | |
| JAVA0019 | 0.00 | JAVA0019 Interface name does not have required form | |
| JAVA0020 | 0.00 | JAVA0020 Field name does not have required form | |
| JAVA0021 | 0.00 | JAVA0021 Interface method name does not have required form | |
| JAVA0022 | 0.00 | JAVA0022 Static final field name does not have required form | |
| JAVA0023 | 0.00 | JAVA0023 Empty finalize method | |
| JAVA0024 | 0.00 | JAVA0024 Empty class | |
| JAVA0025 | 0.00 | JAVA0025 Method override is empty | |
| JAVA0026 | 0.00 | JAVA0026 Finalize method with parameters | |
| JAVA0029 | 0.00 | JAVA0029 Private method not used | |
| JAVA0030 | 0.00 | JAVA0030 Private field not used | |
| JAVA0031 | 0.00 | JAVA0031 Case statement not properly closed | |
| JAVA0032 | 0.00 | JAVA0032 Switch statement missing default | |
| JAVA0033 | 0.00 | JAVA0033 default: not last case in switch statement | |
| JAVA0034 | 36.00 | JAVA0034 Missing braces in if statement | |
| JAVA0035 | 7.00 | JAVA0035 Missing braces in for statement | |
| JAVA0036 | 1.00 | JAVA0036 Missing braces in while statement | |
| JAVA0038 | 0.00 | JAVA0038 Non-case label in switch statement | |
| JAVA0039 | 0.00 | JAVA0039 Break statement with label | |
| JAVA0040 | 0.00 | JAVA0040 Switch statement contains N cases (maximum: M) | |
| JAVA0041 | 0.00 | JAVA0041 Nested synchronized block | |
| JAVA0042 | 0.00 | JAVA0042 Empty synchronized statement | |
| JAVA0043 | 0.00 | JAVA0043 Inner class does not use outer class | |
| JAVA0044 | 0.00 | JAVA0044 Serializable class with no instance variables | |
| JAVA0045 | 0.00 | JAVA0045 Serializable class with only transient fields | |
| JAVA0046 | 0.00 | JAVA0046 Name of class not derived from Exception ends with 'Exception' | |
| JAVA0047 | 0.00 | JAVA0047 Serializable class derives from invalid base class | |
| JAVA0048 | 0.00 | JAVA0048 Name of class derived from Exception does not end with 'Exception' | |
| JAVA0049 | 0.00 | JAVA0049 Nested block at depth N (maximum: M) | |
| JAVA0050 | 0.00 | JAVA0050 Class derives from java.lang.Error | |
| JAVA0051 | 0.00 | JAVA0051 Class derives from java.lang.RuntimeException | |
| JAVA0052 | 0.00 | JAVA0052 Class derives from java.lang.Throwable | |
| JAVA0053 | 0.00 | JAVA0053 Unused label | |
| JAVA0054 | 0.00 | JAVA0054 Inheritance depth N exceeds maximum M | |
| JAVA0055 | 0.00 | JAVA0055 Class should be interface | |
| JAVA0056 | 0.00 | JAVA0056 Unnecessary abstract modifier for interface or annotation | |
| JAVA0057 | 0.00 | JAVA0057 Unnecessary default constructor | |
| JAVA0058 | 1.00 | JAVA0058 Constructor calls super() | |
| JAVA0059 | 0.00 | JAVA0059 Method override only calls super() | |
| JAVA0061 | 0.00 | JAVA0061 Inaccessible member in anonymous class | |
| JAVA0062 | 0.00 | JAVA0062 Public class missing public member or protected constructor | |
| JAVA0063 | 0.00 | JAVA0063 Identifier name should not contain '$' | |
| JAVA0064 | 0.00 | JAVA0064 N variations of identifier name (maximum: M) | |
| JAVA0065 | 0.00 | JAVA0065 Unnecessary final modifier for method in final class | |
| JAVA0066 | 0.00 | JAVA0066 Unnecessary modifier for interface nested type | |
| JAVA0067 | 0.00 | JAVA0067 Array descriptor on identifier name | |
| JAVA0068 | 0.00 | JAVA0068 Modifiers not declared in recommended order | |
| JAVA0071 | 0.00 | JAVA0071 Strings compared with == | |
| JAVA0073 | 1.00 | JAVA0073 Integer division in floating-point context | |
| JAVA0074 | 0.00 | JAVA0074 Use of Object.notify() | |
| JAVA0075 | 0.00 | JAVA0075 Method parameter hides field | |
