public void estimateAll(int iteration) throws IOException { //re-Gibbs sampling on all data data.addAll(test); initializeHistogramsAndCachedValues(); estimate(iteration); }
public void printDocumentTopics (ArrayList<Topication> dataset, PrintWriter pw) { printDocumentTopics (dataset, pw, 0.0, -1); }
public void printState (ArrayList<Topication> dataset, File f) throws IOException { PrintStream out = new PrintStream(new GZIPOutputStream(new BufferedOutputStream(new FileOutputStream(f)))); printState(dataset, out); out.close(); }
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { LabelSequence topicSequence = test.get(di).topicSequence; for( ; iter <= maxIteration; iter++) { sampleTopicsForOneTestDoc (tokenSequence, topicSequence);
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { FeatureSequence tokenSequence = (FeatureSequence) test.get(di).instance.getData(); LabelSequence topicSequence = test.get(di).topicSequence; sampleTopicsForOneTestDocAll (tokenSequence, topicSequence);
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { FeatureSequence tokenSequence = (FeatureSequence) test.get(di).instance.getData(); LabelSequence topicSequence = test.get(di).topicSequence; sampleTopicsForOneDocWithTheta (tokenSequence, topicSequence, topicDistribution);
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { LabelSequence topicSequence = test.get(di).topicSequence; for( ; iter <= maxIteration; iter++) { sampleTopicsForOneTestDoc (tokenSequence, topicSequence);
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { FeatureSequence tokenSequence = (FeatureSequence) test.get(di).instance.getData(); LabelSequence topicSequence = test.get(di).topicSequence; sampleTopicsForOneTestDocAll (tokenSequence, topicSequence);
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { FeatureSequence tokenSequence = (FeatureSequence) test.get(di).instance.getData(); LabelSequence topicSequence = test.get(di).topicSequence; sampleTopicsForOneDocWithTheta (tokenSequence, topicSequence, topicDistribution);
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { LabelSequence topicSequence = test.get(di).topicSequence; for( ; iter <= maxIteration; iter++) { sampleTopicsForOneTestDoc (tokenSequence, topicSequence);
public void estimateAll(int iteration) throws IOException { //re-Gibbs sampling on all data data.addAll(test); initializeHistogramsAndCachedValues(); estimate(iteration); }
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { FeatureSequence tokenSequence = (FeatureSequence) test.get(di).instance.getData(); LabelSequence topicSequence = test.get(di).topicSequence; sampleTopicsForOneTestDocAll (tokenSequence, topicSequence);
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { FeatureSequence tokenSequence = (FeatureSequence) test.get(di).instance.getData(); LabelSequence topicSequence = test.get(di).topicSequence; sampleTopicsForOneDocWithTheta (tokenSequence, topicSequence, topicDistribution);
public void printDocumentTopics (ArrayList<Topication> dataset, PrintWriter pw) { printDocumentTopics (dataset, pw, 0.0, -1); }
public void printState (ArrayList<Topication> dataset, File f) throws IOException { PrintStream out = new PrintStream(new GZIPOutputStream(new BufferedOutputStream(new FileOutputStream(f)))); printState(dataset, out); out.close(); }
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { FeatureSequence tokenSequence = (FeatureSequence) test.get(di).instance.getData(); LabelSequence topicSequence = test.get(di).topicSequence; sampleTopicsForOneTestDoc (tokenSequence, topicSequence);
public void estimateAll(int iteration) throws IOException { //re-Gibbs sampling on all data data.addAll(test); initializeHistogramsAndCachedValues(); estimate(iteration); }
public void printDocumentTopics (ArrayList<Topication> dataset, PrintWriter pw) { printDocumentTopics (dataset, pw, 0.0, -1); }
public void printState (ArrayList<Topication> dataset, File f) throws IOException { PrintStream out = new PrintStream(new GZIPOutputStream(new BufferedOutputStream(new FileOutputStream(f)))); printState(dataset, out); out.close(); }
LabelSequence topicSequence = new LabelSequence(topicAlphabet, new int[instanceLength(instance)]); if (false) { FeatureSequence tokenSequence = (FeatureSequence) test.get(di).instance.getData(); LabelSequence topicSequence = test.get(di).topicSequence; sampleTopicsForOneTestDoc (tokenSequence, topicSequence);