msdy replied on 10-18-2009 6:52 PM
Hi John,
Thanks for reply.
Not sure why my reply doesn't show here. I will reply again.
This model is actually
Blei's correspondence LDA, which annotates images with captions. Here, I just simplifier it to generate two different type of documents.
It is easy to implement under WinBUGS, but it can not handle jagged array, so I'd like to try infer.net.
Here is the code under WinBUGS. Hope it make this extension easier.
model{
for(s in 1:S){
theta[s, 1:K]~ddirch(alpha[])
for(n in 1:L[ s ]){
z[s,n]~dcat(theta[s,1:K])
r[s,n]~dcat(lamda[z[s,n], 1:V1])
}
for(m in 1:T[ s ]){
x[s,m]~dunif(1,N)
g[s,m]~dcat(phi[ z[s,x[s,m]],1:V2])
}
}
for(k in 1:K){
lamda[k,1:V1]~ddirch(beta[])
phi[k,1:V2]~ddirch(gamma[])
}
}
static void Main(string[] args)
{
// Setting
int K = 2; // Number of topics
int V1 = 3; // Number of words in doc type 1
int V2 = 4; // Number of words in doc type 2
// doc type 1
int[][] Doc1 = {
new int[] {0,1,1,1},
new int[] {0,2,2,2},
new int[] {0,1},
new int[] {1,1,2,1},
new int[] {1,1,0},
new int[] {0,1,1,2}
};
// doc type 2
int[][] Doc2 = {
new int[] {2,0,0,3,1},
new int[] {1,3,2,1,2},
new int[] {0,3,2,1},
new int[] {1,1,1,3,2},
new int[] {0,0,3,0},
new int[] {0,0,1,2,3}
};
// Put the sizes into arrayes
int S = Doc1.Length;
int[] word1_size = new int[ S ];
for (int i = 0; i < S; i++)
word1_size[ i ] = Doc1[ i ].Length;
int[] word2_size = new int[ S ];
for (int i = 0; i < S; i++)
word2_size[ i ] = Doc2[ i ].Length;
// Set up the ranges
Range document = new Range(S);
Range TopicsNum = new Range(K);
//Range word1num = new Range(V1);
//Range word2num = new Range(V2);
VariableArray<int> word1Var = Variable.Observed(word1_size, document);
Range word1 = new Range(word1Var[document]);
VariableArray<int> word2Var = Variable.Observed(word2_size, document);
Range word2 = new Range(word2Var[document]);
// Initialization
double[] alpha = { 0.5, 0.5 };
double[] beta = { 0.1, 0.1, 0.1 };
double[] gamma = { 0.2, 0.2, 0.2, 0.2 };
VariableArray<Vector> theta = Variable.Array<Vector>(document);
VariableArray<Vector> lamda = Variable.Array<Vector>(TopicsNum);
VariableArray<Vector> phi = Variable.Array<Vector>(TopicsNum);
theta[document] = Variable.Dirichlet(alpha).ForEach(document);
lamda[TopicsNum] = Variable.Dirichlet(beta).ForEach(TopicsNum);
phi[TopicsNum] = Variable.Dirichlet(gamma).ForEach(TopicsNum);
// Break symmetry by initialising lamda marginals
Vector r_denseBeta = new Vector(V1, 10.0);
Dirichlet[] initLmd = new Dirichlet[K];
Dirichlet r_dirich = new Dirichlet(r_denseBeta);
for (int k = 0; k < K; k++)
initLmd[k] = new Dirichlet(r_dirich.Sample());
lamda.InitialiseTo(Distribution<Vector>.Array(initLmd));
// Break symmetry by initialising phi marginals
Vector g_denseBeta = new Vector(V2, 10.0);
Dirichlet[] initPhi = new Dirichlet[K];
Dirichlet g_dirich = new Dirichlet(g_denseBeta);
for (int k = 0; k < K; k++)
initPhi[k] = new Dirichlet(g_dirich.Sample());
phi.InitialiseTo(Distribution<Vector>.Array(initPhi));
// Latent factors
var Z = Variable.Array(Variable.Array<int>(word1), document);
var X = Variable.Array(Variable.Array<int>(word2), document);
// Observed values
var R = Variable.Array(Variable.Array<int>(word1), document);
R.ObservedValue = Doc1;
var G = Variable.Array(Variable.Array<int>(word2), document);
G.ObservedValue = Doc2;
using (Variable.ForEach(document))
{
using (Variable.ForEach(word1))
{
Z[document][word1] = Variable.Discrete(theta[document]).Attrib(new ValueRange(TopicsNum));
using (Variable.Switch(Z[document][word1]))
{
R[document][word1] = Variable.Discrete(lamda[Z[document][word1]]);
}
}
using (Variable.ForEach(word2))
{
X[document][word2] = Variable.DiscreteUniform(word1Var[document]).Attrib(new ValueRange(word1));
using (Variable.Switch(X[document][word2]))
{
using (Variable.Switch(Z[document][X[document][word2]]))
{
G[document][word2] = Variable.Discrete(phi[Z[document][X[document][word2]]]);
}
}
}
}
InferenceEngine engine = new InferenceEngine();
Console.WriteLine(engine.Infer(Z));
}
A question about LDA extention (Migrated from community.research.microsoft.com)
