protected boolean readTAssignFile(String tassignFile) {
try {
int i, j;
BufferedReader reader =
new BufferedReader(new InputStreamReader(new FileInputStream(tassignFile), "UTF-8"));
String line;
// z = new Vector[M];
// z = new ArrayList[M];
z = new short[M][];
data = new LDADataset(M);
data.V = V;
for (i = 0; i < M; i++) {
line = reader.readLine();
StringTokenizer tknr = new StringTokenizer(line, " \t\r\n");
int length = tknr.countTokens();
// Vector<Integer> words = new Vector<Integer>();
// Vector<Integer> topics = new Vector<Integer>();
ArrayList<Integer> words = new ArrayList<>();
ArrayList<Integer> topics = new ArrayList<>();
for (j = 0; j < length; j++) {
String token = tknr.nextToken();
StringTokenizer tknr2 = new StringTokenizer(token, ":");
if (tknr2.countTokens() != 2) {
System.out.println("Invalid word-topic assignment line\n");
return false;
}
words.add(Integer.parseInt(tknr2.nextToken()));
topics.add(Integer.parseInt(tknr2.nextToken()));
} // end for each topic assignment
// allocate and add new document to the corpus
Document doc = new Document(words);
data.setDoc(doc, i);
// assign values for z
// z[i] = new Vector<Integer>();
// z[i] = new ArrayList<>(topics.size()); // Note: Modify: init arraylist
// size.
z[i] = new short[topics.size()];
for (j = 0; j < topics.size(); j++) {
// z[i].add(topics.get(j));
z[i][j] = (short) (int) topics.get(j);
}
} // end for each doc
reader.close();
} catch (Exception e) {
System.out.println("Error while loading model: " + e.getMessage());
e.printStackTrace();
return false;
}
return true;
}
public Model inference(String[] strs) {
// System.out.println("inference");
Model newModel = new Model();
// System.out.println("read dataset");
LDADataset dataset = LDADataset.readDataSet(strs, globalDict);
return inference(dataset);
}
/** Init parameters for inference reading new dataset from file */
public boolean initNewModel(LDACmdOption option, Model trnModel) {
if (!init(option)) return false;
LDADataset dataset =
LDADataset.readDataSet(dir + File.separator + dfile, trnModel.data.localDict);
if (dataset == null) {
System.out.println("Fail to read dataset!\n");
return false;
}
return initNewModel(option, dataset, trnModel);
}
/** load saved model */
public boolean loadModel() {
if (!readOthersFile(dir + File.separator + modelName + othersSuffix)) return false;
if (!readTAssignFile(dir + File.separator + modelName + tassignSuffix)) return false;
// read dictionary
Dictionary dict = new Dictionary();
if (!dict.readWordMap(dir + File.separator + wordMapFile)) return false;
data.localDict = dict;
return true;
}
/** Init parameters for estimation */
public boolean initNewModel(LDACmdOption option) {
if (!init(option)) return false;
int m, n, w, k;
p = new double[K];
data = LDADataset.readDataSet(dir + File.separator + dfile);
if (data == null) {
System.out.println("Fail to read training data!\n");
return false;
}
// + allocate memory and assign values for variables
M = data.M;
V = data.V;
dir = option.dir;
savestep = option.savestep;
// K: from command line or default value
// alpha, beta: from command line or default values
// niters, savestep: from command line or default values
nw = new int[V][K];
for (w = 0; w < V; w++) {
for (k = 0; k < K; k++) {
nw[w][k] = 0;
}
}
nd = new int[M][K];
for (m = 0; m < M; m++) {
for (k = 0; k < K; k++) {
nd[m][k] = 0;
}
}
nwsum = new int[K];
for (k = 0; k < K; k++) {
nwsum[k] = 0;
}
ndsum = new int[M];
for (m = 0; m < M; m++) {
ndsum[m] = 0;
}
z = new Vector[M];
for (m = 0; m < data.M; m++) {
int N = data.docs[m].length;
z[m] = new Vector<Integer>();
// initilize for z
for (n = 0; n < N; n++) {
int topic = (int) Math.floor(Math.random() * K);
z[m].add(topic);
// number of instances of word assigned to topic j
nw[data.docs[m].words[n]][topic] += 1;
// number of words in document i assigned to topic j
nd[m][topic] += 1;
// total number of words assigned to topic j
nwsum[topic] += 1;
}
// total number of words in document i
ndsum[m] = N;
}
theta = new double[M][K];
phi = new double[K][V];
return true;
}