| JAVA0076 | 11.00 | JAVA0076 Use of magic number | |
| JAVA0077 | 0.00 | JAVA0077 Private field not used in declaring class | |
| JAVA0078 | 1.00 | JAVA0078 Floating point values compared with == | |
| JAVA0079 | 0.00 | JAVA0079 Use of instance to reference static member | |
| JAVA0080 | 0.00 | JAVA0080 Import declaration not used | |
| JAVA0081 | 1.00 | JAVA0081 Boolean literal in comparison | |
| JAVA0082 | 2.00 | JAVA0082 Unnecessary widening cast | |
| JAVA0083 | 2.00 | JAVA0083 Unnecessary instanceof test | |
| JAVA0084 | 0.00 | JAVA0084 Should use compound assignment operator | |
| JAVA0085 | 0.00 | JAVA0085 Use of sun.* class | |
| JAVA0087 | 0.00 | JAVA0087 Use of Thread.sleep() | |
| JAVA0089 | 0.00 | JAVA0089 Use of restricted package | |
| JAVA0092 | 0.00 | JAVA0092 Use of restricted type | |
| JAVA0093 | 0.00 | JAVA0093 Redundant assignment | |
| JAVA0094 | 0.00 | JAVA0094 Field hides a superclass field | |
| JAVA0095 | 0.00 | JAVA0095 Uninitialized private field | |
| JAVA0096 | 0.00 | JAVA0096 Field in nested class hides outer field | |
| JAVA0098 | 0.00 | JAVA0098 Minimize use of implicit field initializers | |
| JAVA0100 | 1.00 | JAVA0100 Class contains N non-final fields (maximum: M) | |
| JAVA0101 | 0.00 | JAVA0101 Unnecessary modifier for field in interface | |
| JAVA0102 | 0.00 | JAVA0102 Last statement in finalize() not super.finalize() | |
| JAVA0103 | 0.00 | JAVA0103 Explicit call to finalize() | |
| JAVA0104 | 0.00 | JAVA0104 finalize() only calls super.finalize() | |
| JAVA0105 | 0.00 | JAVA0105 Duplicate import declaration | |
| JAVA0106 | 0.00 | JAVA0106 Unnecessary import from current package | |
| JAVA0108 | 0.00 | JAVA0108 Incorrect javadoc: no @param tag for 'parameter' | |
| JAVA0109 | 0.00 | JAVA0109 Incorrect javadoc: no parameter 'parameter' | |
| JAVA0110 | 0.00 | JAVA0110 Incorrect javadoc: no @return tag | |
| JAVA0111 | 0.00 | JAVA0111 Incorrect javadoc: @return tag for void method | |
| JAVA0112 | 0.00 | JAVA0112 Incorrect javadoc: no exception 'exception' in throws | |
| JAVA0113 | 0.00 | JAVA0113 Incorrect javadoc: no @author tag | |
| JAVA0114 | 0.00 | JAVA0114 Incorrect javadoc: no @version tag | |
| JAVA0115 | 0.00 | JAVA0115 Incorrect javadoc: no @throws or @exception tag for 'exception' | |
| JAVA0116 | 0.00 | JAVA0116 Missing javadoc: field 'field' | |
| JAVA0117 | 0.00 | JAVA0117 Missing javadoc: method 'method' | |
| JAVA0118 | 0.00 | JAVA0118 Missing javadoc: type 'type' | |
| JAVA0119 | 2.00 | JAVA0119 Control variable changed within body of for loop | |
| JAVA0123 | 1.00 | JAVA0123 Use all three components of for loop | |
| JAVA0125 | 0.00 | JAVA0125 Continue statement with label | |
| JAVA0126 | 0.00 | JAVA0126 Method declares unchecked exception in throws | |
| JAVA0128 | 0.00 | JAVA0128 Public constructor in non-public class | |
| JAVA0130 | 1.00 | JAVA0130 Non-static method does not use instance fields | |
| JAVA0131 | 0.00 | JAVA0131 Compatible method does not override base | |
| JAVA0132 | 0.00 | JAVA0132 Method overload with compatible signature | |
| JAVA0133 | 0.00 | JAVA0133 Non-synchronized method overrides synchronized method | |
| JAVA0135 | 0.00 | JAVA0135 Only one of Object.equals and Object.hashCode defined: missing 'method' | |
| JAVA0136 | 1.00 | JAVA0136 N methods defined in class (maximum: M) | |
| JAVA0137 | 0.00 | JAVA0137 Non-abstract class missing constructor | |
| JAVA0138 | 1.00 | JAVA0138 N parameters defined for method (maximum: M) | |
