public void TrainNewModel(Corpora cor, CommandLineOption opt)
{
InitOption(opt);
InitModel(cor);
PrintModelInfo();
GibbsSampling(niters);
}
public void TrainNewModel(Corpora cor, CommandLineOption opt, string outputFile)
{
this.outputfile = outputFile;
InitOption(opt);
InitModel(cor);
PrintModelInfo();
GibbsSampling(niters);
}
static void Main(string[] args)
{
CommandLineOption opt = GetDefaultOption();
Parser parser = new Parser();
var stopwatch = new Stopwatch();
try
{
parser.ParseArguments(args, opt);
string[] info = File.ReadAllLines(opt.info);
int sourceAuthorNum = Convert.ToInt32(info[0]);
int targetAuthorNum = Convert.ToInt32(info[1]);
int sourcePaperNum = Convert.ToInt32(info[2]);
int targetPaperNum = Convert.ToInt32(info[3]);
int collaborationPaperNum = Convert.ToInt32(info[4]);
List <string> papers = File.ReadAllLines(opt.papers).ToList();
List <string> authors = File.ReadAllLines(opt.authors).ToList();
var sourcePapers = papers.Take(sourcePaperNum).ToArray();
var targetPapers = papers.Skip(sourcePaperNum).Take(targetPaperNum).ToArray();
var collaborationPapers = papers.Skip(sourcePaperNum + targetPaperNum).ToArray();
var sourceAuthors = authors.Take(sourcePaperNum).ToArray();
var targetAuthors = authors.Skip(sourcePaperNum).Take(targetPaperNum).ToArray();
var collaborationAuthors = authors.Skip(sourcePaperNum + targetPaperNum).ToArray();
int vocabSize = 12;
Corpora sourceCor = new Corpora(vocabSize, sourceAuthorNum);
sourceCor.LoadDataFile(sourcePapers, sourceAuthors);
ACTPhraseSampling sourceModel = new ACTPhraseSampling();
sourceModel.TrainNewModel(sourceCor, opt, "outSource.txt");
Corpora targetCor = new Corpora(vocabSize, targetAuthorNum);
targetCor.LoadDataFile(targetPapers, targetAuthors);
ACTPhraseSampling targetModel = new ACTPhraseSampling();
targetModel.TrainNewModel(targetCor, opt, "outTarget.txt");
Corpora collaborationCor = new Corpora(vocabSize, 0);
collaborationCor.LoadDataFile(collaborationPapers, collaborationAuthors);
CTLPhraseSampling collaborationModel = new CTLPhraseSampling(sourceModel, targetModel);
collaborationModel.TrainNewModel(collaborationCor, opt, "outColl.txt");
}
catch (Exception ex)
{
Console.WriteLine(ex.StackTrace);
Console.WriteLine(ex.Message);
}
Console.ReadLine();
}
private void InitModel(Corpora cor)
{
this.cor = cor;
M = cor.totalDocuments; //jumlah dokumen
Voc = cor.VocabSize; //jumlah kata di vocab
V = cor.TotalAuthor; //jumlah author
Console.WriteLine(V);
p = new double[K];
zx = new int[Voc][]; //kata-topik
zv = new int[V][]; //author-topik
for (int w = 0; w < Voc; w++)
{
zx[w] = new int[K];
}
for (int v = 0; v < V; v++)
{
zv[v] = new int[K];
}
zxsum = new int[K]; //jumlah kata untuk setiap topik
zvsum = new int[V]; //jumlah topik untuk setiap author
words = new int[cor.totalWords]; //sejumlah token pada korpus, isinya wordID sesuai vocab
docs = new int[cor.totalWords]; //docID untuk setiap kata
