public String toString() {
StringBuilder out = new StringBuilder();
Formatter formatter = new Formatter(out, Locale.US);
for (int topic = 0; topic < numTopics; topic++) {
formatter.format("Topic %d", topic);
for (TopicScores scores : diagnostics) {
formatter.format("\t%s=%.4f", scores.name, scores.scores[topic]);
}
formatter.format("\n");
for (int position = 0; position < topicTopWords[topic].length; position++) {
if (topicTopWords[topic][position] == null) {
break;
}
formatter.format(" %s", topicTopWords[topic][position]);
for (TopicScores scores : diagnostics) {
if (scores.wordScoresDefined) {
formatter.format("\t%s=%.4f", scores.name, scores.topicWordScores[topic][position]);
}
}
out.append("\n");
}
}
return out.toString();
}
public void doInference() {
try {
ParallelTopicModel model = ParallelTopicModel.read(new File(inferencerFile));
TopicInferencer inferencer = model.getInferencer();
// TopicInferencer inferencer =
// TopicInferencer.read(new File(inferencerFile));
// InstanceList testing = readFile();
readFile();
InstanceList testing = generateInstanceList(); // readFile();
for (int i = 0; i < testing.size(); i++) {
StringBuilder probabilities = new StringBuilder();
double[] testProbabilities = inferencer.getSampledDistribution(testing.get(i), 10, 1, 5);
ArrayList probabilityList = new ArrayList();
for (int j = 0; j < testProbabilities.length; j++) {
probabilityList.add(new Pair<Integer, Double>(j, testProbabilities[j]));
}
Collections.sort(probabilityList, new CustomComparator());
for (int j = 0; j < testProbabilities.length && j < topN; j++) {
if (j > 0) probabilities.append(" ");
probabilities.append(
((Pair<Integer, Double>) probabilityList.get(j)).getFirst().toString()
+ ","
+ ((Pair<Integer, Double>) probabilityList.get(j)).getSecond().toString());
}
System.out.println(docIds.get(i) + "," + probabilities.toString());
}
} catch (Exception e) {
e.printStackTrace();
System.err.println(e.getMessage());
}
}
public String toXML() {
int[] tokensPerTopic = model.tokensPerTopic;
StringBuilder out = new StringBuilder();
Formatter formatter = new Formatter(out, Locale.US);
out.append("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n");
out.append("<model>\n");
for (int topic = 0; topic < numTopics; topic++) {
int[][] matrix = topicCodocumentMatrices[topic];
formatter.format("<topic id='%d'", topic);
for (TopicScores scores : diagnostics) {
formatter.format(" %s='%.4f'", scores.name, scores.scores[topic]);
}
out.append(">\n");
TreeSet<IDSorter> sortedWords = topicSortedWords.get(topic);
// How many words should we report? Some topics may have fewer than
// the default number of words with non-zero weight.
int limit = numTopWords;
if (sortedWords.size() < numTopWords) {
limit = sortedWords.size();
}
double cumulativeProbability = 0.0;
Iterator<IDSorter> iterator = sortedWords.iterator();
for (int position = 0; position < limit; position++) {
IDSorter info = iterator.next();
double probability = info.getWeight() / tokensPerTopic[topic];
cumulativeProbability += probability;
formatter.format(
"<word rank='%d' count='%.0f' prob='%.5f' cumulative='%.5f' docs='%d'",
position + 1,
info.getWeight(),
probability,
cumulativeProbability,
matrix[position][position]);
for (TopicScores scores : diagnostics) {
if (scores.wordScoresDefined) {
formatter.format(" %s='%.4f'", scores.name, scores.topicWordScores[topic][position]);
}
}
formatter.format(
">%s</word>\n",
topicTopWords[topic][position].replaceAll("&", "&").replaceAll("<", ">"));
}
out.append("</topic>\n");
}
out.append("</model>\n");
return out.toString();
}
