void GMM::Init(const char* sampleFileName)
{
const double MIN_VAR = 1E-10;
KMeans* kmeans = new KMeans(m_dimNum, m_mixNum);
kmeans->SetInitMode(KMeans::InitUniform);
kmeans->Cluster(sampleFileName, "gmm_init.tmp");
int* counts = new int[m_mixNum];
double* overMeans = new double[m_dimNum]; // Overall mean of training data
for (int i = 0; i < m_mixNum; i++)
{
counts[i] = 0;
m_priors[i] = 0;
memcpy(m_means[i], kmeans->GetMean(i), sizeof(double) * m_dimNum);
memset(m_vars[i], 0, sizeof(double) * m_dimNum);
}
memset(overMeans, 0, sizeof(double) * m_dimNum);
memset(m_minVars, 0, sizeof(double) * m_dimNum);
// Open the sample and label file to initialize the model
ifstream sampleFile(sampleFileName, ios_base::binary);
//assert(sampleFile);
ifstream labelFile("gmm_init.tmp", ios_base::binary);
//assert(labelFile);
int size = 0;
sampleFile.read((char*)&size, sizeof(int));
sampleFile.seekg(2 * sizeof(int), ios_base::beg);
labelFile.seekg(sizeof(int), ios_base::beg);
double* x = new double[m_dimNum];
int label = -1;
for (int i = 0; i < size; i++)
{
sampleFile.read((char*)x, sizeof(double) * m_dimNum);
labelFile.read((char*)&label, sizeof(int));
// Count each Gaussian
counts[label]++;
double* m = kmeans->GetMean(label);
for (int d = 0; d < m_dimNum; d++)
{
m_vars[label][d] += (x[d] - m[d]) * (x[d] - m[d]);
}
// Count the overall mean and variance.
for (int d = 0; d < m_dimNum; d++)
{
overMeans[d] += x[d];
m_minVars[d] += x[d] * x[d];
}
}
// Compute the overall variance (* 0.01) as the minimum variance.
for (int d = 0; d < m_dimNum; d++)
{
overMeans[d] /= size;
m_minVars[d] = max(MIN_VAR, 0.01 * (m_minVars[d] / size - overMeans[d] * overMeans[d]));
}
// Initialize each Gaussian.
for (int i = 0; i < m_mixNum; i++)
{
m_priors[i] = 1.0 * counts[i] / size;
if (m_priors[i] > 0)
{
for (int d = 0; d < m_dimNum; d++)
{
m_vars[i][d] = m_vars[i][d] / counts[i];
// A minimum variance for each dimension is required.
if (m_vars[i][d] < m_minVars[d])
{
m_vars[i][d] = m_minVars[d];
}
}
}
else
{
memcpy(m_vars[i], m_minVars, sizeof(double) * m_dimNum);
cout << "[WARNING] Gaussian " << i << " of GMM is not used!\n";
}
}
delete kmeans;
delete[] x;
delete[] counts;
delete[] overMeans;
sampleFile.close();
labelFile.close();
}
void GMM::Init(double *data, int N)
{
const double MIN_VAR = 1E-10;
KMeans* kmeans = new KMeans(m_dimNum, m_mixNum);
kmeans->SetInitMode(KMeans::InitUniform);
int *Label;
Label=new int[N];
kmeans->Cluster(data,N,Label);
int* counts = new int[m_mixNum];
double* overMeans = new double[m_dimNum]; // Overall mean of training data
for (int i = 0; i < m_mixNum; i++)
{
counts[i] = 0;
m_priors[i] = 0;
memcpy(m_means[i], kmeans->GetMean(i), sizeof(double) * m_dimNum);
memset(m_vars[i], 0, sizeof(double) * m_dimNum);
}
memset(overMeans, 0, sizeof(double) * m_dimNum);
memset(m_minVars, 0, sizeof(double) * m_dimNum);
int size = 0;
size=N;
double* x = new double[m_dimNum];
int label = -1;
for (int i = 0; i < size; i++)
{
for(int j=0;j<m_dimNum;j++)
x[j]=data[i*m_dimNum+j];
label=Label[i];
// Count each Gaussian
counts[label]++;
double* m = kmeans->GetMean(label);
for (int d = 0; d < m_dimNum; d++)
{
m_vars[label][d] += (x[d] - m[d]) * (x[d] - m[d]);
}
// Count the overall mean and variance.
for (int d = 0; d < m_dimNum; d++)
{
overMeans[d] += x[d];
m_minVars[d] += x[d] * x[d];
}
}
// Compute the overall variance (* 0.01) as the minimum variance.
for (int d = 0; d < m_dimNum; d++)
{
overMeans[d] /= size;
m_minVars[d] = max(MIN_VAR, 0.01 * (m_minVars[d] / size - overMeans[d] * overMeans[d]));
}
// Initialize each Gaussian.
for (int i = 0; i < m_mixNum; i++)
{
m_priors[i] = 1.0 * counts[i] / size;
if (m_priors[i] > 0)
{
for (int d = 0; d < m_dimNum; d++)
{
m_vars[i][d] = m_vars[i][d] / counts[i];
// A minimum variance for each dimension is required.
if (m_vars[i][d] < m_minVars[d])
{
m_vars[i][d] = m_minVars[d];
}
}
}
else
{
memcpy(m_vars[i], m_minVars, sizeof(double) * m_dimNum);
cout << "[WARNING] Gaussian " << i << " of GMM is not used!\n";
}
}
delete kmeans;
delete[] x;
delete[] counts;
delete[] overMeans;
delete[] Label;
}