| JAVA0139 | 0.00 | JAVA0139 Definition of main other than public static void main(java.lang.String[]) | |
| JAVA0141 | 0.00 | JAVA0141 Unnecessary modifier for method in interface | |
| JAVA0143 | 0.00 | JAVA0143 Synchronized method | |
| JAVA0144 | 2.00 | JAVA0144 Line exceeds maximum M characters | |
| JAVA0145 | 505.00 | JAVA0145 Tab character used in source file | |
| JAVA0150 | 0.00 | JAVA0150 java.lang.Error (or subclass) thrown | |
| JAVA0153 | 0.00 | JAVA0153 Inefficient conversion of integer to string | |
| JAVA0159 | 0.00 | JAVA0159 Inefficient conversion of string to integer | |
| JAVA0160 | 1.00 | JAVA0160 Method does not throw specified exception | |
| JAVA0161 | 0.00 | JAVA0161 Conditional wait() not in loop | |
| JAVA0163 | 1.00 | JAVA0163 Empty statement | |
| JAVA0165 | 0.00 | JAVA0165 Conflicting return statement in finally block | |
| JAVA0166 | 0.00 | JAVA0166 Generic exception caught | |
| JAVA0167 | 0.00 | JAVA0167 ThreadDeath not rethrown | |
| JAVA0169 | 0.00 | JAVA0169 Unnecessary catch block: exception 'exception' | |
| JAVA0170 | 0.00 | JAVA0170 Caught exception not derived from java.lang.Exception | |
| JAVA0171 | 0.00 | JAVA0171 Unused local variable | |
| JAVA0173 | 1.00 | JAVA0173 Unused method parameter | |
| JAVA0174 | 1.00 | JAVA0174 Assigned local variable never used | |
| JAVA0175 | 2.00 | JAVA0175 Successive assignment to variable | |
| JAVA0176 | 0.00 | JAVA0176 Local variable name does not have required form | |
| JAVA0177 | 13.00 | JAVA0177 Variable declaration missing initializer | |
| JAVA0179 | 0.00 | JAVA0179 Local variable hides visible field | |
| JAVA0233 | 0.00 | JAVA0233 Definition of serialVersionUID other than 'private static final long serialVersionUID' | |
| JAVA0234 | 0.00 | JAVA0234 Class is Serializable but does not define serialVersionUID | |
| JAVA0235 | 0.00 | JAVA0235 Class defines serialVersionUID but does not implement Serializable | |
| JAVA0236 | 0.00 | JAVA0236 Attempt to clone an object which does not implement Cloneable | |
| JAVA0237 | 1.00 | JAVA0237 Class implements Cloneable but does not have public clone method | |
| JAVA0238 | 0.00 | JAVA0238 Clone method does not call super.clone() | |
| JAVA0239 | 0.00 | JAVA0239 Class declares 'readObject' or 'writeObject' but does not implement Serializable | |
| JAVA0240 | 0.00 | JAVA0240 Serializable class which declares readObject or writeObject but not both | |
| JAVA0241 | 0.00 | JAVA0241 'readObject' or 'writeObject' should be declared private in Serializable class | |
| JAVA0242 | 0.00 | JAVA0242 Transient field in non-Serializable class | |
| JAVA0243 | 0.00 | JAVA0243 'readResolve' or 'writeReplace' should be declared private or protected | |
| JAVA0244 | 0.00 | JAVA0244 Field or method name in subclass differs only by case from inherited field or method | |
| JAVA0245 | 0.00 | JAVA0245 JUnit TestCase with non-trivial constructor | |
| JAVA0246 | 0.00 | JAVA0246 JUnit assertXXX statement missing message parameter | |
| JAVA0247 | 0.00 | JAVA0247 JUnit 'setUp()' and 'tearDown()' should call super method | |
| JAVA0248 | 0.00 | JAVA0248 JUnit method 'setUp' or 'tearDown' with incorrect signature | |
| JAVA0249 | 0.00 | JAVA0249 JUnit TestCase 'suite()' should be declared static | |
| JAVA0250 | 0.00 | JAVA0250 JUnit TestCase declares testXXX method with incorrect signature | |
| JAVA0251 | 0.00 | JAVA0251 Use '%n' for line breaks in printf/format for platform independence | |
| JAVA0252 | 0.00 | JAVA0252 'enum' is a Java 1.5 reserved word | |
| JAVA0253 | 0.00 | JAVA0253 Not all enum constants consumed in switch statement | |
