public int getClusterSize(int clusterNum) {
int count = 0;
for (int i = 0; i < seqList.size(); i++) {
if (cluster[i] == clusterNum) count++;
}
return count;
}
public ArrayList<String> getClusterIDs(int clusterNum) {
ArrayList<String> clusterSeqs = new ArrayList<String>();
for (int i = 0; i < seqList.size(); i++) {
if (cluster[i] == clusterNum) clusterSeqs.add(seqList.get(i));
}
return clusterSeqs;
}
public void buildClusterer(ArrayList<String> seqDB, double[][] sm) {
seqList = seqDB;
this.setSimMatrix(sm);
Attribute seqString = new Attribute("sequence", (FastVector) null);
FastVector attrInfo = new FastVector();
attrInfo.addElement(seqString);
Instances data = new Instances("data", attrInfo, 0);
for (int i = 0; i < seqList.size(); i++) {
Instance currentInst = new Instance(1);
currentInst.setDataset(data);
currentInst.setValue(0, seqList.get(i));
data.add(currentInst);
}
try {
buildClusterer(data);
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
public void generateWekaFile(ArrayList<MyTerm> myTerms, int maxDocNum, String wekaFilePath)
throws IOException {
String text = "@relation interest\n";
text += "@attribute text string\n";
for (int i = 0; i < maxDocNum; i++) {
text += "@attribute doc" + i + "\treal\n";
}
text += "@data\n";
for (int j = 0; j < myTerms.size(); j++) {
MyTerm term = myTerms.get(j);
String line = "";
line += term.originTrem.text();
for (int i = 0; i < term.vector.length; i++) {
line += "," + term.vector[i];
}
line += "\n";
text += line;
}
// System.out.println(text);
PrintWriter Pout = new PrintWriter(new FileWriter(wekaFilePath));
Pout.println(text);
Pout.close();
}
public JSONArray Cluster(String wekaFilePath, int clusterNum) throws Exception {
File inputFile = new File(wekaFilePath);
ArffLoader arf = new ArffLoader();
arf.setFile(inputFile);
Instances originIns = arf.getDataSet();
Instances insTest = new Instances(originIns);
insTest.deleteStringAttributes();
int totalNum = insTest.numInstances();
// SimpleKMeans sm = new SimpleKMeans();
EM em = new EM();
em.setNumClusters(clusterNum);
MakeDensityBasedClusterer sm = new MakeDensityBasedClusterer();
sm.setClusterer(em);
sm.buildClusterer(insTest);
System.out.println("totalNum:" + insTest.numInstances());
System.out.println("============================");
System.out.println(sm.toString());
Map<Integer, ArrayList<String>> result = new HashMap<Integer, ArrayList<String>>();
for (int i = 0; i < clusterNum; i++) {
result.put(i, new ArrayList<String>());
}
for (int i = 0; i < totalNum; i++) {
Instance ins = originIns.instance(i);
String word = ins.stringValue(0);
Instance tempIns = new Instance(ins);
tempIns.deleteAttributeAt(0);
int cluster = sm.clusterInstance(tempIns);
result.get(cluster).add(word);
}
// print the result
ArrayList<String> words = new ArrayList<String>();
JSONArray keyWords = new JSONArray();
for (int k : result.keySet()) {
words = result.get(k);
PriorityQueue<MyTerm> clusterQueue = new PriorityQueue<MyTerm>(1, MyTermCompare);
for (int i = 0; i < words.size(); i++) {
String s = words.get(i);
assert linkMap.containsKey(s);
int freq = linkMap.get(s).totalFreq;
clusterQueue.add(linkMap.get(s));
words.set(i, "(" + s + ":" + freq + ")");
}
JSONArray clusterArray = new JSONArray();
int num = clusterQueue.size() / 10 + 1; // 5%
int totalFreq = 0;
int totalLength = 0;
for (int i = 0; i < num && !clusterQueue.isEmpty(); ) {
JSONObject mem = new JSONObject();
MyTerm myTerm = clusterQueue.poll();
String word = myTerm.originTrem.text();
if (word.length() == 1) {
continue;
}
mem.put("text", word);
mem.put("freq", myTerm.totalFreq);
clusterArray.put(mem);
i++;
totalFreq += myTerm.totalFreq;
totalLength += word.length();
}
double averFreq = totalFreq * 1.0 / num;
double averLength = totalLength * 1.0 / num;
int count = 0;
while (!clusterQueue.isEmpty() && count < num) {
MyTerm myTerm = clusterQueue.poll();
String word = myTerm.originTrem.text();
int freq = myTerm.totalFreq;
int times = (int) (word.length() / averFreq) + 1;
if (freq > averFreq / times) {
