Package uk.ac.cam.ha293.tweetlabel.types

Examples of uk.ac.cam.ha293.tweetlabel.types.Category

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  public static void srccKFinder(String topicType, double alpha) {
    System.out.println("Finding optimal SRCC K for "+topicType+" "+alpha);
    String[] topics = Tools.getTopics(topicType);
    int maxK = topics.length;
    for(int k=1; k<=maxK; k++) {
      SimilarityMatrix baseline = new SimilarityMatrix(2506);
      baseline.fillRestricted(true, topicType, k, 0);
      SimilarityMatrix llda = new SimilarityMatrix(2506);
      llda.fillRestricted(false, topicType, k, alpha);
      System.out.println(alpha+","+topicType+","+k+","+SpearmanRank.jscSRCC(baseline, llda));
    }
  }
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        //if(fac.getScore(topic) < scoreThreshold) break; //stop getting low-prob topics
        topics.add(topic);
        count++;
      }
    }else if(topicType.equals("liwc")) {
      FullLIWCClassification flc = new FullLIWCClassification(false,userID);
      int topTopics = 3;
      int count = 0;
      for(String topic : flc.getCategorySet()) {
        if(count == topTopics) break;
        topics.add(topic);
        count++;
      }
    } else if(topicType.equals("liwcnb")) {
      FullLIWCClassification flc = new FullLIWCClassification(true,userID);
      int topTopics = 3;
      double threshold = 0.2;
      int count = 0;
      for(String topic : flc.getCategorySet()) {
        if(count == topTopics) break;
        if(flc.getScore(topic) < threshold) break;
        topics.add(topic);
        count++;
      }
    } else {
      System.out.println("invalid topic type");
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  public void fillLIWC(boolean naiveBayes) {
    System.out.println("Filling from LIWC classifications, NB="+naiveBayes);
    FullLIWCClassification[] classifications = new FullLIWCClassification[d];
    for(long id : Tools.getCSVUserIDs()) {
      classifications[indexLookup.get(id)] = new FullLIWCClassification(naiveBayes,id);
    }
   
    //cosine similarities!
    for(int m=0; m<d; m++) {
      System.out.println("On row "+m);
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  public void fillLDAAndSave(int numTopics, int burn, int sample, double alpha) {
    //get clasifications
    System.out.println("Filling from LDA classifications");
    FullLDAClassification[] classifications = new FullLDAClassification[d];
    for(long id : Tools.getCSVUserIDs()) {
      classifications[indexLookup.get(id)] = new FullLDAClassification(id,numTopics,burn,sample,alpha);
    }
   
    //cosine similarities!
    for(int m=0; m<d; m++) {
      System.out.println("On row "+m);
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  public void fillLDAJS(int numTopics, int burn, int sample, double alpha) {
    //get clasifications
    System.out.println("Filling from LDA classifications");
    FullLDAClassification[] classifications = new FullLDAClassification[d];
    for(long id : Tools.getCSVUserIDs()) {
      classifications[indexLookup.get(id)] = new FullLDAClassification(id,numTopics,burn,sample,alpha);
    }
   
    //cosine similarities!
    for(int m=0; m<d; m++) {
      System.out.println("On row "+m);
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  }
 
  public static Set<Double> diversitySet(String topicType, double alpha, long uid) {
    Set<Double> valueSet = new HashSet<Double>();
    if(topicType.equals("lda")) {
      FullLDAClassification c = new FullLDAClassification(uid,1000,100,0,alpha);
      for(String cat : c.getCategorySet()) {
        valueSet.add(c.getScore(cat));
      }
    } else if(topicType.equals("alchemy")) {
      FullLLDAClassification c = new FullLLDAClassification("alchemy",alpha,uid);
      for(String cat : c.getCategorySet()) {
        valueSet.add(c.getScore(cat));
      }
    } else if(topicType.equals("calais")) {
      FullLLDAClassification c = new FullLLDAClassification("calais",alpha,uid);
      for(String cat : c.getCategorySet()) {
        valueSet.add(c.getScore(cat));
      }
    } else if(topicType.equals("textwise")) {
      FullLLDAClassification c = new FullLLDAClassification("textwise",alpha,uid);
      for(String cat : c.getCategorySet()) {
        valueSet.add(c.getScore(cat));
      }
    }
    return valueSet;
  }
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  public void fillLLDA(String topicType, double alpha) {
    System.out.println("Filling from LLDA-inferred "+topicType+" classifications");
    FullLLDAClassification[] classifications = new FullLLDAClassification[d];
    for(long id : Tools.getCSVUserIDs()) {
      classifications[indexLookup.get(id)] = new FullLLDAClassification(topicType,alpha,id);
    }
   
    //cosine similarities!
    for(int m=0; m<d; m++) {
      System.out.println("On row "+m);
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  public void fillLLDA(String topicType, double alpha, boolean fewerProfiles, int reduction) {
    //System.out.println("Filling from LLDA-inferred "+topicType+" classifications");
    FullLLDAClassification[] classifications = new FullLLDAClassification[d];
    for(long id : Tools.getCSVUserIDs()) {
      classifications[indexLookup.get(id)] = new FullLLDAClassification(topicType,alpha,fewerProfiles,reduction,id);
    }
   
    //cosine similarities!
    for(int m=0; m<d; m++) {
      //System.out.println("On row "+m);
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  public void fillKLLDA(String topicType, double alpha, int k) {
    //System.out.println("Filling from LLDA-inferred "+topicType+" classifications");
    FullLLDAClassification[] classifications = new FullLLDAClassification[d];
    for(long id : Tools.getCSVUserIDs()) {
      classifications[indexLookup.get(id)] = new FullLLDAClassification(topicType,alpha,id);
    }
   
    //cosine similarities!
    for(int m=0; m<d; m++) {
      //System.out.println("On row "+m);
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  public void fillLLDAJS(String topicType, double alpha) {
    System.out.println("Filling from LLDA-inferred "+topicType+" classifications");
    FullLLDAClassification[] classifications = new FullLLDAClassification[d];
    for(long id : Tools.getCSVUserIDs()) {
      classifications[indexLookup.get(id)] = new FullLLDAClassification(topicType,alpha,id);
    }
   
    //cosine similarities!
    for(int m=0; m<d; m++) {
      System.out.println("On row "+m);
View Full Code Here
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Related Classes of uk.ac.cam.ha293.tweetlabel.types.Category

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