| JAVA0254 | 0.00 | JAVA0254 Use enhanced for loop construct instead of Iterator | |
| JAVA0255 | 0.00 | JAVA0255 Result of method invocation not used | |
| JAVA0256 | 0.00 | JAVA0256 Assignment of external collection/array to field | |
| JAVA0257 | 0.00 | JAVA0257 Use of 'Constant Interface' anti-pattern | |
| JAVA0258 | 0.00 | JAVA0258 Implement Iterable for foreach compatibility | |
| JAVA0259 | 0.00 | JAVA0259 Return of collection/array field | |
| JAVA0260 | 0.00 | JAVA0260 Use 'enum' instead of Enumerated Type pattern | |
| JAVA0261 | 0.00 | JAVA0261 Use specialized Enum collection types | |
| JAVA0262 | 0.00 | JAVA0262 Use of char in integer context | |
| JAVA0263 | 0.00 | JAVA0263 Long literal ends with 'l' instead of 'L' | |
| JAVA0264 | 0.00 | JAVA0264 Integer math in long context - check for overflow | |
| JAVA0265 | 0.00 | JAVA0265 Use of Throwable.printStackTrace() | |
| JAVA0266 | 3.00 | JAVA0266 Use of System.out | |
| JAVA0267 | 0.00 | JAVA0267 Use of System.err | |
| JAVA0269 | 0.00 | JAVA0269 Contents of StringBuffer never used | |
| JAVA0270 | 0.00 | JAVA0270 Use Java 5.0 enhanced for loop construct to iterate over all elements in an array | |
| JAVA0271 | 0.00 | JAVA0271 Minimize use of on-demand (.*) static imports | |
| JAVA0272 | 0.00 | JAVA0272 Thread.run() called | |
| JAVA0273 | 0.00 | JAVA0273 Non-final derivative of Thread calls start() in constructor | |
| JAVA0274 | 0.00 | JAVA0274 Serializable class has a synchronized readObject() | |
| JAVA0275 | 0.00 | JAVA0275 Serializable class has a synchronized writeObject() and no other synchronized methods | |
| JAVA0276 | 0.00 | JAVA0276 Unnecessary use of String constructor | |
| JAVA0277 | 0.00 | JAVA0277 Iterator.next() implementation does not throw NoSuchElementException | |
| JAVA0278 | 0.00 | JAVA0278 Unnecessary use of Boolean constructor | |
| JAVA0279 | 0.00 | JAVA0279 Serialization method readObject or readObjectNoData calls an overridable method | |
| JAVA0280 | 0.00 | JAVA0280 IllegalMonitorStateException caught | |
| JAVA0281 | 0.00 | JAVA0281 Iterator.next() not called in loop | |
| JAVA0282 | 0.00 | JAVA0282 Call to Iterator.next() in loop which does not test Iterator.hasNext() | |
| JAVA0283 | 0.00 | JAVA0283 Control variable not updated in loop body | |
| JAVA0284 | 0.00 | JAVA0284 Explicit garbage collection | |
| JAVA0285 | 0.00 | JAVA0285 Dereference of potentially null variable | |
| JAVA0286 | 0.00 | JAVA0286 Dereference of null variable | |
| JAVA0287 | 0.00 | JAVA0287 Unnecessary null check | |
| JAVA0288 | 0.00 | JAVA0288 Inconsistent null check | |
| LINES | 2405.00 | Number of lines in the source file | |
| LINE_COMMENT | 105.00 | Number of line comments | |
| LOC | 1182.00 | Lines of code | |
| LOGICAL_LINES | 679.00 | Number of statements | |
| LOOPS | 51.00 | Number of loops | |
| NEST_DEPTH | 5.00 | Maximum nesting depth | |
| OPERANDS | 2661.00 | Number of operands | |
| OPERATORS | 5054.00 | Number of operators | |
| PARAMS | 91.00 | Number of formal parameter declarations | |
| PROGRAM_LENGTH | 7715.00 | Halstead program length | |
| PROGRAM_VOCAB | 767.00 | Halstead program vocabulary | |
| PROGRAM_VOLUME | 0.00 | Halstead program volume | |
| RETURNS | 144.00 | Number of return points from functions | |
| SIZE | 70936.00 | Size of the file in bytes | |
| UNIQUE_OPERANDS | 707.00 | Number of unique operands | |
| UNIQUE_OPERATORS | 60.00 | Number of unique operators | |
| WHITESPACE | 290.00 | Number of whitespace lines | |