Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
Topication t = new Topication (testing.get(i), this, topicSequences.get(i)); test.add (t);
public void addInstances (InstanceList training, List<LabelSequence> topics) { initializeForTypes (training.getDataAlphabet()); assert (training.size() == topics.size()); for (int i = 0; i < training.size(); i++) { Topication t = new Topication (training.get(i), this, topics.get(i)); data.add (t); // Include sufficient statistics for this one doc FeatureSequence tokenSequence = (FeatureSequence) t.instance.getData(); LabelSequence topicSequence = t.topicSequence; for (int pi = 0; pi < topicSequence.getLength(); pi++) { int topic = topicSequence.getIndexAtPosition(pi); typeTopicCounts[tokenSequence.getIndexAtPosition(pi)].adjustOrPutValue(topic, 1, 1); tokensPerTopic[topic]++; } } initializeHistogramsAndCachedValues(); }
public void addInstances (InstanceList training, List<LabelSequence> topics) { initializeForTypes (training.getDataAlphabet()); assert (training.size() == topics.size()); for (int i = 0; i < training.size(); i++) { Topication t = new Topication (training.get(i), this, topics.get(i)); data.add (t); // Include sufficient statistics for this one doc FeatureSequence tokenSequence = (FeatureSequence) t.instance.getData(); LabelSequence topicSequence = t.topicSequence; for (int pi = 0; pi < topicSequence.getLength(); pi++) { int topic = topicSequence.getIndexAtPosition(pi); typeTopicCounts[tokenSequence.getIndexAtPosition(pi)].adjustOrPutValue(topic, 1, 1); tokensPerTopic[topic]++; } } initializeHistogramsAndCachedValues(); }
public void addInstances (InstanceList training, List<LabelSequence> topics) { initializeForTypes (training.getDataAlphabet()); assert (training.size() == topics.size()); for (int i = 0; i < training.size(); i++) { Topication t = new Topication (training.get(i), this, topics.get(i)); data.add (t); // Include sufficient statistics for this one doc FeatureSequence tokenSequence = (FeatureSequence) t.instance.getData(); LabelSequence topicSequence = t.topicSequence; for (int pi = 0; pi < topicSequence.getLength(); pi++) { int topic = topicSequence.getIndexAtPosition(pi); typeTopicCounts[tokenSequence.getIndexAtPosition(pi)].adjustOrPutValue(topic, 1, 1); tokensPerTopic[topic]++; } } initializeHistogramsAndCachedValues(); }