Examples of FullTextwiseClassification

Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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        double sim = cosineKSimilarity(baseline,inferred,k);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
      } else if(topicType.equals("textwise")) {
        FullTextwiseClassification baseline = new FullTextwiseClassification(uid,true);
        FullLLDAClassification inferred = new FullLLDAClassification("textwiseproper",alpha,uid);
        double sim = cosineKSimilarity(baseline,inferred,k);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
 
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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        double sim = inferred.cosineSimilarity(baseline);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
      } else if(topicType.equals("textwise")) {
        FullTextwiseClassification baseline = new FullTextwiseClassification(uid,true);
        FullLLDAClassification inferred = new FullLLDAClassification("textwiseproper",alpha,fewerProfiles,reduction,uid);
        if(inferred.getCategorySet().isEmpty()) continue;
        double sim = inferred.cosineSimilarity(baseline);
        cosineSum += sim;
        squareSum += sim*sim;
 
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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        double sim = cosineKSimilarity(baseline,inferred,k);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
      } else if(topicType.equals("textwise")) {
        FullTextwiseClassification baseline = new FullTextwiseClassification(uid,true);
        FullLLDAClassification inferred = new FullLLDAClassification("textwiseproper",alpha,fewerProfiles,reduction,uid);
        if(inferred.getCategorySet().isEmpty()) continue;
        double sim = cosineKSimilarity(baseline,inferred,k);
        cosineSum += sim;
        squareSum += sim*sim;
 
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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        double sim = fsm.cosineSimilarity(fcc);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
      } else if(topicType.equals("textwise")) {
        FullTextwiseClassification ftc = new FullTextwiseClassification(uid,true);
        FullSVMClassification fsm = new FullSVMClassification(topicType,uid);
        double sim = fsm.cosineSimilarity(ftc);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
 
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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        double sim = cosineKSimilarity(fcc,fsm,k);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
      } else if(topicType.equals("textwise")) {
        FullTextwiseClassification ftc = new FullTextwiseClassification(uid,true);
        FullSVMClassification fsm = new FullSVMClassification(topicType,uid);
        double sim = cosineKSimilarity(ftc,fsm,k);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
 
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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      } else if(topicType.equals("calais")) {
        FullCalaisClassification c = new FullCalaisClassification(uid);
        if(c.getCategorySet().size()==0) continue;
        topTopic = c.getCategorySet().toArray(new String[0])[0];
      } else if(topicType.equals("textwiseproper")) {
        FullTextwiseClassification c = new FullTextwiseClassification(uid,true);
        if(c.getCategorySet().size()==0) continue;
        topTopic = c.getCategorySet().toArray(new String[0])[0];
      }
      if(profileSets.containsKey(topTopic)) {
        profileSets.get(topTopic).add(uid);
      } else {
        Set<Long> newSet = new HashSet<Long>();
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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              FullLLDAClassification llda = new FullLLDAClassification(topicType,alpha,uid);
              writeOut.println(uid+","+llda.cosineSimilarity(baseline));
            }
          } else if(topicType.equals("textwise")) {
            for(long uid : uids) {
              FullTextwiseClassification baseline = new FullTextwiseClassification(uid,false);
              FullLLDAClassification llda = new FullLLDAClassification(topicType,alpha,uid);
              writeOut.println(uid+","+llda.cosineSimilarity(baseline));
            }
          }else if(topicType.equals("textwiseproper")) {
            for(long uid : uids) {
              FullTextwiseClassification baseline = new FullTextwiseClassification(uid,true);
              FullLLDAClassification llda = new FullLLDAClassification(topicType,alpha,uid);
              writeOut.println(uid+","+llda.cosineSimilarity(baseline));
            }
          }
          writeOut.close();
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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        double sim = inferred.jsDivergence(baseline);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
      } else if(topicType.equals("textwise")) {
        FullTextwiseClassification baseline = new FullTextwiseClassification(uid,true);
        FullLLDAClassification inferred = new FullLLDAClassification("textwiseproper",alpha,uid);
        double sim = inferred.jsDivergence(baseline);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
 
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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          continue;
        } else if(topTopic.equals("Other")) {
          topTopic = c.getCategorySet().toArray(new String[1])[1];
        }
      } else if(topicType.equals("textwise")) {
        FullTextwiseClassification c = new FullTextwiseClassification(uid,true);
        if(c.getCategorySet().size()==0) {
          noClassifications.add(uid);
          continue;
        }
        topTopic = c.getCategorySet().toArray(new String[1])[0];
      }
      if(svm) {
        FullSVMClassification svmClassification = new FullSVMClassification(topicType,uid);
        String topSVMTopic = svmClassification.getCategorySet().toArray(new String[1])[0];
        gtTopicSets.get(topTopic).add(uid);
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Examples of uk.ac.cam.ha293.tweetlabel.classify.FullTextwiseClassification

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        if(topTopic.equals("Other")) {
          if(cl.getCategorySet().size()==1) topTopic="NO_TOP_TOPIC";
          else topTopic = cl.getCategorySet().toArray(new String[0])[1];
        }
      } else if(topicType.equals("textwise")) {
        FullTextwiseClassification cl = new FullTextwiseClassification(docIDLookup.get(i).getId(),true);
        if(cl.getCategorySet().size()==0) topTopic="NO_TOP_TOPIC";
        else topTopic = cl.getCategorySet().toArray(new String[0])[0];
      }
      if(verbose) System.out.println("Document "+i+" found to have top topic "+topTopic+", id "+topicIDs.get(topTopic));
      topTopics.add(topTopic);
     
      //add this id to the topTopics map
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