JSONObject mem = new JSONObject();
mem.put("text", word);
mem.put("freq", freq);
mem.put("extra", true);
clusterArray.put(mem);
}
}
keyWords.put(clusterArray);
System.out.println(
"cluster" + k + ":" + words.size() + ":\t" + (int) (words.size() * 1.0 / totalNum * 100));
if (result.get(k).size() < 100) {
System.out.println(result.get(k));
}
}
// System.out.println("errorNum:"+errorNum);
return keyWords;
}
/** if clusterIdx is -1, all instances are used (a single metric for all clusters is used) */
public boolean trainMetric(int clusterIdx) throws Exception {
Init(clusterIdx);
double[] weights = new double[m_numAttributes];
int violatedConstraints = 0;
int numInstances = 0;
for (int instIdx = 0; instIdx < m_instances.numInstances(); instIdx++) {
int assignment = m_clusterAssignments[instIdx];
// only instances assigned to this cluster are of importance
if (assignment == clusterIdx || clusterIdx == -1) {
numInstances++;
if (clusterIdx < 0) {
m_centroid = m_kmeans.getClusterCentroids().instance(assignment);
}
// accumulate variance
Instance instance = m_instances.instance(instIdx);
Instance diffInstance = m_metric.createDiffInstance(instance, m_centroid);
for (int attr = 0; attr < m_numAttributes; attr++) {
weights[attr] += diffInstance.value(attr);
}
// check all constraints for this instance
Object list = m_instanceConstraintMap.get(new Integer(instIdx));
if (list != null) { // there are constraints associated with this instance
ArrayList constraintList = (ArrayList) list;
for (int i = 0; i < constraintList.size(); i++) {
InstancePair pair = (InstancePair) constraintList.get(i);
int linkType = pair.linkType;
int firstIdx = pair.first;
int secondIdx = pair.second;
Instance instance1 = m_instances.instance(firstIdx);
Instance instance2 = m_instances.instance(secondIdx);
int otherIdx =
(firstIdx == instIdx)
? m_clusterAssignments[secondIdx]
: m_clusterAssignments[firstIdx];
if (otherIdx != -1) { // check whether the constraint is violated
if (otherIdx != assignment && linkType == InstancePair.MUST_LINK) {
diffInstance = m_metric.createDiffInstance(instance1, instance2);
for (int attr = 0; attr < m_numAttributes; attr++) {
weights[attr] += 0.5 * m_MLweight * diffInstance.value(attr);
}
} else if (otherIdx == assignment && linkType == InstancePair.CANNOT_LINK) {
diffInstance = m_metric.createDiffInstance(instance1, instance2);
for (int attr = 0; attr < m_numAttributes; attr++) {
// this constraint will be counted twice, hence 0.5
weights[attr] += 0.5 * m_CLweight * m_maxCLDiffInstance.value(attr);
weights[attr] -= 0.5 * m_CLweight * diffInstance.value(attr);
}
}
}
}
}
}
}
// System.out.println("Updating cluster " + clusterIdx
// + " containing " + numInstances);
// check the weights
double[] newWeights = new double[m_numAttributes];
double[] currentWeights = m_metric.getWeights();
boolean needNewtonRaphson = false;
for (int attr = 0; attr < m_numAttributes; attr++) {
if (weights[attr] <= 0) { // check to avoid divide by 0 - TODO!
System.out.println(
"Negative weight "
+ weights[attr]
+ " for clusterIdx="
+ clusterIdx
+ "; using prev value="
+ currentWeights[attr]);
newWeights[attr] = currentWeights[attr];
// needNewtonRaphson = true;
// break;
} else {
if (m_regularize) { // solution of quadratic equation - TODO!
int n = m_instances.numInstances();
double ratio = (m_logTermWeight * n) / (2 * weights[attr]);
newWeights[attr] =
ratio + Math.sqrt(ratio * ratio + (m_regularizerTermWeight * n) / weights[attr]);
} else {
newWeights[attr] = m_logTermWeight * numInstances / weights[attr];
}
}
}
// do NR if needed
if (needNewtonRaphson) {
System.out.println("GOING TO NEWTON-RAPHSON!!!\n");
newWeights = updateWeightsUsingNewtonRaphson(currentWeights, weights);
}
// PRINT routine
// System.out.println("Total constraints violated: " + violatedConstraints/2 + "; weights
// are:");
// for (int attr=0; attr<numAttributes; attr++) {
// System.out.print(newWeights[attr] + "\t");
// }
// System.out.println();
// end PRINT routine
m_metric.setWeights(newWeights);
return true;
}