Examples of FullSVMClassification

Examples of uk.ac.cam.ha293.tweetlabel.topics.FullSVMClassification

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  public void fillSVM(String topicType) {
    System.out.println("Filling from SVM "+topicType+" classifications");
    FullSVMClassification[] classifications = new FullSVMClassification[d];
    for(long id : Tools.getCSVUserIDs()) {
      classifications[indexLookup.get(id)] = new FullSVMClassification(topicType,id);
    }
   
    //cosine similarities!
    for(int m=0; m<d; m++) {
      System.out.println("On row "+m);
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Examples of uk.ac.cam.ha293.tweetlabel.topics.FullSVMClassification

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      for(Long uid : Tools.getCSVUserIDs()) {
        //System.out.println(totalCount);
        totalCount++;
        Set<String> svmTopicSet = new HashSet<String>();
        Set<String> baselineTopicSet = new HashSet<String>();
        FullSVMClassification svm = new FullSVMClassification(topicType,uid);
        int kCount=0;
        for(String topic : svm.getCategorySet()) {
          if(kCount == k) break;
          kCount++;
          svmTopicSet.add(topic);
          System.out.println("Adding topic "+topic+" "+svm.getScore(topic));
        }
        if(topicType.equals("alchemy")) {
          FullAlchemyClassification baseline = new FullAlchemyClassification(uid);
          kCount=0;
          for(String topic : baseline.getCategorySet()) {
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Examples of uk.ac.cam.ha293.tweetlabel.topics.FullSVMClassification

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    int cosineCount = 0;
    double squareSum = 0.0;
    for(Long uid : Tools.getCSVUserIDs()) {
      if(topicType.equals("alchemy")) {
        FullAlchemyClassification fac = new FullAlchemyClassification(uid);
        FullSVMClassification fsm = new FullSVMClassification(topicType,uid);
        double sim = fsm.cosineSimilarity(fac);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
      } else if(topicType.equals("calais")) {
        FullCalaisClassification fcc = new FullCalaisClassification(uid);
        FullSVMClassification fsm = new FullSVMClassification(topicType,uid);
        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.topics.FullSVMClassification

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    int cosineCount = 0;
    double squareSum = 0.0;
    for(Long uid : Tools.getCSVUserIDs()) {
      if(topicType.equals("alchemy")) {
        FullAlchemyClassification fac = new FullAlchemyClassification(uid);
        FullSVMClassification fsm = new FullSVMClassification(topicType,uid);
        double sim = cosineKSimilarity(fac,fsm,k);
        cosineSum += sim;
        squareSum += sim*sim;
        cosineCount++;
      } else if(topicType.equals("calais")) {
        FullCalaisClassification fcc = new FullCalaisClassification(uid);
        FullSVMClassification fsm = new FullSVMClassification(topicType,uid);
        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.topics.FullSVMClassification

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          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);
        lldaTopicSets.get(topSVMTopic).add(uid);
      } else {
        FullLLDAClassification llda = new FullLLDAClassification(topicType,alpha,uid);
        if(topicType.equals("textwise")) llda = new FullLLDAClassification("textwiseproper",alpha,uid);
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