/**
* init parameter for continue estimating or for later inference
*/
public bool initEstimatedModel(LDACommandLineOptions option)
{
if (!init(option))
{
return(false);
}
int m, n, w;
p = new double[K];
// load model, i.e., read z and trndata
if (!loadModel())
{
Console.WriteLine("Fail to load word-topic assignment file of the model!\n");
return(false);
}
Console.WriteLine("Model loaded:");
Console.WriteLine("\talpha:" + alpha);
Console.WriteLine("\tbeta:" + beta);
Console.WriteLine("\tM:" + M);
Console.WriteLine("\tV:" + V);
nw = ArrayInitializers.ZerosInt(V, K);
nd = ArrayInitializers.ZerosInt(M, K);
nwsum = ArrayInitializers.ZerosInt(K);
ndsum = ArrayInitializers.ZerosInt(M);
for (m = 0; m < data.M; m++)
{
int N = data.Docs[m].Length;
// assign values for nw, nd, nwsum, and ndsum
for (n = 0; n < N; n++)
{
w = data.Docs[m].Words[n];
int topic = z[m][n];
// number of instances of word i assigned to topic j
nw[w][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 = ArrayInitializers.Empty(M, K);
phi = ArrayInitializers.Empty(K, V);
dir = option.dir;
savestep = option.savestep;
return(true);
}
/**
* Init parameters for inference
* @param newData DataSet for which we do inference
*/
public bool initNewModel(LDACommandLineOptions option, LDADataset newData, Model trnModel)
{
if (!init(option))
{
return(false);
}
int m, n;
var rnd = new Random();
K = trnModel.K;
alpha = trnModel.alpha;
beta = trnModel.beta;
p = new double[K];
Console.WriteLine("K:" + K);
data = newData;
//+ allocate memory and assign values for variables
M = data.M;
V = data.V;
dir = option.dir;
savestep = option.savestep;
Console.WriteLine("M:" + M);
Console.WriteLine("V:" + V);
// K: from command line or default value
// alpha, beta: from command line or default values
// niters, savestep: from command line or default values
nw = ArrayInitializers.ZerosInt(V, K);
nd = ArrayInitializers.ZerosInt(M, K);
nwsum = ArrayInitializers.ZerosInt(K);
ndsum = ArrayInitializers.ZerosInt(M);
z = new List <int> [M];
for (m = 0; m < data.M; m++)
{
int N = data.Docs[m].Length;
z[m] = new List <int>();
//initilize for z
for (n = 0; n < N; n++)
{
int topic = (int)Math.Floor(rnd.NextDouble() * 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 = ArrayInitializers.Empty(M, K);
phi = ArrayInitializers.Empty(K, V);
return(true);
}
