public static void main(String[] args) throws IOException {
BufferedReader in = new BufferedReader(new FileReader("Defrag_4.out"));
Vector<Cluster> v = new Vector<Cluster>();
Vector<Vector<Tweet>> t = new Vector<Vector<Tweet>>();
HashMap<String, Integer> map = new HashMap<String, Integer>();
Vector<HashMap<String, Integer>> vm = new Vector<HashMap<String, Integer>>();
String s = "";
int i = 0, j = 0;
Vector<Tweet> vector = new Vector<Tweet>();
vm.add(new HashMap<String, Integer>());
while ((s = in.readLine()) != null) {
if (s.equals(
"------------------------------------------------------------------------------")) {
// System.out.println(vector.size());
t.add(vector);
vector = new Vector<Tweet>();
vm.add(new HashMap<String, Integer>());
continue;
}
i++;
String label = s.substring(0, s.indexOf(" "));
String str_tw = (s.substring(s.indexOf(" ") + 1)).trim();
vector.add(new Tweet(str_tw, label));
Integer oldCount = map.get(label);
Integer oldCount_Internal = vm.get(vm.size() - 1).get(label);
map.put(label, oldCount == null ? 1 : oldCount + 1);
vm.get(vm.size() - 1).put(label, oldCount_Internal == null ? 1 : oldCount_Internal + 1);
}
t.add(vector);
// Building Clusters
int index = 0;
for (Vector<Tweet> vt : t) {
v.add(new Cluster(vt.firstElement(), index));
for (int idx = 1; idx < vt.size(); idx++) {
v.get(index).addmember(vt.get(idx));
}
index++;
}
System.out.println(v.size());
// De-Fragmentation
Vector<Cluster> vc = new Vector<Cluster>();
// for(i = 0; i < v.size(); i++) {
// Cluster master = v.get(i);
// TweetVector tv_master = master.getCentroid();
// for(j = i + 1; j < v.size(); j++) {
// Cluster slave = v.get(j);
// TweetVector tv_slave = slave.getCentroid();
// double sim = tv_master.cosineSimilarity(tv_slave);
// if(sim >= 0.35) {
// master.addMembers(slave.getMembers());
// v.remove(j);
// }
// }
// vc.add(master);
// }
// for(i = 0; i < v.size(); i++) {
// Cluster master = v.get(i);
// TweetVector tv_master = master.getCentroid();
// for(j = i + 1; j < v.size(); j++) {
// Cluster slave = v.get(j);
// TweetVector tv_slave = slave.getCentroid();
// double sim = tv_master.cosineSimilarity(tv_slave);
// if(sim >= 0.32) {
// for(Tweet tweet : slave.getMembers()) {
// master.addmember(tweet);
// }
// v.remove(j);
// }
// }
// vc.add(master);
// }
System.out.println(vc.size());
Vector<Vector<Tweet>> newClusters = new Vector<Vector<Tweet>>();
for (Cluster clus : vc) {
newClusters.add(clus.getMembers());
}
// percision
double tp;
double fn;
double fp;
// for(String x : map.keySet()) {
// System.out.println(x + " " + map.get(x));
// }
/**
* i عدد ال elements اللي جوه Cluster موجود دلوقتي j و عدد ال elements اللي ليها نفس ال Class k
* و عدد ال elements اللي من نفس ال Class جوا نفس ال cluster
*
* <p>TP = max(k within the cluster)
*
* <p>FN = i - TP
*
* <p>FP = j - TP
*
* <p>TP = max(k in within cluster) J = number of elements in class
*
* <p>that has maximum K
*/
BufferedWriter bw = new BufferedWriter(new FileWriter("F-Measure4_1_After.out"));
index = 0;
double avg = 0.0;
for (Vector<Tweet> tv : t) {
i = tv.size();
String maxLabel = "";
int k = 0;
for (String str : vm.get(index).keySet()) {
int x = vm.get(index).get(str);
if (x > k) {
k = x;
maxLabel = str;
}
}
j = map.get(maxLabel);
tp = k;
fn = i - tp;
fp = j - tp;
double precision = tp / (tp + fp);
double recall = tp / (tp + fn);
double fmeasure = (2 * precision * recall) / (precision + recall);
index++;
avg += (fmeasure);
bw.append("F-Measure = " + fmeasure + "\n");
}
bw.append("Overall F-Measure = " + (avg / vm.size()) + "\n");
bw.close();
// System.out.println(i);
// Collections.shuffle(t);
// LeaderFollowers lf = new LeaderFollowers(0.35, 0);