authors = new int[cor.totalWords]; //authorID untuk setiap kata
z = new int[cor.totalWords]; //topik untuk setiap kata
wn = 0;
for (int i = 0; i < M; i++) //iterate dokumen
{
int l = cor.Docs[i].Length;
for (int j = 0; j < l; j++) //iterate token pada dokumen
{
words[wn] = cor.Docs[i].Words[j]; //menyimpan wordID dari vocab untuk setiap kata
int author = cor.Docs[i].RandomAuthorID;
authors[wn] = author;
zvsum[author] += 1; //jumlah topik pada author diinisialisasi sama dengan jumlah kata
docs[wn] = i;
wn++;
}
}
for (int i = 0; i < wn; i++)
{
int topic = rnd.Next(K); //select random topik untuk tiap kata lalu update nilai statistik
zx[words[i]][topic] += 1;
zv[authors[i]][topic] += 1;
zxsum[topic] += 1;
z[i] = topic;
}
theta = new double[V][]; //theta untuk author topik --> dimensi nya kebalik sama nd
for (int v = 0; v < V; v++)
{
theta[v] = new double[K];
}
phi = new double[K][]; //phi untuk topik kata
for (int k = 0; k < K; k++)
{
phi[k] = new double[Voc];
}
}
private void InitModel(Corpora cor)
{
this.cor = cor;
M = cor.totalDocuments; //jumlah dokumen
Voc = cor.VocabSize; //jumlah kata di vocab
p = new double[K];
ps = new double[2];
pSource = new double[K];
pTarget = new double[K];
sourceAuthorNum = sourceModel.cor.TotalAuthor;
targetAuthorNum = targetModel.cor.TotalAuthor;
zx = new int[Voc][]; //kata-topik
zxSource = new int[Voc][];
zxTarget = new int[Voc][];
for (int w = 0; w < Voc; w++)
{
zx[w] = new int[K];
zxSource[w] = new int[K];
zxTarget[w] = new int[K];
sourceModel.zx[w].CopyTo(zxSource[w], 0);
targetModel.zx[w].CopyTo(zxTarget[w], sourceModel.K);
}
zxsum = new int[K];
zxsumSource = new int[K];
zxsumTarget = new int[K];
sourceModel.zxsum.CopyTo(zxsumSource, 0);
targetModel.zxsum.CopyTo(zxsumTarget, sourceModel.K);
zvSource = new int[sourceModel.V][];
zvTarget = new int[targetModel.V][];
for (int v = 0; v < sourceModel.V; v++)
{
zvSource[v] = new int[K];
sourceModel.zv[v].CopyTo(zvSource[v], 0);
}
for (int v = 0; v < targetModel.V; v++)
{
zvTarget[v] = new int[K];
targetModel.zv[v].CopyTo(zvTarget[v], sourceModel.K);
}
zvsumSource = sourceModel.zvsum;
zvsumTarget = targetModel.zvsum;
zvv = new int[sourceAuthorNum][][]; //author pair-topik
zvvsum = new int[sourceAuthorNum][]; //jumlah topik untuk setiap author pair
vartheta = new double[sourceAuthorNum][][]; //author pair-topik
for (int a = 0; a < sourceAuthorNum; a++)
{
zvv[a] = new int[targetAuthorNum][];
vartheta[a] = new double[targetAuthorNum][];
zvvsum[a] = new int[targetAuthorNum];
for (int b = 0; b < targetAuthorNum; b++)
{
zvv[a][b] = new int[K];
vartheta[a][b] = new double[K];
}
}
phi = new double[K][]; //phi untuk topik kata
for (int k = 0; k < K; k++)
{
phi[k] = new double[Voc];
}
dc = new int[M][];
cz = new int[2][];
for (int m = 0; m < M; m++)
{
dc[m] = new int[2];
}
for (int i = 0; i < 2; i++)
{
cz[i] = new int[K];
}
words = new int[cor.totalWords]; //sejumlah token pada korpus, isinya wordID sesuai vocab