// for(Tweet tw : t) {
// lf.go_tweet(tw);
// }
// BufferedWriter bw = new BufferedWriter(new FileWriter("Purity.out"));
// i = 1;
// BufferedWriter bw2 = new BufferedWriter(new FileWriter("Defrag_4.out"));
//// System.out.println(lf.getClusters().size());
// for(Cluster c : vc) {
// for(Tweet tw : c.getMembers())
// bw2.write(tw.getLabel() + " " + tw.getOriginal() + "\n");
//
// bw2.write("------------------------------------------------------------------------------\n");
// }
// bw2.close();
// double overAll = 0.0;
// for(Vector<Tweet> vt : t) {
// if(!vt.isEmpty()) {
// double pur = purity(vt, vt.firstElement().getLabel());
// j = map.get(vt.firstElement().getLabel());
// double[] arr = tp2(vt, vt.firstElement().getLabel(), j);
// tp = arr[0];
// fn = arr[1];
// fp = arr[2];
// double f = (2 * fn * fp) / (fn + fp);
// overAll += ((double)vt.size() / (double)i) * pur;
// bw.write(pur + " " + f + "\n");
// }
// }
// bw.write("Over All Purity = " + overAll + "\n");
// System.err.println(overAll);
// bw.close();
}
public void calcscores() {
Matrix mbase, mbaseavg;
Matrix mtemp;
double sco, div;
@SuppressWarnings("unused")
double scodef, divdef;
mbase = clustinit.vtestsm;
mbaseavg = clustinit.avgsm;
scorevar = new double[(int) mbase.getRowCount()];
scorestabdet = new double[(int) mbase.getRowCount()];
scorevardef = new double[(int) mbase.getRowCount()];
scorestabdesc = 0;
scorestabpop = 0;
for (int i = 0; i < 3; i++) {
scorevar[i] = 0;
scorevardef[i] = 0;
}
for (int i = 3; i < mbase.getRowCount(); i++) {
mtemp = mbase.selectRows(Ret.LINK, i);
sco = mtemp.abs(Ret.LINK).getMeanValue();
sco = sco * mbaseavg.selectRows(Ret.LINK, i).getStdValue();
div = mbaseavg.abs(Ret.LINK).selectRows(Ret.LINK, i).getMeanValue();
if (div > 0) {
sco = sco / div;
} else {
sco = 0;
}
scorevar[i] = sco;
}
mbase = clustinit.vtestsmdef;
mbaseavg = clustinit.avgsmdef;
for (int i = 3; i < mbase.getRowCount(); i++) {
mtemp = mbase.selectRows(Ret.LINK, i);
sco = mtemp.abs(Ret.LINK).getMeanValue();
sco = sco * mbaseavg.selectRows(Ret.LINK, i).getStdValue();
div = mbaseavg.abs(Ret.LINK).selectRows(Ret.LINK, i).getMeanValue();
if (div > 0) {
sco = sco / div;
} else {
sco = 0;
}
scorevardef[i] = sco;
}
scorestabdesc = 0;
@SuppressWarnings("unused")
int nbvarninit;
for (int i = 3; i < mbase.getRowCount(); i++) {
scorestabdet[i] = 0;
for (int j = 0; j < mbase.getColumnCount(); j++) {
scorestabdet[i] +=
Math.abs(
clustinit.vtestsm.getAsDouble(i, j)
- clustinit.vtestsm.getAsDouble(i, clustinit.idtickinit))
/ Math.max(
Math.abs(clustinit.vtestsm.getAsDouble(i, j)),
Math.abs(clustinit.vtestsm.getAsDouble(i, clustinit.idtickinit)))
/ mbase.getColumnCount();
}
scorestabdet[i] = 1 - scorestabdet[i];
if (!Double.isNaN(scorestabdet[i])) {
scorestabdesc = scorestabdesc + scorestabdet[i] / (mbase.getRowCount() - 3);
}
}
scorestabpop = 0;
Cluster nc;
for (int j = 0; j < clustHistDef.size(); j++) {
nc = clustHistDef.get(j);
scorestabpop +=
(double) clustinit.getNumberOfCommonComponents(nc)
/ (double) Math.max(clustinit.getComponentIds().size(), nc.getComponentIds().size());
}
scorestabpop = (double) scorestabpop / clustinit.vtestsm.getColumnCount();
long idColumn = mbase.getRowForLabel(Cluster.ID_C_NAME);
long classLabelColumn = mbase.getRowForLabel(Cluster.CLASS_LABEL_C_NAME);
long labelColorColumn = mbase.getRowForLabel("LABEL-COLOR");
for (int i = 3; i < mbase.getRowCount(); i++) {
Pattern p = Pattern.compile("CLASS_LABEL");
Matcher m = p.matcher(mbase.getRowLabel(i));
if ((i == idColumn || i == classLabelColumn || i == labelColorColumn || m.lookingAt())) {
scorevardef[i] = 0;
scorevar[i] = 0;
}
}
}