doc = new int[cor.totalWords]; //docID untuk setiap kata
//authors = new int[cor.totalWords]; //authorID untuk setiap kata
sourceAuthors = new int[cor.totalWords];
targetAuthors = new int[cor.totalWords];
coins = new int[cor.totalWords];
isSourceDomain = new int[cor.totalWords];
z = new int[cor.totalWords]; //topik untuk setiap kata
wn = 0;
for (int i = 0; i < M; i++) //iterate dokumen
{
Document currDoc = cor.Docs[i];
int docLength = currDoc.Length;
for (int j = 0; j < docLength; j++) //iterate token pada dokumen
{
words[wn] = currDoc.Words[j]; //menyimpan wordID dari vocab untuk setiap kata
int coin = (rnd.NextDouble() < 0.5) ? 0 : 1;
coins[wn] = coin;
dc[i][coin] += 1;
int sourceAuthor = currDoc.RandomSourceAuthorID;
int targetAuthor = currDoc.RandomTargetAuthorID;
sourceAuthors[wn] = sourceAuthor;
targetAuthors[wn] = targetAuthor;
doc[wn] = i;
wn++;
}
}
for (int i = 0; i < wn; i++)
{
int topic = rnd.Next(K);
z[i] = topic;
cz[coins[i]][topic] += 1;
if (coins[i] == 0)
{
zx[words[i]][topic] += 1; //coba ini ntar di luar
zxsum[topic] += 1;
zvv[sourceAuthors[i]][targetAuthors[i]][topic] += 1;
zvvsum[sourceAuthors[i]][targetAuthors[i]] += 1;
}
else
{
if (topic < sourceModel.K)
{
isSourceDomain[i] = 1;
zxSource[words[i]][topic] += 1;
zxsumSource[topic] += 1;
zvSource[sourceAuthors[i]][topic] += 1;
zvsumSource[sourceAuthors[i]] += 1;
}
else
{
isSourceDomain[i] = 0;
zxTarget[words[i]][topic] += 1;
zxsumTarget[topic] += 1;
zvTarget[targetAuthors[i]][topic] += 1;
zvsumTarget[targetAuthors[i]] += 1;
}
}
}
}
private void InitModel(Corpora cor)
{
this.cor = cor;
M = cor.totalDocuments; //jumlah dokumen
V = cor.VocabSize; //jumlah kata di vocab
p = new double[K];
Random rnd = new Random();
nw = new int[V][]; //kata-topik
nd = new int[M][]; //dokumen-topik
for (int w = 0; w < V; w++)
{
nw[w] = new int[K];
}
for (int m = 0; m < M; m++)
{
nd[m] = new int[K];
}
nwsum = new int[K]; //jumlah kata untuk setiap topik
ndsum = new int[M]; //jumlah topik untuk setiap dokumen
words = new int[cor.totalWords]; //sejumlah token pada korpus, isinya wordID sesuai vocab
docs = new int[cor.totalWords]; //docID untuk setiap kata
z = new int[cor.totalWords]; //topik untuk setiap kata
wn = 0;
for (int i = 0; i < M; i++) //iterate dokumen
{
int l = cor.Docs[i].Length;
for (int j = 0; j < l; j++) //iterate token pada dokumen
{
words[wn] = cor.Docs[i].Words[j]; //menyimpan wordID dari vocab untuk setiap kata
docs[wn] = i;
wn++;
}
ndsum[i] = l; //jumlah topik pada dokumen diinisialisasi sama dengan jumlah kata
}
for (int i = 0; i < wn; i++)
{
int topic = rnd.Next(K); //select random topik untuk tiap kata lalu update nilai statistik
nw[words[i]][topic] += 1;
nd[docs[i]][topic] += 1;
nwsum[topic] += 1;
z[i] = topic;
}
theta = new double[M][];
for (int m = 0; m < M; m++)
{
theta[m] = new double[K];
}
phi = new double[K][]; //phi untuk topik kata --> dimensi nya kebalik sama nw
for (int k = 0; k < K; k++)
{
phi[k] = new double[V];
}
}